Mathieu Hatt

Yannick Morvan

Thanks

Emric, Manon Caroline Thomas

Gulvan

Nolwen

Johana Oliver

Damian Fiona

Julien, Dp, Ajad, Suman, Lija, Maya Alexander

Lambin Philippe

Van Der Heide

Dinh Cuong

Maier-Hein Klaus

Götz Michael

Isensee Fabian

Valentini Vincenzo

Dinapoli Nicola

Boldrini Luca

Lenkowicz Jacopo

Thorwarth Daniela

Socarras Fernandez

Lippert Fiona

Stefan Leger

Löck Steffen

Richter Christian

Troost Esther

Whybra Philip

Spezi Emiliano

Hatt Mathieu

Boellaard Ronald

Beukinga Jorn

Floris H P Van Velden

Guckenberger Matthias

Tanadini-Lang Stephanie

Nesteruk Marta

Gillies Robert

Mahmoud A Abdalah

Lisanne V Van Dijk

Rahmim Arman

Ashrafinia Saeed

Kalpathy-Cramer Jayashree

Arvind U K Rao

Dennis S Mackin

Aerts Hugo

Fedorov Andriy

Griethuysen Van

Joost

I would like to first express my sincere gratitude to my supervisors,

Tumors or neoplasms are an abnormal mass of tissue due to uncontrolled cell proliferation. Brain tumors are "intracranial neoplasms" which are composed of neurons or glial cells or both and possess malignant or benign characteristics. Gliobastomas (GBM) are the highly malignant brain tumors comprise of glial cells and account for 49% of all primary brain tumor [START_REF] Zong | The cellular origin for malignant glioma and prospects for clinical advancements[END_REF]. These tumors are most commonly diagnosed primary brain tumor of adults at a rate of two to three cases per 100,000 per year [2,3]. GBM are generally found in an individual above age 45 [3]. GBM are highly aggressive brain tumor classified as grade IV by WHO [4]. The prognoses for patients with GBM are generally poor with a median survival time of 12-15 months [START_REF] Krex | Long-term survival with glioblastoma multiforme[END_REF]. The diagnosis of tumors are done with the help of biopsy and imaging technique [START_REF] Weller | Novel diagnostic and therapeutic approaches to malignant glioma[END_REF][START_REF] Rs T U P P | FR o i l a ,e ta l . M a l i g n a n tg l i o m a : E s m oc l i n i c a lr e c o m m e n d ations for diagnosis, treatment and follow-up[END_REF][START_REF] Stupp | Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma[END_REF]. The diagnosis is confirmed by biopsy and histology within the suspected lesion shown in MRI scan. MRI scans are the common imaging technique to see any abnormalities in the brain [9]. The scans produces high resolution images of the anatomical structures of the brain with exquisite details. Typically, MRI is also used for the patient examination and therapy monitoring with the set of guideline refereed as Macdonald criteria [10] and RANO criteria [START_REF] Pope | Response assessment in neuro-oncology criteria: implementation challenges in multicenter neuro-oncology trials[END_REF]. Based on the criteria assessment of the tumor response is evaluated in MRI by assessing the change in tumor largest diameter on singleaxial slice or volume measurement and new or increasing enhancement eight to ten weeks post therapy [START_REF] Patrick Y Wen | Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group[END_REF]. The treatment options of GBM include chemotherapy, radiotherapy and surgery, including the combination of all the three [START_REF] Deangelis | Brain tumors[END_REF]. Nearly, after a decade of intensive research in the imaging technology, medical images are no longer simply used for evaluation of structural abnormality and identify tumor related complication but also to incorporate functional, hemodynamic, metabolic, cellular and cytoarchitectural alteration with help of different functional imaging modalities like Diffusion Weighted Imaging (DWI), Perfusion Weighted Imaging (PWI), Magnetic Resonance Spectroscopic (MRS), Positron emission tomography

Structure of Brain

The adult human brain weighs on average about 3 lbs. (1.5 kg) with a volume of around 1130 cubic centimeters (cm 3 )i nw o m e na n d1 2 6 0cm 3 in men; although there is substantial individual variation. The brain is composed of three main parts:

• The cerebellum • The cerebrum

• The brain stem

The cerebrum or cortex is the largest part of the human brain, associated with higher brain function such as thought and action. The cerebral cortex is divided into four sections, called "lobes": the frontal lobe, parietal lobe, occipital lobe, and temporal lobe [fig. 1.1]. • Frontal Lob e asso ciated with reasoning, planning, parts of sp eech, movement, emotions, and problem solving.

• Parietal Lobe associated with movement, orientation, recognition, perception of stimuli.

• Occipital Lobe associated with visual processing

• Temp oral Lob e asso ciated with p erception and recognition of auditory stimuli, memory, and speech

The cerebellum is similar to the cerebrum in that it has two hemispheres and has a highly folded surface or cortex. This structure is associated with regulation and coordination of movement, posture, and balance. Brain Stem is at the base of the brain and links the cerebral cortex white matter and the spinal cord. This structure is responsible for basic vital life functions such as breathing, heartbeat, and blood pressure.

Brain and Gliobastoma (GBM)

Composition of Brain Neuron The neuron is specialized in the transmission and processing of information. Figure 1.2 shows the main components of a neuron, single nerve cell.

The specialized structure consists of a cell (soma), an axon and dendrites. The soma is the central structure of the neuron, where information processing and protein synthesis occur. The dendrites are a treelike extension of the neurons.

It carries incoming signals from other neurons or sensory receptors towards the cell body. The axon is an elongated, tubular extension that carries nerve signals away from the neuron to other neurons, muscle or grand cells in the form of an action potential (electrical impulse). Most Neurons are unable to be repaired, so any loss of neuron leads to irreversible damage to nervous system.

Glial Cells Glial cells constitute about half the volume of the CNS (Central Nervous System). Glial cells are non-neuronal cells that maintain homeostasis from myelin, and provide support and protection for neurons in the brain and peripheral nervous system. They do not participate in synaptic interactions and electrical signaling [16]. There are three types of glial cells in CNS: astrocytes, oligodendrocytes and microglial 1.3. Astrocytes are star shaped glial cells found in the brain's capillaries and form the blood-brain barrier that restricts what substances can enter the brain. Glioblastoma multiforme is malignant astrocytes. Oligodendrocytes are glial cells that support and insulate the axon. Oligodendrogliomas are types of gliomas originate from Oligodendrocytes. Microglial scout in CNS for plaques, damage neurons and infectious agents crossing the blood-brain barrier.

White Matter, Gray Matter, Cerebrospinal Fluid (CSF)

The brain consists of white matter, gray matter and CSF. The white matter is the part of the brain containing myelinated nerve fibers that covers the axon. It connects various gray matter areas and enables the fast conduction of nerve. Gray matter consists predominantly of densely packed neuronal cell bodies, dendrites and glial cells. Cerebrospinal fluid (CSF) surrounds and cushions the brain and

Brain and Gliobastoma (GBM)

the spinal cord. It flows through the sub-arachnoid space and the ventricles (cavities) of the brain. Figure 1.4 levels white matter, gray matter and their micrograph, more details properties are explained in section 1.2 in MRI.

Gliobastoma Multiforme (GBM)

Gliobastoma multiforme are originated from gliomas. Gliomas are tumors found in the brain or spine and arise from gial cells [START_REF] Kleihues | Pathology and genetics of tumours of the nervous system[END_REF]. Glial cells are non neuronal cells that protect and support the neurons. Gliobastoma multiforme are malignant astrocytoma, the cancer of brain (fig. 1.5). They originate from astrocytes, star-shaped glial cells. These are the most invasive type of glial tumors, rapidly growing and commonly spreading to nearby brain tissue. Although, these tumor are highly aggressive, infiltrates and destroys neighboring tissues and forms metastases in subarachnoid space, they do not spread outside the brain and spinal cord and affects other organs. The term "multiforme" is characterized macroscopically where mixture of colors are seen in the brain section: the yellow of fatty infiltration, the grey of necrosis and the red and brown of fresh and old haemorrhage [START_REF] Kj Zülgh | Biology and morphology of glioblastoma multiforme[END_REF]. The edema and necrosis are the characteristics of GBM. In GBM the peritumoral edema is assumed to contain tumor cells that have infltrated into the tissues whereas in metastatic tumors it is assumed to be pure water [START_REF] Kj Zülgh | Biology and morphology of glioblastoma multiforme[END_REF]. The increase in volume due to edema and non-contrast filled pathologic blood ves-CONTENTS 8 sels makes localization by pneumography difficult. Regarding tumor angiogenesis GBM shows prominent microvascular proliferations and area of high vascular density [START_REF] Karl | Angiogenesis in malignant gliomas[END_REF]. Gliobastomas are classified as primary or secondary. Primary glioblastoma multiforme manifest de novo (i.e without clinical or histopathologic evidence of a preexisting) and occurs in adults above 50 years. Secondary gliobastoma multiforme typically develops in younger patients less than 45 years. They develop through malignant progression from a low-grade astrocytoma (WHO grade II) or anaplastic astrocytoma (WHO grade III). GBM has a variety of symptoms including headache, nausea, vomiting, and drowsiness. These symptoms are attributed to an increase in pressure in the brain due to the rapid growth of the tumor. Glioblastomas have the potential of forming in different regions of the brain. Many of these affected regions control motor functions, and patients can develop weakness on one side of the body, visual changes, complications in speech and difficulties with long and short term memory [20].

Census on Incident Rate and Survival

According to the report from the Central Brain Tumor Registry of the United States (CBTRUS) glioblastoma accounts for 16% of all primary brain tumor and

Brain and Gliobastoma (GBM)

53.9% of gliomas based on histology [START_REF] Dolecek | Cbtrus statistical report: primary brain and central nervous system tumors diagnosed in the united states in 2005-2009[END_REF]. CBTRUS also reported glioblastomas as three times higher in white as compared to black and 1.6t i m e sm o r ec o m m o n in male. In a Similar study done in France (rate of 17.6/100 000) the glioblastoma represented 28.2% of all tumor of brain (fig. 1.7)w ithoc c u rre n c eo f3 5 %inma le and 22.7% in female [START_REF] Baldi | Descriptive epidemiology of cns tumors in france: results from the gironde registry for the period 2000-2007[END_REF]. The survival time of the GBM patients without any specific therapy is nine weeks (median 9; range 3-47 weeks (p<0.001)) [START_REF] Fazeny-Dörner | Survival and prognostic factors of patients with unresectable glioblastoma multiforme[END_REF] [ 2]. Whereas, survival of patients treated with radiotherapy (median: 13; range 11-101 weeks) or both radio-chemotherapy (median: 31; range 11-101 weeks) have significant improvement in survival. However, short survival time of almost all patients makes it difficult in identifying prognostic factors of GBM.

Grading of Brain Tumor

The grading of tumor is important aspects for clinicians to explore treatment plans. World Health Organization (WHO) grades the tumor of CNS from I to IV on malignancy scale based on the histological feature of tumors [4](fig. 1.1). The biopsy is done on the malignant region dependent on four main features: nuclear atypia, mitoses, microvascular proliferation and necrosis. In clinical settings, the choice of therapies: chemotherapy or radiotherapy and surgical resection are highly dependent on the grade of tumor [START_REF] Deangelis | Brain tumors[END_REF]. Grade I includes tumor with low proliferation and occurs in children which can be cured using surgical resection. Grade II includes tumor that are infiltrating and low in mitotic activities but recur frequently. Diffuse astrocytoma and oligodendroglioma are grade II gliomas which occurs in all the ages and tends to progress to higher grade of malignancy. Grade III includes tumor with nuclear atypia, increased mitotic activity and infiltration. Anaplastic astrocytoma is grade III gliomas which are usually treated with adjuvant therapy.

Brain and Gliobastoma (GBM)

Grade IV includes tumor that are mitotically active, necrosis-prone, and generally associated with a rapid preoperative and postoperative progression and fatal outcomes. These tumors are usually treated with aggressive adjuvant therapy. Gliobastoma multiforme are grade IV tumors, patients succumb to the disease within a year due to ineffective treatment regimes. Table 1.1 displays WHO Classification of Tumours of the Central Nervous System and lists the tumor types and grades.

Treatment

The efficient treatment plans prevent adverse affect of GBM. The standard treatment options of GBM include chemotherapy, radiotherapy and surgery, including the combination of all the three [START_REF] Rs T U P P | FR o i l a ,e ta l . M a l i g n a n tg l i o m a : E s m oc l i n i c a lr e c o m m e n d ations for diagnosis, treatment and follow-up[END_REF]. The first step in therapy is maximal feasible removal of tumor tissue. The patients with smaller residual of tumor will have ab e t t e rp r o g n o s i sa n da l s o ,r a d i a t i o nt h e r a p yi sm o r ee a s i l yt o l e r a t e dw h e nt h e pressure from the tumor can be reduced. There is great variability in the amount of tumor that can be safely removed from the brain depending on the location of the tumor. For instance, tumors in some brain areas can be removed with very low risk, while in other brain areas surgery is too risky to contemplate.

The tumor cells are very resistant to chemotherapy and other conventional therapies and many drugs cannot cross the blood brain barrier to act on the tumor. Termozolomide, irinotecan, Etoposide, Avastin, Bevacizumab and Carboplatin are some of the most common drugs for chemotherapy. Among those temozolomide became the standard of care for glioblastoma which found to increase the survival to 12 to 14 month [24]. Another study (fig. 1.8)f o u n dG B M patients treated with temozolomide had significantly longer survival than patients previously treated without it; between 1993-1995 and 2005-2007, median survival increased from 13.5 months to 18.5 months in the 20-44 age group, from 9.5 months to 12.5 months in the 45-64 age group, and from 5.5 months to 6.5 months in the 65-79 age group [START_REF] Darefsky | Adult glioblastoma multiforme survival in the temozolomide era: A population-based analysis of surveillance, epidemiology, and end results registries[END_REF]. T r e a t m e n tb yr a d i a t i o nt h e r a p yu s e sh i g henergy x-ray to stop or slow down tumor growth. Patients usually gets radiation therapy following biopsy or the maximal safe surgical resection of tumor [START_REF] Deangelis | Brain tumors[END_REF]. Historically, younger patients benefit more from radiation therapy than older [START_REF] Scott | Long-term glioblastoma multiforme survivors: a population-based study[END_REF].

Tumor Response Evaluation Criteria

In clinical trials the radiologic response evaluation of tumor presents substantial challenges. The response evaluation of gliobastoma is done on postgadolinium contrast-enhanced T1-weighted MRI image. The measurement of contrastenhanced lesion largest diameter on single-axial slice or volumetric analysis based on the morphology [fig. 1.9]i sp e r f o r m e d [START_REF] Jw Henson | Brain tumor imaging in clinical trials[END_REF]. The tumor response is evaluated by these measurements using set of guidelines referred as Macdonal [10]a n d

Brain and Gliobastoma (GBM)

RANO [START_REF] Pope | Response assessment in neuro-oncology criteria: implementation challenges in multicenter neuro-oncology trials[END_REF] criteria between baseline and follow up MRI scans. Some response assessment criteria in the first line treatment of GBM are presented in table [1.2]. These criteria help to categorize the response as Partial Response (PR), Complete Response (CR), Stable disease (SD) and Progressive Disease (PD) [more details in table 1.2]. Figure 1.9: Three enhancing foci in a patient with glioblastoma illustrate issues with lesion measurement during clinical trials. Lesion A is homogeneously enhancing and exceeds 10 mm in diameter and thus is ideal for serial measurement by RECIST or 1D (lower left), Macdonald or 2D (lower right), and volumetric (upper right) approaches. Lesion B is predominantly necrotic and is amenable to volumetric measurement (upper right) because the enhancing and nonenhancing components can be segmented. Lesion C is too small in diameter (8 mm) for accurate serial measurement and should be followed as a nonmeasurable lesion (see text). Images are postgadolinium contrast-enhanced axial T1-weighted. Reprinted from [27, Henson et al., 2008] 1.1 Brain and Gliobastoma (GBM) 1.3) [START_REF] Marc C Mabray | Modern brain tumor imaging[END_REF]. These imaging tools are being applied to diagnose and grade brain tumors preoperatively, to plan and navigate surgery intra-operatively, to monitor and assess treatment response and patient prognosis, and to understand the effects of treatment on the patients brain (Table 1.4). Table 1.4: Role of imaging technique in brain tumors [START_REF] Marc C Mabray | Modern brain tumor imaging[END_REF]. [START_REF] Aaron | The history, development and impact of computed imaging in neurological diagnosis and neurosurgery: Ct, mri, and dti[END_REF]. MRI provides variable pathomorphological manifestations of brain tumors as it is highly sensitive in identifying lesions, mass effect, edema, hemorrhage, necrosis and signs of increased intracranial pressure [START_REF] Marc C Mabray | Modern brain tumor imaging[END_REF]. MRI are acquired with various acquisition protocols. These acquisition protocols help to generate various modalities(sequences) of MR images which capture different properties of the tissue. We briefly describe the physical basis of these MR acquisition protocols routinely used in the clinical imaging in Appendix A and explain here only the clinical aspects, moreover excellent general on physics behind the MRI can be found here [START_REF] Westbrook | MRI in Practice[END_REF][START_REF] Mark | Mr imaging abbreviations, definitions, and descriptions: A review 1[END_REF][START_REF] Westbrook | MRI at a glance[END_REF]. As described in Appendix A,aMRIsequenceisan um ber of radiofrequency pulses and gradients that result in a set of images with particular appearance. MRI sequences can be grouped in a number of ways. Probably most accurately they are grouped according to the type of sequence (e.g. spin echo, or inversion recovery etc..) however, for non radiologists another way of grouping sequences is by general image weighting (e.g. T1 or T2 modality) and additional features (e.g. fat suppressed or gadolinium enhanced). This is a simplified approach to distinguish different MRI sequences hence, the reminder of the description of MRI will be based on second approach. T1 weighted contrast enhanced (T1C) images with administration of a paramagnetic contrast agent "gadolinium" is the most preferred choice for the diagnosis of brain tumor. The contrast agent is injected intravenously (typically 5-15ml) few minutes before scanning the patient and it alters the relaxation times of hydrogen protons. The contrast agent accumulates specifically in pathological tissues (tumors, areas of inflammation or infections) and causes these areas of T1 signal to be increased. Because in the normal brain tissue contrast agents is block from entering the brain region due to blood-brain barrier (BBB) acting as a physical barrier. In T1C modality we can detect active tumor ( Irregular but intense enhancement ) surrounding necrosis (darkish area inside enhanced region) in GBM tumor (Fig. 1.10).

Role of Imaging Techniques

T1-weighted

T2-weighted Imaging

T2 weighted modalities (T2) are part of almost all MRI protocols (SE, fast spin echo [FSE] or TSE,...). T2W MRI refers to spin-spin relaxation time. The tissue contrast in T2 modality is formed on the basis of differences in the T2 relaxation time of tissues. In T2 modality the CSF has high signal intensity and appears white, the grey matter has intermediate signal intensity and appears grey and white matter has hypointense signal intensity compared to grey matter and appears darkish (Fig. 1.10).

T2 Fluid-attenuated Inversion Recovery (FLAIR) Imaging

In many instances, we want to detect parenchymal oedema in soft tissues which often have significant glaring high signal from CSF. To achieve this we suppress high signal from CSF in T2 modality. This modality is called FLAIR, it looks similar to T1 (CSF is dark). But unlike T1W the FLAIR will make white matter appear darker than grey matter (Fig. 1.10).

Susceptibility Weighted Imaging (SWI)

Susceptibility-weighted imaging (SWI) is a fully velocity compensated highresolution 3D gradient-echo sequence that uses magnitude and filtered-phase information, both separately or in combination with each other, to create new sources of contrast. SWI is particularly sensitive to compounds which distort the local magnetic field [START_REF] Mark Haacke | Susceptibility-weighted imaging: technical aspects and clinical applications, part 1[END_REF]. The most common use of SWI is for the identification of small amounts of haemorrhage / blood product or calcium that is often complementary to conventional MRI sequences [START_REF] Sehgal | Clinical applications of neuroimaging with susceptibility-weighted imaging[END_REF][START_REF] Thomas | Clinical applications of susceptibility weighted mr imaging of the brain-a pictorial review[END_REF]. In glioblastoma, the evidence shows that microvascularity and hemorrhagic component can be identified with the help of SWI [START_REF] Gregory A Christoforidis | Susceptibility-based imaging of glioblastoma microvascularity at 8 t: correlation of mr imaging and postmortem pathology[END_REF] (Fig. 1.11). scanners [START_REF] Horská | Imaging of brain tumors: Mr spectroscopy and metabolic imaging[END_REF]. The most recognizable metabolites which are of primary interest in the evaluation of brain tumors, include N-acetylaspartate (NAA-neuronal marker), creatine (Cr-marker for cellular metabolism), and choline (Cho-marker for cell membrane turnover). Absolute heights of these MRS peaks (NAA, Cr and Cho) are generally not used and the metabolic peaks are generally analyzed as ratios including Cho-NAA and Cho-Cr [START_REF] Kostas N Fountas | Noninvasive histologic grading of solid astrocytomas using proton magnetic resonance spectroscopy[END_REF]. High-grade gliomas have been found to have higher Cho-NAA and Cho-Cr ratios than lower-grade gliomas, these characteristic of the tissue aid in diagnosis or grading of tumors [START_REF] Huang | Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection[END_REF]. In DWI the signal observed in a voxel at a millimetric resolution is calculated on statistical basis of overall microscopic displacement of water molecules present in that voxel. In other words, it assess the ease with which water molecules move within a tissue, the mobility classically called Brownian motion [START_REF] James M Provenzale | Peritumoral brain regions in gliomas and meningiomas: Investigation with isotropic diffusion-weighted mr imaging and diffusiontensor mr imaging 1[END_REF]. This give insights into cellularity (e.g. tumours), cell swelling (e.g. ischaemia) and oedema because water diffusion is strongly affected by molecular viscosity and membrane permeability between intra-and extracellular compartments. Furthermore, DWI seems to be useful in providing a greater degree of confidence in detecting areas of tumor infiltration and distinguishing brain abscesses from cystic or necrotic brain tumors than conventional MRI [START_REF] Chang | Diffusion-weighted mri features of brain abscess and cystic or necrotic brain tumors: comparison with conventional mri[END_REF]. The apparent diffusion coefficient (ADC) map are images representing the actual diffusion values of the tissue, derived from diffusion weighted MR images. They represent the physical measurement of the water molecule movement using the following equation: ADC = -ln[S(b) -S(0)]/b,w i t hbb e i n gt h ed i ff u s i o n sensitivity factor ranging between 700 and 1000s/mm 2 ,S ( 0 )a n dS ( b )b e i n gt h e image intensity when b =0andb =700-1000s/mm 2 [START_REF] Le | Looking into the functional architecture of the brain with diffusion mri[END_REF].

State-of-the-Art Multiparametric Imaging of GBM

Perfusion Weighted Imaging (PWI)

Tumour neovascularization and haemo dynamic changes are the basic principles of perfusion MRI. Tumour neovascularization, which leads to a higher volume of blood flow through tumour tissue is detected and quantified, generating values such as cerebral blood volume (CBV is the quantity of blood in a given volume in mL/100mg), cerebral blood flow (CBF is the blood flow in brain tissue in mL/100g/min) and mean transit time (MTT is the average time for arteriovenous passage of blood in a given volume in seconds) [START_REF] Jeffrey | Mr perfusion imaging of the brain: techniques and applications[END_REF][START_REF] Jeffrey M Pollock | Arterial spin-labeled mr perfusion imaging: clinical applications[END_REF]. In PWI the contrast agent bolus leads to a reduction of signal intensity in vascularized parts of the tumour. The high-grade tumors have higher CBV values than low-grade in PWI [START_REF] Siegal | Utility of relative cerebral blood volume mapping derived from perfusion magnetic resonance imaging in the routine follow up of brain tumors[END_REF]. Moreover, it also help to identify the postirradition changes from tumor recurrence as CBV is higher in tumor recurrence area [START_REF] Forsting | Mr perfusion imaging: a tool for more than stroke[END_REF].

Computed Tomography (CT)

A CT (computed tomography) scan, also known as a CAT scan, uses X-rays to form images and is based on the measurement penetration and attenuation of photons as they traverse the head/body as a beam of radiation passes through it from a source to a detector. Then, this raw data are, reconstructed in 2D or 3D images through tomographic reconstruction and displayed. Each pixel/voxel in CT image is assigned a number collectively referred as Hounsfield Unit (HU) named after the inventor of computed tomography. These numbers are relative to the attenuation of water, which is assigned a value of 0 HU. Using water as the reference the maximum brightness of a pixel/voxel is +1000 HU and will appear white whereas, maximum darkness is -1000 HU and will appear black and between these extremes are various shades of gray. CT is superior to MRI in detecting calcification within brain tumors and it is also less expensive and time consuming, but it is inferior to MRI in the classication of other stages of the disease [START_REF] Eskandary | Incidental findings in brain computed tomography scans of 3000 head trauma patients[END_REF]. Figure 1.14 shows contrast enhanced CT where GBM is characterized by necrosis and irregular enhancement due to the disruption of the bloodbrain barrier (BBB) where the contrast material enters into the extracellular spaces of the tumor [START_REF] Drevelegas | Imaging modalities in brain tumors[END_REF].

Positron Emission Tomography (PET)

Positron Emission Tomography (PET) provides unique functional information of tumors that help neuro-oncologists to gain insights into tumor biology and also understand treatment-related phenomena as molecular information like glucose consumption, expression of amino acid transporters, proliferation rate, membrane biosynthesis, and hypoxia are useful to develope therapeutics [START_REF] Dunet | Performance of 18f-fluoro-ethyl-tyrosine (18f-fet) pet for the differential diagnosis of primary brain tumor: a systematic review and metaanalysis[END_REF]. Furthermore, conventional MRI evaluation of contrast enhancing lesion can either under-or overestimate the presence of active tumor, but PET images generally lack anatomic context and are of lower spatial resolution, so only unique strengths of functional activities in tumor is considered. [START_REF] Jacobs | Molecular imaging of gliomas[END_REF]. PET has also been useful in differentiating post-operative residual tumor from therapy induced necrosis and edema. The metabolic or molecular information derived from PET or SPECT studies is being used in some institutions for the exact planning for radio-and gene therapy [START_REF] Grosu | Validation of a method for automatic image fusion (brainlab system) of ct data and 11 c-methionine-pet data for stereotactic radiotherapy using a linac: first clinical experience[END_REF]. But, conventional 18 F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) is of limited usefulness for imaging GBM due to limited differentiation between tumor and normal gray matter (Fig. 1.15). However, more specific radiotracers like the radiolabeled amino acids methyl-

[ 11 C]-L -methionine ([ 11 C]MET), [ 11 C]-tyrosine, [ 18
F]fluoro-tyrosine and O-(2-[ 18 F]-fluoroethyl)-Ltyrosine are effective due to their low uptake in normal brain [START_REF] Kaschten | Preoperative evaluation of 54 gliomas by pet with fluorine-18fluorodeoxyglucose and/or carbon-11-methionine[END_REF][START_REF] Chung | Usefulness of 11c-methionine pet in the evaluation of brain lesions that are hypo-or isometabolic on 18f-fdg pet[END_REF][START_REF] Ogawa | Clinical positron emission tomography for brain tumors: comparison of fludeoxyglucose f 18 and l-methyl-11c-methionine[END_REF][START_REF] Anthony F Shields | Imaging proliferation in vivo with [f-18] flt and positron emission tomography[END_REF]. In conclusion, positron-labeled amino acids are showing highest general utility for staging and therapy management of gliomas/Glioblastoma.

Radiomics: "more than meets the eye"

Radiomics-the high-throughput extraction of large amounts of quantitative image features from radiographic images-the process transfer simple medical images (CT, MR, PET) that has been considered only for visual assessment into a large minable high-dimensional data that may represent "more than meets the eye" [START_REF] Hatt | Radiomics in pet/ct: more than meets the eye?[END_REF][START_REF] Lambin | Radiomics: extracting more information from medical images using advanced feature analysis[END_REF]. Radiomics is designed to develop decision support tools; therefore, it involves combining radiomic data with other patient characteristics, as available, to increase the power of the decision support models [START_REF] Robert | Radiomics: images are more than pictures, they are data[END_REF]. These radiomics are morphological as well as intra-tumoral heterogeneity properties that are extracted using various image processing technique by quantifying shape com-1.3 Radiomics: "more than meets the eye" plexity, first-, second-and higher-order statistics. Shape complexity features are computed based on the surface reconstruction whereas, first-order metrics are computed based on intensity histogram, and second-and higher-order statistics are computed based on texture analysis of the images (Table 1.5). Lambin et el., first introduced the term Radiomics in 2012 to represent these qualitative image features [START_REF] Lambin | Radiomics: extracting more information from medical images using advanced feature analysis[END_REF]. Since then radiomics is exploited in various domain like predicting treatment response and survival, tumor staging and classification, and even correlating radiomics with genomics. For more details use of radomics in these domain the references are provided in the table 1.5. However, in the reminder of this section the use of the radiomics in GBM and various challenges are summarized.

Historically, some clinical features(variables) (preoperative Karnofsky Performance Status (KPS), age, extent of resection after surgery) and imaging features (volume, proportion of enhancing tumor, extent of edema, degree of necrosis, major and minor axis length) have been associated with survival in GBM [START_REF] Cui | Prognostic imaging biomarkers in glioblastoma: Development and independent validation on the basis of multiregion and quantitative analysis of MR images[END_REF][START_REF] Zhang | Af u l l ya u t o m a t i ce x t r a c t i o no fm a g n e t i cr e s o n a n c ei m a g ef e a t u r e si n glioblastoma patients[END_REF]. Later, VASARI (Visually Accessible Rembrandt Images) was introduced in order to standardize the assessment of GBM tumors [START_REF] David A Gutman | Mr imaging predictors of molecular profile and survival: multi-institutional study of the tcga glioblastoma data set[END_REF], more details on section 1.3.1. To our knowledge, the asso ciation of first-, second-and higher-order statistics features with survival of patients in glioblastoma was first evaluated by Upadhaya et al., [START_REF] Upadhaya | Prognostic value of multimodal MRI tumor features in glioblastoma multiforme using textural features analysis[END_REF] although only in small cohort (n=40). Later, many studies has been done with slightly bigger cohort; Prasanna et al. [START_REF] Prasanna | Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric mr imaging predict long versus shortterm survival in glioblastoma multiforme: Preliminary findings[END_REF]u s e dr a d i o m i c sw i t h3 -f o l dc r o s svalidation on [START_REF] Lambin | Radiomics: extracting more information from medical images using advanced feature analysis[END_REF] [START_REF] Cui | Prognostic imaging biomarkers in glioblastoma: Development and independent validation on the basis of multiregion and quantitative analysis of MR images[END_REF]u s e dp r o p o r t i o no fs u b -t u m o rr e g i o nf o r survival prediction. So far only one satisfactory study using radiomics and machine learning (RF) for overall and progression free survival with proper testing and validation using various modalities has been conduct [START_REF] Kickingereder | Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models[END_REF]. Same group also conducted study associating radiogenomics of Glioblastoma [START_REF] Kickingereder | Radiogenomics of glioblastoma: Machine learning-based classification of molecular characteristics by using multiparametric and multiregional mr imaging features[END_REF].

Among all the study, only one study has used the machine learning for survival, most of them have used the univariate analysis (table 1.5). None of them have investigated the potential benefit and respective impact of the addition of several MRI pre-processing steps (spatial resampling for isotropic voxels, intensities quantization and normalization) before radiomics features computation and reproducibility of these radiomics features. None of them have compared the various machine learning algorithm. And moreover, have not used the feature selection technique within the classification algorithm framework that itself presents features that make it well suited for the types of problems frequently faced with radiomics. In this thesis, we have explored respective impact and potential benefit of these steps in resulting accuracy of classifier and various other challenges in chapter 2, 3 and 4.

VASARI

In order to standardize the assessment of GMB tumors from a qualitative and quantitative standpoint, 30 morphological visual observations from MR sequences (including DWI) by neuroradiologists were derived from a multi-institutional effort and called VASARI (Visually Accessible Rembrandt Images) [START_REF] David A Gutman | Mr imaging predictors of molecular profile and survival: multi-institutional study of the tcga glioblastoma data set[END_REF]. They are based on four cardinal imaging features of non-enhanced tumor, contrastenhanced tumor, necrosis, and edema. Terms were grouped into general categories such as lesion location, morphology, margin, vicinity of lesion, and remote alterations.

Radiomics: "more than meets the eye"

Table 1.5: List of the main publications based on Radiomics according to their application and the imaging modality studied. Reprinted Stephen et al., [START_REF] Stephen | Applications and limitations of radiomics[END_REF]and Desseroit et al., [START_REF] Desseroit | Caractrisation et exploitation de l'htrognit intra-tumorale des images multi-modales tdm et tep[END_REF].

Application Study Modality Haralick et el., [START_REF] Robert M Haralick | Textural features for image classification[END_REF], Galloway et el., [START_REF] Galloway | Texture analysis using gray level run lengths[END_REF],

Radiomics definition

Pentland et el., [START_REF] Pentland | Fractal-based description of natural scenes[END_REF], Rahmim et el., [START_REF] Rahmim | A novel metric for quantification of homogeneous and heterogeneous tumors in pet for enhanced clinical outcome prediction[END_REF], Amadasun et el., [START_REF] Amadasun | Textural features corresponding to textural properties[END_REF], Davnall et el., [START_REF] Davnall | Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?[END_REF], Thibault et el., [START_REF] Thibault | Texture indexes and gray level size zone matrix application to cell nuclei classification[END_REF] Johansen et el., [START_REF] Johansen | Predicting survival and early clinical response to primary chemotherapy for patients with locally advanced breast cancer using dcemri[END_REF], Baek et el., [START_REF] Hye | Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas[END_REF], Shukla-Dave et el., [START_REF] Shukla-Dave | Dynamic contrast-enhanced magnetic resonance imaging as a predictor of outcome in head-and-neck squamous cell carcinoma patients with nodal metastases[END_REF], Foroutan et el., [START_REF] Parastou Foroutan | Diffusion mri and novel texture analysis in osteosarcoma xenotransplants predicts response to anti-checkpoint therapy[END_REF], MRI King et el., [START_REF] King | Head and neck squamous cell carcinoma: diagnostic performance of diffusion-weighted mr imaging for the prediction of treatment response[END_REF], Peng et el., [START_REF] Peng | Analysis of parametric histogram from dynamic contrast-enhanced mri: application in evaluating brain tumor response to radiotherapy[END_REF], Eary et el., [START_REF] Eary | Spatial heterogeneity in sarcoma 18f-fdg uptake as a predictor of patient outcome[END_REF], El Naqa et el., [START_REF] Naqa | D e s h a nY a n g ,MS c h m i t t ,R i c h a r dL a f o r e s t ,e ta l . E x p l o ring feature-based approaches in pet images for predicting cancer treatment outcomes[END_REF], Predicting treatment

Yang et el., [START_REF] Yang | Temp oral analysis of intratumoral metab olic heterogeneity characterized by textural features in cervical cancer[END_REF], Cooket el., [START_REF] Gary Jr Cook | Are pretreatment 18f-fdg pet tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy[END_REF], PET response & survival Tixier et el., [START_REF] Tixier | Intratumor heterogeneity characterized by textural features on baseline 18f-fdg pet images predicts response to concomitant radiochemotherapy in esophageal cancer[END_REF], Zhang et el., [START_REF] Zhang | Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatialtemporal 18 f-fdg pet features, clinical parameters, and demographics[END_REF] Aerts et el., [START_REF] Hugo Jwl Aerts | Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[END_REF], Parmar et el., [START_REF] Parmar | Machine learning methods for quantitative radiomic biomarkers[END_REF], Tateishi et el., [START_REF] Tateishi | Contrast-enhanced dynamic computed tomography for the evaluation of tumor angiogenesis in patients with lung carcinoma[END_REF], Kim et el., [START_REF] Kyo | Neoangiogenesis and sinusoidal capillarization in hepatocellular carcinoma: Correlation between dynamic ct and density of tumor microvessels 1[END_REF], CT Tixier et el., [START_REF] Tixier | Correlation of intra-tumor 18 f-fdg uptake heterogeneity indices with perfusion ct derived parameters in colorectal cancer[END_REF] Vallires et el., [START_REF] Vallières | A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities[END_REF] MRI + PET Donget el., [START_REF] Lin | State-of-the-art molecular imaging in esophageal cancer management: implications for diagnosis, prognosis, and treatment[END_REF], Muet el., [START_REF] Mu | Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage[END_REF] PET Tumor Staging Ganeshanet el., [100] CT Zacharaki et el., [START_REF] Evangelia I Zacharaki | Classification of brain tumor type and grade using mri texture and shape in a machine learning scheme[END_REF] MRI Lerski et el., [START_REF] Ra Lerski | Viii. mr image texture analysisan approach to tissue characterization[END_REF], Kjaer et el., [START_REF] Kjaer | Texture analysis in quantitative mr imaging: tissue characterisation of normal brain and intracranial tumours at 1.5 t[END_REF], MRI Mahmoud-Ghoneimet el., [START_REF] Mahmoud-Ghoneim | Three dimensional texture analysis in mri: a preliminary evaluation in gliomas[END_REF], Nie et el., [START_REF] Nie | Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast mri[END_REF],

Tumor classification

McNitt-Gray et el., [START_REF] Michael F Mcnitt-Gray | A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution ct: preliminary results[END_REF], Kidoet el., [START_REF] Kido | Fractal analysis of small peripheral pulmonary nodules in thin-section ct: evaluation of the lung-nodule interfaces[END_REF], CT Petkovska et el., [START_REF] Petkovska | Pulmonary nodule characterization: a comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps[END_REF], Wayet el., [START_REF] Way | Computer-aided diagnosis of pulmonary nodules on ct scans: Segmentation and classification using 3d active contours[END_REF], Xu et el., [START_REF] Xu | Texture analysis on 18f-fdg pet/ct images to differentiate malignant and benign bone and soft-tissue lesions[END_REF], Yuet el., [START_REF] Yu | Automated radiation targeting in head-and-neck cancer using region-based texture analysis of pet and ct images[END_REF],

PET + MRI

Radiogenomics

Diehn et el., [START_REF] Diehn | Identification of noninvasive imaging surrogates for brain tumor geneexpression modules[END_REF], Ellingson et el., [START_REF] Bm Ellingson | Probabilistic radiographic atlas of glioblastoma phenotypes[END_REF], MRI Naeini et el., [START_REF] Kourosh | Identifying the mesenchymal molecular subtype of glioblastoma using quantitative volumetric analysis of anatomic magnetic resonance images[END_REF], Gutman et el., [START_REF] David A Gutman | Somatic mutations associated with mri-derived volumetric features in glioblastoma[END_REF], Nair et el., [START_REF] Viswam S Nair | Prognostic pet 18f-fdg uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer[END_REF], Nair et el., [START_REF] Viswam S Nair | Nf-κb protein expression associates with 18 f-fdg pet tumor uptake in non-small cell lung cancer: a radiogenomics validation study to understand tumor metabolism[END_REF] PET Cui et el., [START_REF] Cui | Volume of high-risk intratumoral subregions at multi-parametric mr imaging predicts overall survival and complements molecular analysis of glioblastoma[END_REF], Kickingereder et el., [START_REF] Kickingereder | Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models[END_REF][START_REF] Kickingereder | Radiogenomics of glioblastoma: Machine learning-based classification of molecular characteristics by using multiparametric and multiregional mr imaging features[END_REF], Prasanna et el., [START_REF] Prasanna | Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric mr imaging predict long versus shortterm survival in glioblastoma multiforme: Preliminary findings[END_REF]M c g a r r yet el., [START_REF] Mcgarry | Magnetic resonance imaging-based radiomic profiles predict patient prognosis in newly diagnosed glioblastoma before therapy[END_REF], GBM radio(geno)mics Molina et el., [START_REF] Molina | Tumour heterogeneity in glioblastoma assessed by mri texture analysis: a potential marker of survival[END_REF][START_REF] Molina | Geometrical measures obtained from pretreatment postcontrast t1 weighted mris predict survival benefits from bevacizumab in glioblastoma patients[END_REF], Martinez et el., [START_REF] Martínez-González | Glioblastoma: Pre-treatment geometry and texture of postcontrast t1 mri matter[END_REF], MRI Mazurowski et el., [START_REF] Maciej A Mazurowski | Computer-extracted mr imaging features are associated with survival in glioblastoma patients[END_REF], Chaddad et el., [START_REF] Chaddad | Radiomic analysis of multi-contrast brain mri for the prediction of survival in patients with glioblastoma multiforme[END_REF], Korfiatis et el., [START_REF] Korfiatis | Mri texture features as biomarkers to predict mgmt methylation status in glioblastomas[END_REF], Nicolasjilwan et el., [START_REF] Nicolasjilwan | Addition of mr imaging features and genetic biomarkers strengthens glioblastoma survival prediction in tcga patients[END_REF], Upadhaya et el., [START_REF] Upadhaya | Prognostic value of multimodal MRI tumor features in glioblastoma multiforme using textural features analysis[END_REF][START_REF] Upadhaya | Prognosis classification in glioblastoma multiforme using multimodal mri derived heterogeneity textural features: impact of pre-processing choices[END_REF][START_REF] Upadhaya | A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in glioblastoma multiforme[END_REF], Levner et el., [START_REF] Levner | Predicting mgmt methylation status of glioblastomas from mri texture[END_REF] 1.4 Structure of the Manuscript and Objective/ Contribution Prognosis of glioblastomas is generally dismal and historically, the most common methods used for predicting prognosis are based on analysis of clinical/ pathological features. Despite the fact that some prognostic factors have been well identified amongst clinical features (VASARI [START_REF] David A Gutman | Mr imaging predictors of molecular profile and survival: multi-institutional study of the tcga glioblastoma data set[END_REF], volume, proportion of enhancing tumor, extent of edema, degree of necrosis, major and minor axis length [START_REF] Cui | Prognostic imaging biomarkers in glioblastoma: Development and independent validation on the basis of multiregion and quantitative analysis of MR images[END_REF][START_REF] Zhang | Af u l l ya u t o m a t i ce x t r a c t i o no fm a g n e t i cr e s o n a n c ei m a g ef e a t u r e si n glioblastoma patients[END_REF][START_REF] David A Gutman | Mr imaging predictors of molecular profile and survival: multi-institutional study of the tcga glioblastoma data set[END_REF]) they have limited predictive ability. For example, it is well known fact that younger patients treated by multimodal treatment (radiotherapy, chemotherapy and resection) usually have longer survival than the older patients. Thus, how much longer/shorter a older patients live compare to another and why do some younger patients defy the trend and have short survival time ? There could be additional underlying factors influencing survival. Hence, it is worth exploring other options and why not start by systematically analyzing the very appearances of the glioblastoma tumors on medical images? Therefore in this thesis, we explored the development of a prognostic model of GBM tumor, which is based on plausible assumptions of intratumoral heterogeneity reflected through multimodal MRI. Using the model of radiomics features (including intensity, shape and textural metrics) from multimodal MRI sequences and two machine learning algorithm (Support Vector Machine and Random Forest) as a core component, we have developed and validated a framework for prognostic model.

The manuscript is organised as follows:

• Chapter 2 presents the first contribution of the thesis: development of carefully designed framework for prognostic of GBM patients. The intrinsic appearance of a glioblastoma on a magnetic resonance image could say a lot about the tumor. Visually, an oncologist might describe the tumor having irregular borders or being more or less heterogeneous. It is possible to measure these intuitive properties using radiomics (shape descriptors and 1.4 Structure of the Manuscript and Objective/ Contribution texture) and investigate how these image properties are correlated with survival. Radiomics features may also reveal tumor properties that cannot be assessed visually (radiomics, more than meets the eye). Therefore, we have developed a model using shape features and texture analysis in 3D, extracted based on first-(intensity histogram), textural second-(cooccurence matrix) and higher-order (grey-level run length and grey-level size zone matrices) statistics from each delineated tumor volume and in each of the four MRI sequences. Moreover, using modern machine learning approach with Support Vector Machine prior to classification of patients into long term or short term overall survival. Embedded method of feature selection techniques was applied to select, rank and combine optimal number of features to build the prognostic model. Also at the time of the beginning of our work, there had been no systematic attempts at using radiomics and machine learning on multimodal MRI for GBM. Most texture analysis frameworks developed regarding brain tumors deal with tumor segmentation or tissue characterization. This chapter was published as a journal article in IRBM [START_REF] Upadhaya | A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in glioblastoma multiforme[END_REF], which builds upon a conference paper published in IEEE ISBI [START_REF] Upadhaya | Prognostic value of multimodal MRI tumor features in glioblastoma multiforme using textural features analysis[END_REF].

• Chapter 3 presents the second contribution of the thesis: the crucial importance of investigating appropriate image pre-processing steps to be used for methodologies based on textural features extraction in medical imaging.

In our previous study, we developed an operational workflow for multimodal GBM MR images pre-processing, registration, segmentation, characterization of heterogeneity, and prognostic model training and validation using Support Vector Machine (SVM). Our preliminary results (Chapter 2)s u ggested that textural features extracted from multimodal MRI could provide higher prognostic value than standard clinical variables and standard image features. However, in this chapter we show due to the multi centric nature of the cohort, the variability in acquisition protocols and scanner models involved could lead to undesirable variability in the textural features, and a resulting bias in the classification performance. Therefore, in this chapter we have investigated and highlighted the potential benefits and respective impact of several MRI pre-processing steps (spatial resampling of voxels, intensities quantization and normalization) to be performed before textural features computation, on the resulting accuracy of the classifier. This chapter was published as a conference paper in SPIE Medical Imaging [START_REF] Upadhaya | Prognosis classification in glioblastoma multiforme using multimodal mri derived heterogeneity textural features: impact of pre-processing choices[END_REF].

• Chapter 4 presents the third contribution of the thesis: the importance of the features selection and classifier techniques choices, as well as the impact of exploiting only robust features to build the model. As a result, machine learning that allows computers to learn from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets were compared. This capability is particularly well-suited to medical applications, especially those that depend on complex measurements of image features for cancer prognosis and prediction, and it is also part of a growing trend towards personalized, predictive medicine/ precision medicine. Therefore, purpose of this part was to determine whether feature selection and classification methods for building a prognostic model in GBM effect the resulting accuracy of the model. Two benchmark machine learning techniques exploiting different paradigms to perform features selection and classification were evaluated: Support Vector Machines (SVM) and Random Forest. Various models were built using radiomics features (including intensity, shape and textural metrics) from multimodal MRI sequences. This chapter was published as a conference paper in IEEE NSS/MIC [START_REF] Upadhaya | Multimodal mri radiomics in gbm: a comparative investigation of feature selection and classification techniques for prognostic models including robustness assessment[END_REF] • Chapter 5 uses the previously developed framework in a larger cohort of GBM patients to explore in more details the training/validation issue. The study is conducted on large cohort of 142 patients for the prognostic model building based on MRI-derived radiomics and machine learning Finally, chapter 6 concludes the thesis on GBM by summarizing the contributions and providing some perspectives. In the previous chapter, the Glioblastoma multiforme (GBM) and it's Census on incident rate and survival, grading of tumor, treatment options and tumor response evaluation criteria are discussed. Furthermore, all the state-of-the-art multiparametric imaging technique (PET, CT, MRI) for clinical management of GBM tumor are also mentioned. In this chapter, we utilize one of the standard imaging technique specially, multimodal or multisequence MRI (T1-weighted pre and post-contrast sequence) to solve the problem statement. This chapter deals with the methodological framework development and validation

In Glioblastoma Multiforme (GBM) image-derived features ("radiomics") could help in individualizing patient management. Simple geometric features of tumors (necrosis, edema, active tumor) and first-order statistics in Magnetic Resonance Imaging (MRI) are often used to characterize tumors in clinical practice. However, these features provide limited characterization power because they do not incorporate spatial information and thus cannot differentiate patterns. The aim of this work was to develop and evaluate a methodological framework dedicated to building a prognostic model based on heterogeneity textural features of multi-modal MRI sequences (T1, T1-contrast, T2 and FLAIR) in GBM. The proposed workflow consists in i) registering the available 3D multimodal MR images and segmenting the tumor volume, ii) extracting image features such as heterogeneity metrics and iii) building a prognostic model by selecting, ranking and combining optimal features through machine learning (Support Vector Machine). This framework was initially developed by exploiting a small database of 40 histologically proven GBM patients with the endpoint being overall survival (OS) classified as above or below the median survival (15 months). The models combining features from a maximum of two modalities were evaluated using leaveone-out cross-validation (LOOCV). A classification accuracy of 90% (sensitivity 85%, specificity 95%) was obtained by combining features from T1 pre-contrast and T1 post-contrast sequences. Our results suggest that radiomics features from several MRI sequences combined through machine learning could form the basis of a framework to improve prognosis of GBM patients.

Introduction

Glioblastoma multiforme (GBM) is the most malignant grade IV primary intracranial tumor of adults according to the World Health Organization' histological grading system [START_REF] Dolecek | Cbtrus statistical report: primary brain and central nervous system tumors diagnosed in the united states in 2005-2009[END_REF]. The prognosis is poor with a median survival of 15 months and occurrence rate is two or three cases per 100,000 per year [START_REF] Krex | Long-term survival with glioblastoma multiforme[END_REF][START_REF] David N Louis | The 2007 who classification of tumours of the central nervous system[END_REF]. The current standard treatment of GBM is a surgical resection followed by radiotherapy and chemotherapy [START_REF] Preusser | Current con-cepts and management of glioblastoma[END_REF]. Within this context, multimodal Magnetic Resonance Imaging (MRI) sequences (T1, T1-contrast, T2, FLAIR...) play a major role for diagnosis, treatment planning, as well as prognosis, on which depend an um berofclinicaldecisions. Th us,image-deriv edfeaturesextractedfromstandard MRI sequences could potentially be combined into a powerful prognostic tool with impact on patient management through higher stratification.

Although novel contrast agents, tracers and imaging sequences are being developed to investigate various aspects of tumor underlying pathophysiological

Introduction

processes, it is recognized that standard medical images may contain useful yet still unexploited information that could be useful in a clinical setting. Imagederived features (also called "radiomics") extracted from medical images may not only allow to be associated with prognosis but also non-invasively probe molecular and histological profiles of tumors, as a further step towards personalized medicine. Hence, considerable efforts have been carried out in identifying radiomics as a prognostic factor in GBM. Gutman et al. conducted an assessment of GBM tumor size and composition by using 24 visual observations familiar to neuroradiologists from MR sequences dubbed as VASARI, and found that contrast-enhanced tumor volume and major axis length of tumor were strongly associated with poor survival [START_REF] David A Gutman | Mr imaging predictors of molecular profile and survival: multi-institutional study of the tcga glioblastoma data set[END_REF]. Recently, Gevaert et al. evaluated 153 2D MR image-derived features for the development of a radiogenomic map, establishing a link between image features and underlying molecular data. Three features were found to be correlated with survival, 77 with VASARI, and 7 with molecular subgroups [START_REF] Gevaert | Glioblastoma multiforme: Exploratory radiogenomic analysis by using quantitative image features[END_REF]. Mazurowski et al. investigated 8 MRI derived features and concluded that proportion enhancing tumor and major axis length were prognostic factors [START_REF] Maciej A Mazurowski | Computer-extracted mr imaging features are associated with survival in glioblastoma patients[END_REF]. Clinical features such as Karnofsky Performance Status (KPS), age, extent of resection and degree of necrosis were found to be significant prognostic factors [START_REF] Lacroix | A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival[END_REF]. However, most studies were focused on the geometry of tumors, which do not incorporate the within-tumor heterogeneity characteristics imaged through diagnostic imaging techniques such multimodal MR sequences. In tumors regional variations due to metabolic activity, proliferation, cell death and micro vascular structure makes it inhomogeneous. Various non-invasive diagnostic imaging techniques are available to visualize and detected the heterogeneity characteristics in whole tumor (necrosis, metabolic activity, cell density and vascularity) [START_REF] Peter A Fasching | Ki67, chemotherapy response, and prognosis in breast cancer patients receiving neoadjuvant treatment[END_REF][START_REF] Nicholas | Intratumoral heterogeneity of receptor tyrosine kinases egfr and pdgfra amplification in glioblastoma defines subpopulations with distinct growth factor response[END_REF]. These image heterogeneity can be quantified using variety of texture analysis methods which then can be used as a potential biomaker for tumor characterization, for response predection, prognostic prediction and monitoring. During the last decades, several methods are available to quantify tumor heterogeneity from imaging data. Some of them are derived from simple image histogram quantifying heterogeneity in global scale, is also called "first order statics" (section 2.4). However, these features do not take into account the spatial distribution of the intensity values. In contrast, textural analysis methods take spatial information into account by quantifying the spatial variations in the images. These textural analysis methods are based on grey-level co-occurance matrix quantifying heterogeneity in local scale, is also called "second order statics" (section 2.4.1)a n d ,g r e y -l e v e lr u nl e n g t hm a t r i xa n dg r e y -l e v e ls i z ez o n e matrix quantifying heterogeneity in regional scale, is also called "higher order statics" (section 2.4.1). These methods result in features which can be considered to be imaging biomarkers providing information on the underlying tumor heterogeneity. Some of these features are related to image properties that are visually perceived by the radiologist, whereas others are more abstract. Therefore, quantifying heterogeneity could be of interest for prognosis in GBM and it can be achieved through textural features analysis, which has been found to be of interest in various pattern recognition applications in medical imaging [START_REF] Alic | Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review[END_REF].

For example, textural features have been used to quantify FDG tracer uptake heterogeneity in PET images of tumors [START_REF] Tixier | Intratumor heterogeneity characterized by textural features on baseline 18f-fdg pet images predicts response to concomitant radiochemotherapy in esophageal cancer[END_REF][START_REF] Tixier | Visual versus quantitative assessment of BIBLIOGRAPHY intratumor 18f-fdg pet uptake heterogeneity: Prognostic value in non-small cell lung cancer[END_REF]w h i c ho u t p e r f o r m ss i mple SUV measures in treatment outcome predictions. Likewise, texture features from NGTDM could better differentiate between responders and nonresponders to chemoradiotherapy and also predict overall survival of patient than the SUV measures in non-small cell lung cancer [START_REF] Cook | Are pretreatment 18f-fdg pet tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? Journal of nuclear medicine: official publication[END_REF]. Textural feature also have strong prognostic value in CT imaging that can predict patients survival in head and neck cancer [START_REF] Hugo Jwl Aerts | Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[END_REF][START_REF] Parmar | Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer[END_REF]. Furthermore, it is also found to be significant predictors of distant metastasis in the patients with lung cancer [START_REF] Coroller | Ct-based radiomic signature predicts distant metastasis in lung adenocarcinoma[END_REF]. Texture analysis has been used on computed tomography (CT) images to detect microcalcification in breast cancer and to analyze breast tumors on contrast-enhanced MRI [START_REF] Da Kulkarni | Texture analysis of mammographic images[END_REF][START_REF] Sinha | Multifeature analysis of gd-enhanced mr images of breast lesions[END_REF]. Recently, it has been shown that the combination of MR and [ 18 F]FDG-PET textural features can also predict the risk of lung metastases in soft-tissue sarcomas [START_REF] Cr Freeman | A radiomics model from joint fdg-pet and mri texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities[END_REF]. It has also been used in classification of brain tumor type (benign, malignant, and normal tissue) and grade in MR images [START_REF] Evangelia I Zacharaki | Classification of brain tumor type and grade using mri texture and shape in a machine learning scheme[END_REF][START_REF] Herlidou-Meme | Mri texture analysis on texture test objects, normal brain and intracranial tumors[END_REF]. Most recently,

Patients Population and Imaging Data

many studies have shown the strong relationship between texture features and the underlying tumor genetics [START_REF] Viswam S Nair | Prognostic pet 18f-fdg uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer[END_REF][START_REF] Gevaert | Nonsmall cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray datamethods and preliminary results[END_REF]. In case of GBM, it has been shown that texture features extracted from multimodal MRI can predict patients survival and also improve model performence over the established clinical and radiologic risk model [START_REF] Cui | Prognostic imaging biomarkers in glioblastoma: Development and independent validation on the basis of multiregion and quantitative analysis of MR images[END_REF][START_REF] Kickingereder | Imaging predictor of patient survival with improved performance over established clinical and radiologic risk models[END_REF].

The aim of this work is to develop a framework for multimodal imaging based prognostic model and evaluate the prognostic value of textural features quantifying the heterogeneity of GBM tumors in baseline multimodal MRI sequences. We developed a fully operational workflow for multimodal MR images pre-processing, registration, segmentation of tumors, characterization of tumors heterogeneity, and prognostic model training and validation using support vector machine (SVM) based on recursive feature elimination algorithm for feature selection and classification [START_REF] Guyon | Gene selection for cancer classification using support vector machines[END_REF] (Figure 4.1). We present preliminary results obtained on 40 patients with leave-one-out cross-validation (LOOCV).

This chapter is organized as follows. Patients population and imaging data are described in section 5.2.1.P r e -p r o c e s s i n g a n d F e a t u r e e x t r a c t i o n o f M R I Sequances are introduced in Section 2.3 and 2.4 respectively. We formulate the prognosis classification problem using machine learning approach in Section 2.5. In Section 2.6 and 2.7,e x p e r i m e n t sw i t hd a t a s e t sa r ed i s c u s s e da n dc o n c l u d e d . Section 2.8 concludes this part of our work with brief perspective of the next chapter.

Patients Population and Imaging Data

In order to simplify the analysis, patients were divided into two groups for the classification task: short and long overall survival (SOS and LOS) with a threshold of 14.8m o n t h sc o r r e s p o n d i n gt ot h em e d i a ns u r v i v a l( n = 2 0i ne a c hg r o u p )w i t h the standard treatment [START_REF] Preusser | Current con-cepts and management of glioblastoma[END_REF]( t a b l e2.1). The MR images of GBM for this study

Pre-processing of MRI Sequences

Pre-processing of MRI Sequences

MRI sequences were acquired using various acquisition parameters and pulse sequences thereby with potential impact on the resulting quantification within the images. Standardization for comparison was performed using several sequentially implemented pre-processing steps.

Inhomogeneity Correction

The undesirable signal that corrupts the MR images due to magnetic settings, patients' position and other factors is known as inhomogeneity or field bias. These inhomogeneities in a same tissue type degrade the performance of segmentation and classification, based on the assumption of spatial invariance. Therefore all the images included in this study were corrected for inhomogeneity using the N3ITK filter [START_REF] Ibanez | The ITK Software Guide[END_REF].

Multimodal Co-registration

All the MRI sequences (T1 pre-contrast, T2 and FLAIR) were registered to the T1 post-contrast image using mutual information similarity metric [START_REF] Josien Pw Pluim | Mutualinformation-based registration of medical images: a survey[END_REF].

Tumor Delineation

Prior to segmentation, a fully-automatic and customized skull-stripping algorithm was used to isolate the brain region from the MRI sequences [START_REF] Porz | Multi-modal glioblastoma segmentation: Man versus machine[END_REF]. Segmentation of the tumor regions was carried-out using all four MRI sequences and a single mask with labels representing edema, necrosis and active tumor was automatically produced. The segmentation algorithm is based on Support Vector Machine exploiting multimodal voxels' intensities with subsequent hierarchical regularization by Conditional Random Fields [START_REF] Bauer | Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization[END_REF]. An expert clinician in neuro-oncology (P-J Le Reste) validated and manually edited the resulting masks when needed.

Intensity Standardization within Delineated Lesions

As a consequence of using different MRI sequences with the varying intensity ranges, the quantization step which yields finite range of intensities among modalities and across patients was employed using equation 3.1.

V (x)=B I (x) -min i i2Ω max i i2Ω -min i i2Ω +1 (2.1)
where B is the quantization bins value, usually considered within the range 4-256. We considered 4 different values in the present work for B: 16, 32, 64, and 128. I is the intensity of a voxel in the original image and Ω is the set of voxels in the delineated volume. This quantization step reduces the spatial variability of intensities (noise) and is also necessary to compute relevant values for textural features and facilitate their comparison. All the above pre-processing steps except the quantization were carried out using Brain Tumor Image Analysis software (BraTumIA)2 ,afullydedicatedopensourcesoftwareforGBMsegmentationusing MRI sequences [START_REF] Ibanez | The ITK Software Guide[END_REF][START_REF] Josien Pw Pluim | Mutualinformation-based registration of medical images: a survey[END_REF][START_REF] Porz | Multi-modal glioblastoma segmentation: Man versus machine[END_REF][START_REF] Bauer | Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization[END_REF].

Feature Extraction

Thirty-four textural features (Table 3.1)w e r ee x t r a c t e db a s e do nt h efi r s t -o r d e r , second-order and higher-order statistics accounting for intensity distribution in global, local and regional scale respectively, of each delineated tumor volume in each of the four MRI sequences. Global scale textural features were computed from intensity histogram, local scale textural features were computed from Grey-level co-occurrence matrix (GLCM), whereas regional scale textural features were computed from grey-level run-length (GLRLM) and grey-level sizezone matrices (GLSZM) [START_REF] Robert M Haralick | Textural features for image classification[END_REF][START_REF] Thibault | Texture indexes and gray level size zone matrix application to cell nuclei classification[END_REF][START_REF] Loh | The analysis of natural textures using run length features[END_REF]. These texture matrices were calculated in 3D taking into account all 13 directions. All these features have been somewhat useful in various pattern recognition applications including medical imaging and were therefore considered in the present work.

First-order Statics

First order statics are measures that can be computed from gray-level histogram of the image hence, the locations of gray values relative to each other is not taken into account. The histogram of an image is the count of the number of pixels

Feature Extraction

in the image that possess a given grey-level value. Therefore, the statistical results would remain the same if the pixels in a region of interest (ROI) were to be scrambled. The most common first-order statistical features are the mean, median, standard deviation skewness, variance and kurtosis.

1. Mean of a histogram is the mean of the gray-level value in an image.

M ean(x)= 1 n n X i=1 x i . (2.2)
x= gray-level values n= number of gray-levels 2. Standard deviation and variance are a measure of how far from the mean the gray values in the image are distributed.

Standard deviation = v u u t 1 n -1 n X i=1 (x i -x) 2 . (2.3) V ar(X)= 1 n n X i=1 (x i -µ) 2 .
(2.4)

3. Sum is a measure of sum of all the gray level value in the image.

Sum = n X i = 1 x 2 i . (2.5)
4. Skewness of the histogram refers to the asymmetry of the distribution of the gray values. A distribution is symmetric if the right side of the distribution is similar to the left side of the distribution. If the distribution is symmetric, then the skewness value is zero. A distribution with an asymmetric tail extending out to the right is referred to as positively skewed, while a distribution with an asymmetric tail extending out to the left is referred to as negatively skewed.

Skewness = 1 n P n i=1 (x i -x) 3 ⇥ 1 n-1 P n i=1 (x i -x) 2 ⇤ 3/2 (2.6) 5.
Kurtosis is a measure of how flat or peaked the top of a symmetric distribution is when compared to a normal distribution. If the grey level distribution is similar to the normal distribution, the kurtosis value is 3. Flat-topped distributions are referred to as platykurtic and have a kurtosis value of less than 3, while less flat-topped distributions are referred to as leptokurtic and have a kurtosis value greater than 3.

Kurtosis = 1 n P n i=1 (x i -x) 4 hq 1 n P n i=1 (x i -x) 2 i 4 (2.7)

Textural Features

Texture analysis is tremendously versatile and can be applied to virtually any digital image. It refers to the appearance of the image and it is a function of spatial variations of the pixels intensities in an image [START_REF] Richard | A theoretical comparison of texture algorithms[END_REF]. MR images hold a large amount of texture information that may be relevant for clinical diagnosis. The histological changes present in various diseases may generate textural changes in the MR image that can be quantified through texture analysis. Twenty-nine textural features (Table 3.1)wereextractedbasedonthesecond-orderandhigherorder statistics accounting for intensity distribution in local and regional scale respectively, of each delineated tumor volume in each of the four MRI sequences.

Local scale textural features were computed from Grey-level co-occurrence matrix (GLCM) [START_REF] Robert M Haralick | Textural features for image classification[END_REF], whereas regional scale textural features were computed from greylevel run-length (GLRLM) [START_REF] Loh | The analysis of natural textures using run length features[END_REF]andgrey-lev elsize-zonematrices(GLSZM) [START_REF] Thibault | Texture indexes and gray level size zone matrix application to cell nuclei classification[END_REF]. These texture matrices were calculated in 3-D voxel lattice taking into account all 13 directions. All these features have been somewhat useful in various pattern recognition applications including medical imaging and were therefore considered in the present work.

Second-order Textures

Second-order statistic are feature that can be computed from Gray-Level Cooccurance Matrix (GLCM). The GLCM is a two-dimensional histogram of graylevels for a pair of pixels separated by a fixed distance (d) at a fixed angle (θ) [START_REF] Robert M Haralick | Textural features for image classification[END_REF]. The 2D GLCM considers the spatial dependency of pixels on one slice, while 3D GLCM quantifies the 3D dependency of voxel data on the object volume across several slices. Similar to the case of 2.2.D) where entry of GLCM matrix is filled whenever the number of times gray-level i and j satisfy the condition stated by the offset distance vector d=1 and angle =0 • .L i k e w i s e ,as i n g l eG L C Mm a t r i xi sc a l c u l a t e d using all the 13 directions in this work.

Each of the five GLCM-based second-order texture features that are used in this thesis are described below:

1. Entropy is the measure of randomness of the GLCM. It describes the amount of chaos or disorder within the elements of the GLCM. Entropy is higher when the image is non-uniform. Therefore, a homogeneous image will result in a lower entropy value, while an inhomogeneous (heterogeneous) region will result in a higher entropy value.

Entropy = - X i,j GLCM (i,j) log 2 GLCM (i,j) . (2.8)
2. Inverse difference moment measures the closeness of the distribution of elements in the GLCM to the GLCM diagonal. It is also known as homogeneity. It is sensitive to the near diagonal elements of the GLCM. It is higher for a diagonal GLCM.

Inverse dif f erence moment = X i,j GLCM (i,j) 1+|i -j| .
(2.9)

3. Inertia measures the intensity or gray-level variation between the reference pixel and its neighbor over the whole image. It describes the local variations in the GLCM. It is inversely correlated to homogeneity and will be lower for a diagonal GLCM.

Inertia = X i,j (i -j) 2 GLCM (i,j) (2.10) 4.
Energy describes the uniformity of the image. It measures the sum of squared elements of the GLCM. It is also known as angular second moment feature. Energy is high if the image is homogenous.

Energy = X i,j GLCM 2 (i,j) (2.11)
5. Cluster shade is a measure of the skewness of the matrix and is believed to gauge the perceptual concepts of uniformity. A new i + j image is created, having a range of integer intensities from 0 to 2(Ng 1). The ui + j value is computed and stored for the first neighborhood of the image, and is subsequently updated as the neighborhood is moved by one pixel. When the cluster shade is high, the image is asymmetric.

Cluster shade = X i,j

(i + j -µ x -µ y ) 3 GLCM (i,j) (2.12)
where, µ x = P i,j i.GLCM (i,j) is mean for column and µ y = P i,j j.GLCM (i,j) is mean for the row.

6. Cluster prominence is also a measure of asymmetry. When the cluster prominence value is high, the image is less symmetric. In addition, when cluster prominence value is low, there is a peak in the GLCM matrix around the mean values.

Cluster prominence = X i,j

(i + j -µ x -µ y ) 4 GLCM (i,j) (2.13)

Higher-order Textures

Higher order statistic are feature that can be computed from Gray-Level Run-Length Matrix (GLRLM) and Gary-Level Size-Zone Matrix (GLSZM) [START_REF] Thibault | Texture indexes and gray level size zone matrix application to cell nuclei classification[END_REF][START_REF] Loh | The analysis of natural textures using run length features[END_REF].

Grey-level Run Length Matrix:

The Grey-level run length matrix (GLRLM) is a two-dimensional histogram of total number of occurrences of grey-level runs of length j and of intensity value i in the direction θ (Fig. 2.3). This method consists in counting the number of pixel segments having the same intensity in a given direction. The 2D GLRLM considers the spatial dependency of pixels on one slice, while 3D GLCM quantifies the 3D dependency of voxel data on the object volume across several slices. The various features calculated from grey-level run length matrix of an image can be used as distinguishying features similar to GLCM matrix features. is shown in (Fig. 2.3.C) where entry of GLRLM matrix is filled whenever the number of times gray-level i and j satisfy the condition stated by the angle =0 • . Likewise, a single GLRLM matrix is calculated using all the 13 directions in this work.

Each of the five GLRLM-based third-order texture features that are used in this thesis are described below:

1. The five traditional run-length features originally proposed by Galloway [START_REF] Galloway | Texture analysis using gray level run lengths[END_REF] are:

(a) Short Run Emphasis (SRE):

SRE(θ)= 1 n r M X i=1 N X j=1 p(i, j, θ) j 2 . (2.14) (b) Long Run Emphasis (LRE): LRE(θ)= 1 n r M X i=1 N X j=1 p(i, j, θ).j 2 .
(2.15)

CONTENTS

54

(c) Grey-Level Nonuniformity (GLN):

GLN (θ)= 1 n r M X i=1 N X j=1 p(i, j, θ) ! 2 .
(2.16) (d) Run Length Nonuniformity (RLN):

RLN(θ)= 1 n r N X j=1 M X i=1 p(i, j, θ) ! 2 . ( 2
LGRE(θ)= 1 n r M X i=1 N X j=1 p(i, j, θ) i 2 .
(2.19) (b) High Grey-Level Run Emphasis (HGRE): (a) Short Run Low Grey-Level Emphasis (SRLGE):

HGRE(θ)= 1 n r M X i=1 N X j=1 p(i, j, θ).i 2 . ( 2
SRLGE(θ)= 1 n r M X i=1 N X j=1 p(i, j, θ) i 2 .j 2 . ( 2
SRHGE(θ)= 1 n r M X i=1 N X j=1 p(i, j, θ).i 2 j 2 . ( 2.22)
(c) Long Run Low Grey-Level Emphasis (LRLGE):

LRLGE(θ)= 1 n r M X i=1 N X j=1 p(i, j, θ).j 2 i 2 . ( 2
LRHGE(θ)= 1 n r M X i=1 N X j=1 p(i, j, θ).i 2 .j 2 . (2.24)
Grey-level Size Zone matrix: Grey-level size zone matrix (GLSZM) is calculated according to the pioneering Run Length Matrix principle: it estimates a bivariate conditional probability density function of the image distribution values. The value of the GLSZM matrix is equal to the number of zones of size j and of gray level (intensity) i, in other words, it is a two-dimensional histogram of total number of zones of size j and of intensity value i. The resulting matrix has afi x e dn u m b e rr o w se q u a lt ot h en u m b e ro fg r a yl e v e l s ,a n dad y n a m i cn u m b e r of columns, determined by the size of the largest zone. ) where entry of GLSZM matrix is filled whenever the number of times zones j is present representing intensity i. Likewise, a single GLSZM matrix is calculated using all the 13 directions in this work.

Each of the five GLSZM-based third-order texture features that are used in this thesis are described below:

SZE(θ)= 1 Ω M X i=1 N X j=1 z(i, j, θ) j 2 .
(2.25)

2. Long Zone Emphasis (LZE):

LZE(θ)= 1 Ω M X i=1 N X j=1 z(i, j, θ).j 2 .
(2.26)

3. Grey-Level Nonuniformity (GLN):

GLN (θ)= 1 Ω M X i=1 N X j=1 z(i, j, θ) ! 2 .
(2.27)

4. Zone Length Nonuniformity (ZLN): LGZE

ZLN(θ)= 1 Ω N X j=1 M X i=1 z(i, j, θ) ! 2 . ( 2
= 1 Ω M X i=1 N X j=1 z(i, j, θ) i 2 .
(2.30)

7. High Grey-Level Zone Emphasis (HGZE):

HGZE = 1 Ω M X i=1 N X j=1 z(i, j, θ).i 2 .
(2.31)

8. Short Zone Low Grey-Level Emphasis (SZLGE):

SZLGE = 1 Ω M X i=1 N X j=1 z(i, j, θ) i 2 .j 2 .
(2.32)

9. Short Zone High Grey-Level Emphasis (SZHGE):

SZHGE = 1 Ω M X i=1 N X j=1 z(i, j, θ).i 2 j 2 .
(2.33)

10. Long Zone Low Grey-Level Emphasis (LZLGE):

LZLGE = 1 Ω M X i=1 N X j=1 z(i, j, θ).j 2 i 2 .
(2.34)

11. Long Zone High Grey-Level Emphasis (LZHGE):

LZHGE = 1 Ω M X i=1 N X j=1 z(i, j, θ).i 2 .j 2 . (2.35)
where, Ω is the total number of homogeneous areas within the tumor, z is the intesity size-zone matrix, M is the discretization values, N is the size of the largest homogeneous area within the tumor and z(i,j) represent the number of areas with an intesnity i and a size j.

Machine Learning

Machine learning, a branch of artificial intelligence that make accurate predictions from large, noisy or complex datasets by allowing computers to "learn" from data. It uses statistical, probabilistic and optimization technique to build predictive models [START_REF] Thomas | Machine learning[END_REF]. But unlike statistics, machine learning is more powerful because it employs Boolean logic (AND, OR, NOT) and also assumes that the variable relationships are nonlinear and interdependent (or conditionally dependent) [START_REF] Richard O Duda | Pattern classification[END_REF]. These programmable computational methods are particularly well-suited to medical applications that depend on complex high dimensional measurements. As a result, machine learning is frequently used in proteomic and genomic measurements analysis. More recently, machine learning has been applied to cancer prognosis and prediction [START_REF] Kourou | Machine learning applications in cancer prognosis and prediction[END_REF]. This latter approach is particularly interesting as it is part of a growing trend towards personalized/precision medicine. It also becomes required when dealing with a large amount of radiomics features (dozens of parameters).

There are two general categories of machine learning algorithms: 1) supervised learning and 2) unsupervised learning. In supervised learning algorithms are given training data with the labeled ground truth and algorithm has to learn and distinguish patterns, whereas, in unsupervised learning no training data is provided, instead it is up to algorithm to find the patterns from data. The major types of supervised algorithms include Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM) and Artificial Neural Networks (ANN) [START_REF] Liaw | Classification and regression by randomforest[END_REF][START_REF] Jerome H Friedman | Greedy function approximation: a gradient boosting machine[END_REF][START_REF] Stuart | Artificial intelligence: a modern approach[END_REF][START_REF] David E Rumelhart | Learning representations by back-propagating errors[END_REF]. Unsupervised learning algorithms include such expectationmaximization (EM) algorithm, hierarchical clustering and K-means clustering algorithms [START_REF] To Dd K Mo On | The exp ectation-maximization algorithm[END_REF][START_REF] Stephen | Hierarchical clustering schemes[END_REF][START_REF] John | Algorithm as 136: A k-means clustering algorithm[END_REF]. Interestingly, almost all machine learning algorithms used in cancer prediction and prognosis employ supervised learning. In our work, we also use supervised machine learning to build prognostic model for GBM patients. In this section we give a detailed overview of our prognosis framework and its component. We first describe the support vector machine algorithm relying on supervised learning. Then we describe our feature extraction scheme to be used in supervised learning. Finally, we will describe our prognosis framework evaluation approach.

2.5.1 Supervised Learning: Support Vector Machine(SVM) Intuitively, supervised learning is to get a machine to learn a task, such as prediction or recognition, from a set of provided examples also called traning set that consist of labeled examples and then have the machine perform the prediction task autonomously on test set. When the predicted labels are categories (or classes), e.g. malignant and benign, then the method is referred to as classification, whereas when the labels are continuous value the method is referred as CONTENTS 60 regression [START_REF] Christopher | Pattern recognition and machine learning[END_REF].

Support vector machines (SVM) seeks to find the optimal separating hyperplane between classes, by choosing the hyperplane with the maximal margin [START_REF] Naumovich | Estimation of dependences based on empirical data[END_REF][START_REF] Cortes | Support-vector networks[END_REF]. How an SVM works can best be understood if one is given a scatter plot of points, say of tumor versus KPS performance (for GBM) among patients with excellent prognoses and poor prognoses (Figure 2.5). What the SVM machine learner would do is find the equation for a line that would separate these two classes. If one was plotting more variables (say volume, KPS and age) the line of separation would become a plane. If more variables were included the separation would be defined by a hyperplane. The SVM algorithm creates a hyperplane that separates the data into two classes with the maximum margin meaning that the distance between the hyperplane and the closest examples (the margin) is maximized. For classes that are not fully separable, a soft margin can be used, which allows some objects to lie between the margins, or even on the wrong side of the separating plane. A cost parameter, defining the penalty for misclassifying objects, is then introduced. If classes are not necessarily linearly separable in the original feature space, nonlinear feature transformations may be required. In SVM modeling this is implicitly handled by applying kernels to obtain nonlinear decision boundaries in the feature space. however, in this work we used SVM with linear kernel. Accuracy, sensitivity, specificity, and fraction of support vectors were used to evaluate the performance of the SVM models. Accuracy, sensitivity, and specificity are defined as:

Accuracy = TP + TN TP + TN + FN + FP (2.36) Sensitivity = TP TP + FN (2.37) Specif icity = TN TN + FP (2.38)
where, TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives, and FN is the number of false negatives [START_REF] Friedman | The elements of statistical learning[END_REF]. Using sensitivity/specificity in addition to accuracy ensured that both patient groups were well classified.

Feature Selection

The objective of feature selection is to improve the prediction performance of the predictors, understand the underlying process that generated the data and in most of the cases to provide faster and more cost-effective predictors. Given a training data set consisting of N instances, P predictor variables/features X i (i = 1,...,P)a n dt h ec l a s sY in {1, 2,...,C}, the objective of feature selection is to select a compact variable/feature subset without loss of predictive information about Y . Note that feature selection selects a subset of the original feature set, and, therefore, may be more interpretable than feature extraction (e.g., principal component analysis)which creates new features based on transformations of the original feature set [START_REF] Jolliffe | Principal component analysis[END_REF].

Feature selection metho ds are divided into filters, wrapp ers and emb edded methods. Wrappers utilize the learning machine of interest as a black box to score subsets of variables according to their predictive power e.g forward and backward selection. Filters select subsets of variables as a pre-processing step, independently of the chosen predictor e.g correlation criteria. Embedded methods perform variable selection in the process of training and are usually specific to given learning machines e.g SVM and Random Forest (RF) [START_REF] Guyon | An introduction to variable and feature selection[END_REF]. To improve the interpretability and to moderate the curse of dimensionality feature selection has been widely used in applications such as gene selection. [START_REF] Guyon | Gene selection for cancer classification using support vector machines[END_REF]170]. Sophisticated wrapper or embedded methods improve predictor performance compared to simpler variable ranking methods like correlation methods, so in this work we used an embedded method associated with SVM: support Vector Machine Recursive Feature Elimination (SVM-RFE) [START_REF] Guyon | Gene selection for cancer classification using support vector machines[END_REF].

Features Ranking

Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm initially proposed by Guyon et al. for gene selection in a cancer classification problem was employed for the ranking of features [START_REF] Guyon | Gene selection for cancer classification using support vector machines[END_REF]. This method selects the features in a linear separation case, based on the vector of weighted magnitude

||w ψ || 2 defined as w = X i2ζ y i α i x i (2.39)
where ζ is the number of training sets, ψ the total number of features, y is the class label, x is a feature and α is the corresponding solution of the SVM classifier. The features corresponding to smallest weight magnitudes are recursively eliminated.

The whole procedure follows the nested k-fold cross-validation in training set in order to correct for the selection bias. The features most frequently occurring on ag i v e np a r t i c u l a rr a n ka r ea s s i g n e ds a i dr a n k i n g .

Classification for Prognosis

We used multivariate pattern analysis methods "Support Vector Machine (SVM) 3 " to solve the classification problem, as this approach has been intensively studied and benchmarked against a variety of other techniques [START_REF] Guyon | Gene selection for cancer classification using support vector machines[END_REF]. As a preliminary step in our research, we chose to simplify the problem by considering a binary classification problem, in which the two classes are 'long overall survival' (above the median OS of 14.8 months) and 'short overall survival' (below or equal to 14.8 months). The concatenation of multi textural features from each MRI sequence was fed as input to the algorithm. Prior to the analysis, each feature was scaled to mean zero and standard deviation one. Four different models, one for each modality, and six different models corresponding to all combinations of a maximum of 2 different MRI sequences were trained, tuned and cross-validated using LOOCV on the 40 patient datasets. Model parameters selection for the SVM

Results

Results

Figure 4.1 presents the results for ten different models (one for each MRI sequence, as well as all possible combinations of two MRI sequences) that were trained and validated using LOOCV. The best results were consistently obtained in all modalities using textural features calculated with quantization value of B=64 (Fig. 2.6).

The best classification results considering only one MRI sequence at a time were obtained using a combination of five features (cluster shade, high grey-level zone emphasis, large zone high grey-level emphasis, grey-level non-uniform spectral homogeneity and size zone non-uniformity) extracted from the T1 post-contrast sequence, with an accuracy of 82.5%. Models based on T1 pre-contrast, T2 and FLAIR sequence exploited cluster prominence, large zone emphasis, cluster shade and grey-level non-uniformity features, with slightly lower associated accuracy of 62.5%, 72.5% and 75% respectively. Regarding the combination of two different MRI sequences, the highest accuracy was obtained by combining the T1 pre-contrast and T1 post-contrast sequences, with an associated accuracy of 90% (sensitivity 85% and specificity 95%). Two features only (high grey-level zone emphasis and variance) from T1 pre-contrast and four features (cluster prominence, short run high grey-level emphasis, size zone non-uniformity and sum) from T1 post-contrast contributed in this model. Note that for this best model, features from first, second and third orders were retained. The feature subset selection method showed that four features from local and regional scales (cluster prominence, cluster shade, grey-level non-uniformity and size zone non-uniformity) were consistently considered as relevant. Overall, the T1 post-contrast MRI sequence seemed to offer the most relevant information regarding prognosis. The model built using contextual clinical variables (age, gender, karnofsky and treatment modality (radiotherapy, chemotherapy and surgery)) only reached a limited accuracy of 58%. In the multivariate selection when included with image-derived features, none of these clinical variables were retained for building the model.

Discussion-Conclusion

Discussion-Conclusion

Our results suggest that using multivariate methods such as SVM with the combination of global, local and regional textural features quantifying heterogeneity in all four MR modalities available in routine clinical acquisitions can provide a model with high prognostic value. However, one drawback of multivariate methods using high dimensional features is the lack of a framework for estimating the statical interpretation because unlike univariate methods, SVM model does not compute statistical tests (and corresponding p-values) associated with the significance level. Rather, evaluating the SVM models' maximum weight coefficients recursively helps in ranking the significant features. In addition, selecting an optimal number of significant features and building the SVM models based on forward selection of features gives the highest cross-validation accuracy. We emphasize that these results are only preliminary and the present work has a few limitations: i) the analysis included only 40 patients and validation was carried out using LOOCV, which is more optimistic than validation in an independent dataset; ii) prognosis analysis was simplified by considering a binary classification problem (SOS and LOS) instead of a continuous variable; iii) we did not investigate the combination of more than two MR sequences among the four available due to the limited number of patients. We emphasize however that the analysis is currently ongoing, and that additional patients will be included in the future, which will allow for a more rigorous evaluation of the accuracy, by testing the trained models on an additional group of patients not used in the model training. In addition, we expect that by projecting these image features in a higher dimension using a "kernel trick" (for eg. Radial Basis Kernel) for non-linear multivariate analysis and by combining features from all four MRI sequences, an even more robust and accurate model for feature selection and classification can be developed. However, for a reliable analysis, we will have to wait for more patients datasets to be processed through the entire described workflow (pre-processing and segmentation of the images constitutes the bottleneck for producing more results). Future work will consist in i) adding more patients for validation using training and testing groups, ii) improving the pre-processing steps of voxels spatial resampling, standardization and quantization, as recently suggested [START_REF] Vallières | A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities[END_REF]i i i ) considering additional features to improve the proposed model, iv) building a fully multimodal MRI model by combining features from more than two sequences, v) considering survival as a continuous variable using survival SVM method [START_REF] Evers | Sparse kernel methods for high-dimensional survival data[END_REF] and vi) building and comparing models with various other machine learning based classifier techniques such as random forest or artificial neural network.

Overview on next chapter

In the next chapter, first eighteen patients datasets are added (58 patients in total, previously 40) and all the available MRI modalities (T1-weighted pre-and postcontrast, T2 and FLAIR) are used for analysis. The prognostic model depends on using one, combination of two, three and all the four MRI sequences. However, the feature selection and model building steps remain the same as described in the current chapter. Furthermore, in the next chapter, due to multi centric nature of the cohort, the variability in acquisition protocols and scanner models involved could lead to undesirable variability in the textural features, and a resulting bias in the classification performance. Therefore, the next chapter is specifically dedicated to investigate the potential benefit and respective impact of the addition of several MRI pre-processing steps (spatial resampling for isotropic voxels, intensities quantization and normalization) before textural features computation, on the resulting accuracy of the classifier. Hence, the next chapter outlines the appropriate image pre-processing steps to be used for methodologies based on textural features extraction in MRI and evaluates the prognostic models based on various combination of MRI modalities. In the previous chapter, a framework for automatic extraction and combination of image-derived features (also called Radiomics) through support vector machines (SVM) for predictive model building was developed. The results were obtained in a cohort of 40 GBM patients and only two MRI modalities (T1weighted pre-and post-contrast sequences) were used for analysis. The results suggested MRI-derived textual features could be used to identify patients with poorer outcome. The original developed work-flow included skull removal, bias homogeneity correction, and multimodal tumor segmentation, followed by textural features computation, and lastly ranking, selection and combination through a SVM-based classifier. In this chapter, the workflow remains the same, however, additional pre-procesing steps, more patients cohort and all four MRI modalities (T1-weighted pre-and post-contrast. T2-weighted and FLAIR) are added. Moreover, this chapter deals with the crucial importance of investigating appropriate image pre-processing steps to be used for methodologies based on textural features extraction in MRI.

Prognosis Classification in

Heterogeneity image-derived features of Glioblastoma multiforme (GBM) tumors from multimodal MRI sequences may provide higher prognostic value than standard parameters used in routine clinical practice. We previously developed af r a m e w o r kf o ra u t o m a t i ce x t r a c t i o na n dc o m b i n a t i o no fi m a g e -d e r i v e df e a t u r e s (also called "Radiomics") through support vector machines (SVM) for predictive model building. The results we obtained in a cohort of 40 GBM suggested these features could be used to identify patients with poorer outcome. However, ex-traction of these features is a delicate multi-step process and their values may therefore depend on the pre-processing of images. The original developed workflow included skull removal, bias homogeneity correction, and multimodal tumor segmentation, followed by textural features computation, and lastly ranking, selection and combination through a SVM-based classifier. The goal of the present work was to specifically investigate the potential benefit and respective impact of the addition of several MRI pre-processing steps (spatial resampling for isotropic voxels, intensities quantization and normalization) before textural features computation, on the resulting accuracy of the classifier. Eighteen patients datasets were also added for the present work (58 patients in total). A classification accuracy of 83% (sensitivity 79%, specificity 85%) was obtained using the original framework. The addition of the new pre-processing steps increased it to 93% (sensitivity 93%, specificity 93%) in identifying patients with poorer survival (below the median of 12 months). Among the three considered pre-processing steps, spatial resampling was found to have the most important impact. This shows the crucial importance of investigating appropriate image pre-processing steps to be used for methodologies based on textural features extraction in medical imaging.

Introduction

Glioblastoma multiforme (GBM) is the most aggressive and malignant tumor found in 50% of brain tumors patients. The prognosis is poor, as 50% of patients die within 14 months despite aggressive multimodal treatments (radiotherapy, chemotherapy and surgical resections) [START_REF] Stupp | Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma[END_REF]. Recent years have witnessed 3-dimensional multimodal magnetic resonance imaging (MRI) being routinely employed as an imaging technique of choice for diagnosis, treatment planning and monitoring of GBM [START_REF] John W Henson | MRI in treatment of adult gliomas[END_REF]. According to recent studies, quantitative imaging biomarkers extracted from multimodal MRI could have great potential for stratifying patients at diagnosis regarding their prognosis [START_REF] Cui | Prognostic imaging biomarkers in glioblastoma: Development and independent validation on the basis of multiregion and quantitative analysis of MR images[END_REF][START_REF] Upadhaya | Prognostic value of multimodal MRI tumor features in glioblastoma multiforme using textural features analysis[END_REF][START_REF] Gevaert | Glioblastoma multiforme: Exploratory radiogenomic analysis by using quantitative image features[END_REF]. Some clinical variables (preoperative Karnofsky Performance Status (KPS),

Introduction

age, extent of resection after surgery) and imaging features (volume, extent of edema, degree of necrosis, major and minor axis length) have been associated with survival [START_REF] Maciej A Mazurowski | Computer-extracted mr imaging features are associated with survival in glioblastoma patients[END_REF][START_REF] Whitney B Pope | MR imaging correlates of survival in patients with high-grade gliomas[END_REF]. Recently, in order to standardize the assessment of GMB tumors from a qualitative and quantitative standpoint, 24 morphological visual observations from MR sequences by neuroradiologists were derived from a multi-institutional effort and called VASARI (Visually Accessible Rembrandt Images) [START_REF] David A Gutman | Mr imaging predictors of molecular profile and survival: multi-institutional study of the tcga glioblastoma data set[END_REF]. GBM is characterized by extensive heterogeneity at the cellular and molecular levels across and within patients' tumor. Heterogeneity is also captured at the macroscale level by multimodal imaging, such as MRI [START_REF] Sottoriva | Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics[END_REF]. However, to date in most studies only simple geometric image-derived features from MRI have been explored. These do not fully quantify intra tumor heterogeneity characteristics imaged through multimodal MRI. Textural features analysis of multimodal MRI may quantitatively characterize multidimensional, in vivo heterogeneity within tumors. Global, regional and local textural features have been successfully used to quantify tumor heterogeneity in multimodal images of tumors [START_REF] Tixier | Intratumor heterogeneity characterized by textural features on baseline 18f-fdg pet images predicts response to concomitant radiochemotherapy in esophageal cancer[END_REF][START_REF] Evangelia I Zacharaki | Classification of brain tumor type and grade using mri texture and shape in a machine learning scheme[END_REF][START_REF] Farhidzadeh | Texture feature analysis to predict metastatic and necrotic soft tissue sarcomas[END_REF]. Recently, Vallières et al. exploited MRI and PET combined texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities [START_REF] Vallières | A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities[END_REF]. In our previous study, we developed an operational workflow for multimodal GBM MR images pre-processing, registration, segmentation, characterization of heterogeneity, and prognostic model training and validation using Support Vector Machine (SVM). Feature selection and classification was based on the recursive feature elimination algorithm [START_REF] Guyon | Gene selection for cancer classification using support vector machines[END_REF]. Our preliminary results in a cohort of 40 patients suggested that textural features extracted from multimodal MRI could provide higher prognostic value than standard clinical variables and standard image features [START_REF] Upadhaya | A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in glioblastoma multiforme[END_REF]. However, due to the multi centric nature of the cohort, the variability in acquisition protocols and scanner models involved could lead to undesirable variability in the textural features, and a resulting bias in the classification performance.

The goal of this work was therefore to investigate the potential benefits and respective impact of several MRI pre-processing steps (spatial resampling of voxels, intensities quantization and normalization) to be performed before textural features computation, on the resulting accuracy of the classifier. This study was carried out on a slightly larger cohort of patients than the previous one (58 instead of 40) and also evaluates the prognostic models based on various combination of MRI modalities (one, combination of two, three and all the four).

This chapter is organized as follows: Section 2 presents the cohort used and the previously developed framework for prognostic predictive model; Section 3 presents the pre-processing steps that were considered to improve reliability and robustness of textural features computation within the current multicentric context; Section 4 provides the results and associated discussion; whereas, Section 5 contains the conclusion of the study.

Materials and Methods

Patient Cohort and Imaging Data

The MR images of the 58 GBM patients (along with clinical contextual data) retrospectively analyzed in this work were acquired from the Cancer Imaging Archive 1 , an imaging portal containing anonymized and publicly available images. In the analysis, patients were divided into two groups for the classification task: short and long overall survival with a threshold of 12 months corresponding to the median survival (mean 15.1, range 1-54, n=29 in each group). All patients were histopathologically diagnosed with GBM and treated with radiotherapy and chemotherapy. All 58 patients had the following baseline MRI sequences for analysis: 1) T1-weighted pre contrast, 2) T1-weighted post contrast, 3) T2-weighted, and 4) FLAIR. These scans exhibited a large variability in acquisition protocols and reconstruction parameters across the cohort. Overall, the median in-plane resolution was between 0.86 × 0.86 mm2 and 0.93 × 0.93 mm 2 ,a n dt h es l i c e 73

Materials and Methods

thickness was between 3 mm and 7.5 mm.

Framework for Radiomics Analysis

Ap r o g n o s t i cm od e lw a sd e r i v e df r o mab i n a r yc l a s s i fi c a t i o np r o b l e m ,c l a s s i f y i n g the patients as either above or below the median survival. The following workflow was developped to achieve the classification:

Pre-processing Steps:

Several factors such as patients' position, or scanner magnetic settings can induce intensity inhomogeneities in the same tissue types. This is usually corrected using N4ITK [177]t oi m p r o v et h er e l i a b i l i t ya n dp e r f o r m a n c eo fs e g m e n t a t i o n and classification algorithms. In addition, skull stripping using ITK filter [START_REF] Menze | The multimodal brain tumorimage segmentation benchmark[END_REF]is performed to separate the brain from the skull and other surrounding structures to facilitate automated MRI images registration and segmentation in subsequent steps.

Registration and Tumor Volume Delineation:

All the MRI sequences (T1 pre-contrast, T2 and FLAIR) were rigidly registered to the T1 post-contrast image using mutual information similarity metric. Segmentation of the tumor regions was carried-out using all four MRI sequences and a single mask with labels representing edema, necrosis and active tumor was automatically produced. All the above preprocessing steps were carried out using Brain Tumor Image Analysis software (BraTumIA) 2 ,af u l l yd e d i c a t e do p e n source software for GBM segmentation using MRI sequences.

Pre-processing Schemes

Feature Extraction:

Thirty-four textural features (Table 3.1)w e ree xtra c te dba s e do nfi rs t-(in te n s it y histogram), second-(co-occurence matrix) and higher-order (grey-level run length and grey-level size zone matrices) statistics accounting for intensity distribution in global, local and regional scale respectively, of each delineated tumor volume in each of the four MRI sequences.

Machine Learning for Prognostic Model Building:

We used multivariate pattern analysis metho ds based on Supp ort Vector Machine (SVM) for building a prognostic model. To deal with the curse of dimensionality due to the large number of features, these were ranked and selected to build the model with an optimal combination of features. For this work we used the open source R package CARET (Classification And REgression Training) [179].

For further details regarding the develop ed framework we refer the interested readers to our previous publications [START_REF] Upadhaya | Prognostic value of multimodal MRI tumor features in glioblastoma multiforme using textural features analysis[END_REF][START_REF] Upadhaya | A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in glioblastoma multiforme[END_REF]a n ds e c t i o n2.5.

Pre-processing Schemes

The following pre-processing steps before the textural features computation could contribute to the improvement of the reliability and robustness of extracted features across scanner types and image acquisition variability as previously suggested [START_REF] Vallières | A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities[END_REF]. These steps are spatial resampling of voxels, multimodal MRI graylevel normalization and finally, the method used to quantitize original grey-levels into a given discrete number of values for texture matrices computation.

Isotropic Voxels

The original voxel sizes were between 0.93 × 0.93 × 3 mm 3 and 0.89 × 0.89 × 7.5 mm 3 .T h e yw e r er e s a m p l e di n t oe i t h e ri s o t r o p i cv o x e l so f1 × 1 × 1 mm 3 as previously suggested [START_REF] Vallières | A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities[END_REF]o rn o n -i s o t r o p i cv o x e l s1× 1 × 3 mm 3 as in our previous work [START_REF] Upadhaya | A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in glioblastoma multiforme[END_REF]. Resampling was performed using Lagrangian interpolation implemented in MIPAV [180]. The Lagrange kernel of degree N-1 for an N × N region with n ∈ {-N/2+1, -N/2+2,...,N/2} can be defined by Equation 1.

Lagra hn (x)= 8 > > < > > : N -1 Y j=0,j-N/2+16 =n n-i-x n-i ,n-1 ≤ x<n 0, elsewhere (3.1)
Where, i = j -N/2+1.

In our case, the Lagrange kernel for N=4 supporting points results in cubic polynomials. The mean intensity of the image is not affected if the image is interpolated by Lagrange Kernels [180].

Normalization

As the multimodal MR images of GBM for this study were acquired from the Cancer Imaging Archive various scanner models and acquisition protocols could introduce variability in the textural features values. And thus, textural features could be sensitive to these. We used the method suggested by Collewet et al. [START_REF] Collewet | Influence of MRI acquisition protocols and image intensity normalization methods on texture classification[END_REF], consisting in normalizing intensities between µ ± σ,w h e r eµ and σ are mean and standard deviation value of the intensities inside the delineated tumor volume. The intensities values outside the range [µ + σ, µ -σ]w e r et h e ns i m p l yi g n o r e d during the textural features calculations.

Quantization

All voxels values within the delineated lesions were re-sampled to yield a finite range of values to allow for calculating the textural features. This quantization is necessary to compute textural features (the chosen value determines the size of the second-and higher-order matrices). As suggested by Vallières et al. [START_REF] Vallières | A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities[END_REF] the images grey-levels were either quantized using the Lloyd-Max algorithm or using the uniform approach, as in our previous work [START_REF] Upadhaya | A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in glioblastoma multiforme[END_REF]. Usually, different quantization values are considered between 8 and 256. In this work, we chose 64 based on our previous investigations [START_REF] Upadhaya | Prognostic value of multimodal MRI tumor features in glioblastoma multiforme using textural features analysis[END_REF]. The main aim of this quantization step is to normalize intensities among the modalities and across patients, and reduces the spatial variability of intensities (noise). survival curves obtained with the median survival cut-off and with the classified patients with the best performance (93% accuracy). After the addition of the pre-processing steps, some previously misclassified patients (both green and black circled) were correctly classified (green circled only) into their respective classes, increasing the model accuracy. The results suggest that the textural features extracted from multimodal MRI exhibit some variability due to different scanner models and acquisition protocols involved in the process. However, spatial resampling into isotropic voxels, normalization of grey levels, and improved quantization made the textural features more robust and reliable, resulting in ap o t e n t i a lb e n e fi ti nt h ea c c u r a c ya c h i e v e db yt h ec l a s s i fi e r . T h er e s u l t sp r e -

Results and Discussion

Results and Discussion

sented in Figure 3.2 are based on the highest accuracies obtained from optimal features' combinations from top ten features (that were selected and ranked based on leave-one out cross validation) in the 58 patients. The green curve represents the accuracy reached using the original framework, whereas red, yellow and blue curves represent the accuracy achieved after adding the various pre-processing steps. The accuracy obtained with a combination of isotropic voxels resampling and Lloyd-Max quantization is shown in blue, with accuracy between 70% and 93% depending on the number of modalities exploited. Comparing the results achieved between single or several MR modalities, the combination of T1C, T2 and FLAIR sequences led to the highest accuracy. Overall, exploiting additionnal sequences led to increased accuracy, showing the potential value of extracting and combining textural features from several modalities. The best performance reached using the original framework was 83% accuracy, 79% sensitivity, 85% positive predictive value, 86% specificity and 80% negative predictive value. After adding the new pre-processing steps, the best performance increased to 93%, sensitivity to 93%, positive predictive value to 93%, specificity to 93% and negative predictive value to 93%. Spatial resampling of voxels into isotropic voxels of 1×1×1 mm 3 had the most substantial impact on the accuracy (between 68% and 93% vs. between 63% and 83%). Our results suggest that global, regional and local textural features quantifying heterogeneity in all four MR modalities available in routine clinical acquisitions can provide complementary prognostic value. In another set of experiments, we built a model with the contextual clinical variables such as age, gender, karnofsky and treatment modality (radiotherapy, chemotherapy and surgery) and clinical imaging features such as volume, major axis length and minor axis length. The variables gender and treatment modality were categorized using 1-of-k coding scheme. The model built using contextual clinical variables only reached a limited accuracy of 58%, whereas the one built using clinical imaging features reached an accuracy of 67%. When incorporated with heterogeneity textural features in the multivariate selection, none of these clinical variables or standard imaging features were retained for building the model, suggesting textural features from MRI have higher prognostic value. Additionally, in another set of experiments, in order to assess the impact of texture features in resulting classification accuracy, the features were again calculated by varying the tumor contours by expanding or shrinking it by 20% in each of the four MRI sequences. Figure 3.3 shows the corresponding results. The mean and standard deviation (SD) of the accuracy varied depending on the number of features used to build the model. Using less features high accuracies was obtained with high SD whereas, using more features lower accuracy was obtained with less SD. For example, when using one modality SD of classification results for T2 was highest (SD 7%) and SD for Flair image was the lowest (SD 4.5). T1 pre-contrast and T1 post-contrast sequence where in between, with SD of 5.3 and 5 respectively. Similar kind of results were obtained when using combination of 2, 3 or all the MRI sequences (Figure 3.3). The results suggest that texture feature extract from Flair image are highly reliable, T1 pre-and post-contrast image are moderately reliable and T2 image were least reliable. Thus, intra observer variability in segmentation may lead to bias in classification however, the model build with these sequence still led to models with high accuracy.

We emphasize that these results are only preliminary and the present work has several limitations: i) the analysis included only 58 patients and validation was carried out using LOOCV, which is more optimistic than validation in an independent dataset; ii) survival analysis was carried out as a binary classification problem; iii) we did not compare the models accuracy and features obtained through different machine learning schemes. In future work, we will consider adding more patients for validation using training and testing groups, consider survival as a continuous variable, compare models accuracy and features with various other machine learning based classifier techniques such as random forest or artificial neural network and finally, combining these purely image-based models with other histopathological parameters with demonstrated prognostic power, in order to even further improve/complement their prognostic power.

Conclusion

Conclusion

In this paper, we have investigated the potential benefits and impact of different pre-processing steps on textural feature extraction from multimodal MRI. A method to normalize the MRI intensities and two different voxel resampling and intensities quantization methods' impact on textural features were compared in the present work. These additional pre-processing steps in a cohort of 58 patients with GBM suggest that the features extracted from different MR scanners due to various acquisition protocols can influence the classification accuracy. However, appropriate choices in image pre-processing (spatial voxels resampling, normalization and quantization) shows potential benefits in extracting more reliable features, resulting in improved accuracy of the classifier.

Overview on next chapter

Next chapter deals with comparing two benchmarked machine learning techniques such as Support Vector Machines (SVM) and Random Forest (RF) for building predictive models by selecting, ranking and combining optimal features. These algorithm were chosen as they exploit different paradigms to perform the classification task. Moreover, radiomics features were in addition evaluated for their robustness and models were built using either the entire set of features or by exploiting only the robust ones. Furthermore, in order to standardize the radiomics among various centres, features were compared, improved and added upon by participating in the standardization process called Image Biomarker Standardisation Initiative (IBSI). The main aim of the chapter was to highlight the importance of the features selection and classifier techniques choices, as well as the impact of exploiting only robust features to build the model. 6. The multi-platform configuration tool CMake was used for configuring ITK and facilitating its use from our project. CMake is an open source system and it is freely available at www.cmake.org.

Data used in the preparation

7. This machine learning work benefited from the use of the CARET package in R. The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models and freely available at https://cran.r-project.org/web/packages/caret/index.html

The previous chapter was dedicated to investigating the potential benefit and respective impact of the addition of several MRI pre-processing steps (spatial resampling for isotropic voxels, intensities quantization and normalization) before textural features computation, on the resulting accuracy of the classifier. Moreover, all the available MRI modalities (T1-weighted pre-and post-contrast, T2 and FLAIR) were used for analysis. The prognostic models performances varied depending on the number of sequences exploited to extract features and build them. In this chapter, the pipeline for pre-processing and features extraction is the same as in the previous chapters. However, in this chapter we focus on the standardization of radiomics among centres, robust radiomics extraction and more specifically feature selection and prognostic model building by comparing two benchmarked state-of-the-art feature selection and classifier technique, SVM and RF.

The new era of "Radiomics" (image-derived features) have shown potential in prognosis stratification to improve the clinical management of glioblastoma 4.1 Introduction multiforme patients. The large amount of features involved calls for the use of robust machine learning techniques to identify and optimize the combination of the most relevant features with high prognostic value. Benchmarked machine learning techniques such as Support Vector Machines (SVM) and Random Forest (RF) have been extensively exploited for building predictive models, resulting in accurate decision-making. The goal of the present work was to compare these feature selection and classification methods for building a prognostic model in GBM that would combine clinical and contextual features with radiomics features from multimodal MRI. The radiomics features were in addition evaluated for their robustness and models were built using either the entire set of features or by exploiting only the robust ones. SVM obtained a higher accuracy (100% using all features and as well as robust ones) compared to RF (76% and 74% respectively). These results highlight the importance of the features selection and classifier techniques choices, as well as the impact of exploiting only robust features to build the model. Future work will consist in testing in an external validation dataset these models to investigate whether using robust features only improves their validation performance.

Introduction

Glioblastoma multiforme (GBM) is a WHO grade IV incurable tumor that represents 49% of all brain tumours [START_REF] David N Louis | The 2016 world health organization classification of tumors of the central nervous system: A summary[END_REF]. Despite aggressive treatment modalities (radiotherapy, chemotherapy and surgical resections) the prognosis is poor, as median overall survival (OS) is 12-14 months [START_REF] Stupp | Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase iii study: 5-year analysis of the eortcncic trial[END_REF]. Magnetic resonance imaging (MRI) captures the highly varied morphological heterogeneity like enhancing tumor with central areas of necrosis and peritumoral vasogenic edema of GBM tumor [START_REF] John W Henson | Mri in treatment of adult gliomas[END_REF]. Identifying and quantifying these heterogeneity characteristics non-invasively as an imaging biomarker to predict survival prognosis can help in individualizing patients management. Historically, contextual clinical features such as Karnofsky Performance Status (KPS), age, extent of resection and degree of necrosis, and rudimentary clinical imaging features such as measurements of tumor volume, major/minor axis length derived from MRI, were found to be significant prognostic factors [START_REF] Pascal O Zinn | A novel volume-age-kps (vak) glioblastoma classification identifies a prognostic cognate micrornagene signature[END_REF][START_REF] Nicolasjilwan | Addition of mr imaging features and genetic biomarkers strengthens glioblastoma survival prediction in tcga patients[END_REF]. Recently, in order to standardize the assessment of GMB tumors from a qualitative and quantitative standpoint, 24 morphological visual observations from MR sequences by neuroradiologists were derived from a multi-institutional effort and denoted VASARI (Visually Accessible Rembrandt Images) [START_REF] David A Gutman | Mr imaging predictors of molecular profile and survival: multi-institutional study of the tcga glioblastoma data set[END_REF]. Although useful, VASARI does not explicitly incorporate within-tumor heterogeneity characteristics and cannot quantitatively exploit the complementary value of all the multimodal MRI (T1-weighted preand post contrast, T2-weighted, FLAIR) available from standard clinical protocols. We previously developed a pipeline allowing for the extraction of radiomics features (including intensity, shape and textural metrics) from multimodal MRI sequences. This pipeline was exploited to build a prognostic model in GBM patients with high accuracy in identifying patients with poor survival [START_REF] Upadhaya | Prognostic value of multimodal MRI tumor features in glioblastoma multiforme using textural features analysis[END_REF][START_REF] Upadhaya | Prognosis classification in glioblastoma multiforme using multimodal mri derived heterogeneity textural features: impact of pre-processing choices[END_REF][START_REF] Upadhaya | A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in glioblastoma multiforme[END_REF]. We also demonstrated the p otential b enefit of some image pre-pro cessing steps such as isotropic voxels resampling and noise filtering as the dataset we used to train the model is a multi-center cohort with numerous different MRI systems involved [START_REF] Upadhaya | Prognosis classification in glioblastoma multiforme using multimodal mri derived heterogeneity textural features: impact of pre-processing choices[END_REF].

Our present study has two objectives: i) evaluate the robustness of the features and incorporate this assessment in the feature selection step of the model building process; ii) compare state-of-the-art feature selection and classifier techniques for building the models.

Materials and Methods

In this section, first imaging data and patients cohort used for the study are described. Secondly, the previously developed framework for radiomics analysing of GBM involving various preprocessing steps is shortly summarized. Additional details can be found in previous chapters 2 and 3. The feature selection and classifier techniques compared in this work are also shortly described.

Patients Cohort and Imaging Data

The MR images of 96 GBM patients (along with clinical and contextual features) retrospectively analyzed in this work were acquired from the Cancer Imaging Archive (n=75)1 [START_REF] Clark | The cancer imaging archive (tcia): maintaining and operating a public information repository[END_REF], an imaging portal containing anonymized and publicly available images and from CHRU Brest-Centre Hospitalier Rgional et Universitaire de Brest and (n=21). In the analysis, patients were divided into two groups for the classification task: short and long overall survival with a threshold of 11.7 months corresponding to the median survival (mean 14.99, range 1-69.9 months, n=48 in each group). All patients were histopathologically diagnosed with GBM and treated with radiotherapy and/or chemotherapy. All 96 patients had the following baseline MRI sequences available for analysis: 1) T1-weighted pre-contrast, 2) T1-weighted post-contrast, 3) T2-weighted, and 4) FLAIR. As patients in this cohort were imaged in numerous different clinical centres, these scans exhibited a large inter-patient variability in acquisition protocols and reconstruction parameters, hence in image quality and characteristics. For example, the median in-plane resolution was between 0.86×0.86 mm 2 and 0.93×0.93 mm 2 , and the slice thickness was between 3 mm and 7.5 mm. According to our previous results chapter 3,a l li m a g e sw e r efi r s tr e s a m p l e dt o1× 1 × 1 mm 3 for better results.

Overview of the Framework for Radiomics Analysis

A framework (Fig. 4.1) for prognostic model was derived to address the binary classification problem, classifying the patients as either above or below the median survival. The following workflow was developed:

1. All the images were corrected for inhomogeneity using the N4ITK filter, the MRI sequences (T1 pre-contrast, T2 and FLAIR) were registered to the T1 post-contrast image using mutual information similarity metric and skull stripping using ITK filter was performed [START_REF] Josien Pw Pluim | Mutualinformation-based registration of medical images: a survey[END_REF]177,[START_REF] Bauer | A skull-stripping filter for itk[END_REF].

2. Segmentation of the tumor regions was carried-out using all four MRI sequences and a single mask with labels representing edema, necrosis and active tumor was automatically produced. All the above preprocessing steps were carried out using Brain Tumor Image Analysis software (BraTu-mIA [START_REF] Porz | Multi-modal glioblastoma segmentation: Man versus machine[END_REF]), a fully dedicated open source software for GBM segmentation using MRI sequences. A binary mask containing active tumor and necrosis was visually checked and manually edited when needed by an expert clinician in neuro-oncology.

3. According to our latest results, we also performed spatial resampling of voxels into isotropic 1 × 1 × 1mm 3 , as well as intensities normalization and quantization as additional pre-processing steps to address the multicentric nature of images and the associated variability chapter 3.

4. Thirty-four textural features were extracted based on the first-(intensity histogram), second-(co-occurence matrix [START_REF] Robert M Haralick | Textural features for image classification[END_REF]a n dh i g h e r -o r d e r( g r e y -l e v e l run length [START_REF] Loh | The analysis of natural textures using run length features[END_REF]andgrey-lev elsizezonematrices [START_REF] Thibault | Texture indexes and gray level size zone matrix application to cell nuclei classification[END_REF]) statistics accounting for intensity distribution in global, local and regional scale respectively, of each delineated tumor volume in each of the four MRI sequences. In addition, 3 standard geometrical (e.g. minor/major axis length) and six contextual features (e.g. age or Karnofsky's score) were considered for model

Materials and Methods

building. More details about each of the above steps are provided in our previous publications [START_REF] Upadhaya | Prognostic value of multimodal MRI tumor features in glioblastoma multiforme using textural features analysis[END_REF][START_REF] Upadhaya | Prognosis classification in glioblastoma multiforme using multimodal mri derived heterogeneity textural features: impact of pre-processing choices[END_REF][START_REF] Upadhaya | A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in glioblastoma multiforme[END_REF]a n dc h a p t e r s2 and 3.

Multicentre Initiative for Standardisation of Radiomics

In order to standardize the radiomics among various centres, all the features were compared and improved upon by participating in the standardization process called Image Biomarker Standardisation Initiative (IBSI 4.2). It addresses the challenge of reproducing and validating by comparing and standardising definitions and implementation of several image feature sets between participating institutions. A Digital phantom (Fig. 4.3)ofv o xel5× 4 × 4w asdevised,witha super-imposed region-of-interest (ROI) mask. The phantom was sufficiently small to manually calculate features and, since no pre-processing steps (discretisation, spatial filters, etc.) were necessary for features calculations feature values were easily standardised across an international network of x participants (list here the instutions participating in it).

Fourteen geometric features and fifty-eight features (Tab. 4.1)b a s e do nt h e first-(intensity histogram), second-(co-occurence matrix [START_REF] Robert M Haralick | Textural features for image classification[END_REF]a n dh i g h e r -o r d e r (grey-level run length [START_REF] Loh | The analysis of natural textures using run length features[END_REF]a n dg r e y -l e v e ls i z ez o n em a t r i c e s [START_REF] Thibault | Texture indexes and gray level size zone matrix application to cell nuclei classification[END_REF]) in 3D either for every direction and averaged (avg), or after merging the matrices into a single matrix (Single) were derived and standardised between institutions. Statistical features were more easy to standardise as the degrees of freedom are more limited compared to other feature sets. The feature definitions, digital phantom and corresponding feature values will be made available as a standard benchmark database for use by other institutions.

Robustness Analysis

Ideally, reliability of features may be assessed using test-retest images, however no MRI test-retest database were available to us [START_REF] Desseroit | Reliability of pet/ct shape and heterogeneity features in functional and morphological components of non-small cell lung cancer tumors: a repeatability analysis in a prospective multi-center cohort[END_REF][START_REF] Tixier | Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18f-fdg pet[END_REF]. So, in order to assess reliability of features, their robustness was quantified based on their dependency on differences in the segmentation. Fifty-eight previously standardised intensity image features in 3D based on first-(intensity histogram), textural second-(cooccurence matrix) and higher-order (grey-level run length and grey-level size zone matrices) statistics and eight geometric features were extracted from each delineated tumor volume and in each of the four MRI sequences. In order to assess the robustness of these features, their dependency on differences in the segmentation of tumors was quantified by varying the tumor contours by expanding or shrinking it by 20% (Fig. 4.4b). Robustness of each feature was assessed using Bland-Altman analysis [START_REF] Myles | I. using the bland-altman method to measure agreement with repeated measures[END_REF]. Features were then categorized into four groups of reliability: very reliable (≤ 5% standard deviation variability), reliable (between < 5% and ≤ 10%), moderately reliable (between < 10% and ≤ 20%) and unreliable (>20%). We emphasize that this reliability does not tell anything about their relevance to the clinical endpoint (OS), only their level of dependency on variable segmentation masks, hence their level of robustness and reliability.

Feature Selections

Selection of relevant radiomics features for various classification tasks (e.g., to differentiate between patients with various grade, identifying patients at risk, and assessing the disease progression) are a common challenge in most radiomics studies. When facing feature selection problems in radiomics, often the goal is one of the following:

• To identify relevant radiomics for subsequent research; this involves obtaining a (probably large) set of features that are related to the outcome of interest, and this set should include features even if they perform similar functions and are highly correlated.

• To identify small sets of radiomics that could b e used for diagnostic purposes in clinical practice; this involves obtaining the smallest possible set of features that can still achieve good predictive performance, thus, "redundant" radiomics should not be selected.

In our work the focus is on the second objective. Most radiomics selection approaches for predictive models combine ranking features (e.g., using an F-ratio or a Wilcoxon statistic or correlation methods (univariate analysis)) with a specific classifier (e.g., discriminant analysis, nearest neighbor). There are some guidelines available for selecting optimal number of features for classification task [START_REF] Hua | Optimal number of features as a function of sample size for various classification rules[END_REF]. However, frequently an arbitrary decision as to the number of features to retain is made (e.g., keep the 10 best ranked features and use them with a linear discriminant analysis, Generalized linear models, Nearest neighbors (multivariate analysis),... as in [START_REF] Parmar | Machine learning methods for quantitative radiomic biomarkers[END_REF]). This approach, although it can be appropriate when the only objective is to classify samples, is not the most appropriate if the objective is to obtain the smaller possible sets of radiomics that will allow good predictive performance. Another common approach, with many variants (e.g., [START_REF] Cr Freeman | A radiomics model from joint fdg-pet and mri texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities[END_REF]) wrapper (multivariate analysis), is to repeatedly apply the same classifier over progressively smaller sets of features until a satisfactory solution is achieved (often the smallest error rate over all sets of features tried). A potential problem of this second approach, if the elimination is based on univariate rankings, is that the ranking of a feature is computed in isolation from all other features, or at most in combinations of pairs of features, and without any direct relation to the classification algorithm that will later be used to obtain the class predictions. Finally, the problem of features selection is more problematic in multiclass situations. Therefore, classification algorithms that directly provide measures of variable importance (related to the relevance of the variable in the classification) are of great interest for radiomics selection, specially if the classification algorithm itself presents features that make it well suited for the types of problems frequently faced with radiomics. We compared two popular such embedded methods for feature selection; support vector machine and random forest. Moreover, these two methods were chosen based on their popularity in the literature and because they exploit different paradigms to perform the feature selection and classification task. As mentioned earlier these embedded (build-in multivariate feature selection) methods have been shown to be superior to filter (univariate) 99

Materials and Methods

and wrapper (multivariate) methods for feature selection [START_REF] Guyon | An introduction to variable and feature selection[END_REF]. So, in our work these two methods were used to adress the problem of curse of dimensionality by reducing the number of features prior to classification.

SVM-RFE.

Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm initially proposed by Guyon et al. [START_REF] Guyon | Gene selection for cancer classification using support vector machines[END_REF]f o rg e n es e l e c t i o ni nac a n c e rc l a s s i fi c ation problem was employed for the ranking of features. This method selects the features in a linear separation case, based on the vector of weighted magnitude

||w ψ || 2 defined as w = X i2ζ y i α i x i (4.1)
where, ζ is the number of training sets, ψ the total number of features, y is the class label, x is a feature and α is the corresponding solution of the SVM classifier. The features corresponding to smallest weight magnitudes are recursively eliminated.

Random Forest Variable Importance Score.

Random forests (RF) are machine learning techniques used for the classification or regression and estimation of variable importance based on multiple decision trees [START_REF] Breiman | Random forests[END_REF]. Every node in the decision trees is a condition on a single feature that is designed to split the dataset into two so that similar response values end up in the same set. The measure based on which the optimal condition is chosen is called impurity. For classification, it is typically either Gini impurity or information gain/entropy and for regression trees it is variance. Thus when training a tree, it can be computed how much each feature decreases the weighted impurity in a tree. For a forest, the impurity decrease from each feature can be averaged and the features are ranked according to this measure. Hence, mean

Results

consists of many simple tree classifiers. Each of the classification trees is built using a bootstrap sample of the data, and at each split the candidate set of variables is a random subset of the variables. Thus, random forest uses both bagging (bootstrap aggregation), a successful approach for combining unstable learners, and random variable selection for tree building [START_REF] Friedman | The elements of statistical learning[END_REF]. Each tree is unpruned (grown fully), so as to obtain low-bias trees; at the same time, bagging and random variable selection result in low correlation of the individual trees. Although each classification tree is itself weak, as an ensemble random forests classifiers yield high accuracy. The classifier construction and prediction steps are highly parallelisable, and therefore RFs runs efficiently on large datasets.

The number of trees and mtry parameters are tuned based on grid search. No pre-processing is required for RF as it is scale independent. The algorithm was implemented using the open-source caret package in R [179].

Classification Comparison

In order to exploit the robustness results into feature selection, feature selection techniques and classifiers under comparison were run exploiting either the full set of features (the four groups including the unreliable ones) or only robust features (discarding unreliable ones). Finally, the leave-one-out cross-validation (LOOCV) and out-of-bag (OOB) error from bootstrap with 25 repetition are used to measure the performance of SVM and RF, respectively.

Results

In this section, first the results showing various features in different reliable group are reported, then the results obtained from two different machine learning algorithm (SVM and RF) using all the features irrespective of their reliability and finally, the results using only reliable features and machine learning algorithms are reported. (f) Variability of features calculated on T1 images in the dilated mask compared to the original mask. Features are ranked left to right from highest to lowest repeatability. 2). Note that, these reliability does not tell anything about their relevance to the clinical endpoint (OS) but only their level of dependency on variable segmentation masks. proaches by considering the full set of features (including unreliable ones). We emphasize that the results described henceforth are obtained after selecting, ranking and building the models by combining various number of features from different MR modalities, in total we have compare 3440 machine learning models. The leave-one-out cross-validation (LOOCV) is used to measure the SVM per-

Results

Maximum

Accuracy Results: All Features

Results

formance whereas, are out-of-bag (OOB) error from bootstrap with 25 repetition is use to measure the performance of RF.

Here, only the highest model accuracy obtained without considering the number of features used to build the model is presented. When using only one modality the highest accuracy was obtained using FLAIR modality with an accuracy of 98% (sensitivity 98%, specificity 98%, Fig. 4.5a) using SVM. By comparison, RF led to a lower accuracy of 72% (sensitivity 81%, specificity 65%). When using a combination of two modalities two models obtained the same accuracy. First model using T1C and FLAIR with an accuracy of 100% (sensitivity 100%, specificity 100%, Fig. 4.5b) with SVM. Again by comparison, RF led to a lower accuracy of 75% (sensitivity 81%, specificity 71%). Second model using T2 and FLAIR also obtain the accuracy of 100% (sensitivity 100%, specificity 100%, Fig. 4.5b) with SVM. Again by comparison, RF led to a lower accuracy of 75% (sensitivity 79%, specificity 71%). When using a combination of three modalities the highest accuracy was obtained using T1C, T2 and FLAIR with an accuracy of 99% (sensitivity 98%, specificity 100%, Fig. 4.5c) with SVM. Again by comparison, RF led to a lower accuracy of 76% (sensitivity 73%, specificity 79%). In case of a combination of all four modalities the highest accuracy of 100% (sensitivity 100%, specificity 100%, Fig 4 .5d) accuracy was obtained using SVM. Again by comparison, RF led to a lower accuracy of 74% (sensitivity 73%, specificity 75%).

Results

(c) SVM and RF model accuracies obtained with different combination of features using combination of three MR modalities.

(d) SVM and RF model accuracies obtained with different combination of features using combination of four MR modalities.

Accuracy Results: Robust Features

This section presents the classification results obtained from SVM and RF approaches by considering only the robust features (very reliable, moderately reliable and reliable). These three sets of robust imaging features (section 4.3.1) depending on the corresponding modality and contextual features (Age, Gender, Therapeutic options and Karnofsky) are combined. Then, the models are build by incorporating the various groups of features according to their reliability (Fig. 4.6); group one incorporating only very reliable features, group two incorporating only moderately reliable features, group three incorporating only reliable features, group four incorporating reliable and moderately reliable features, and finally, group five incorporating using all robust features (very reliable, moderately reliable and reliable). Results described henceforth are obtained after selecting, ranking and building the models by combining various number of features. Performance measures is standard leave-one-out cross-validation for SVM and out-of-bag (OOB) error from bootstrap with 25 repetition for RF are shown for comparison. If the cross-validation accuracy is less than 65% we exclude these models from further evaluation.

Here, only the highest model accuracy obtained without considering the number of features used to build the model is presented. Figure 4.6 shows the behaviour of various model based on various combination of the modalities using different groups of reliable features (group 1=very reliable, 2=reliable, 3=moderately reliable, 4=combination of features very reliable & reliable & 5=combination of features very reliable, reliable & moderately reliable). As shows in figure 4.6a-4.6d, when using one modality and features from various group of reliability, different modality were exploited to achieved the highest accuracy in different group. However, the highest accuracy was obtained using FLAIR modality with an accuracy of 96% (sensitivity 98%, specificity 94%, Fig. 4.6d) using SVM. By comparison, RF led to a lower accuracy of 74% (sensitivity 88%, specificity 60%). As shows in figure 4.6f-4.6j,w h e nu s i n gc o m b i n a t i o no ft w om o d a l i t i e sa n df e atures from various group of reliability, different combination of modalities were

Results

exploited to achieved the highest accuracy in different group. However, the highest accuracy was obtained using combinations of T2 and FLAIR modalities with an accuracy of 100% (sensitivity 100%, specificity 100%, Fig. 4.6j) using SVM. By comparison, RF led to a lower accuracy of 74% (sensitivity 79%, specificity 69%). As shows in figure 4.6k-4.6n,w h e nu s i n gc o m b i n a t i o no ft h r e em o d a l i t i e s and features from various group of reliability different combination of three were exploited to achieved the highest accuracy in different group. However, the highest accuracy was obtained using combinations of T1, T1C and FLAIR modalities with an accuracy of 100% (sensitivity 100%, specificity 100%, Fig. 4.6m)u s i n g SVM. By comparison, RF led to a lower accuracy of 72% (sensitivity 71%, specificity 73%). As shows in figure 4.6o,w h e nu s i n gc o m b i n a t i o no ff o u rm o d a l i t i e s and features from various group of reliability, the highest accuracy of 100% (sensitivity 100%, specificity 100%, Fig. 4.6o) using SVM. By comparison, RF led to a lower accuracy of 71% (sensitivity 73%, specificity 69%).

(e) SVM and RF model accuracies obtained with combination of features from various group from various group using T1C and FLAIR modalities.

(f) SVM and RF model accuracies obtained with combination of features from various group using T1C and T1 modalities.

Results

(o) SVM and RF model accuracies obtained with combination of features from various group using all four modalities T1C, T1, T2 and FLAIR.

Discussion

The goal of the present work was to compare the performance of feature selection and classifier techniques in training a model on a learning cohort of 96 patients. Overall performance was assessed here using standard leave-one-out cross-validation for SVM and out-of-bag (OOB) error for RF. Overall, SVM outperformed RF in term of performance when using all sets of features or robust feature. Ongoing work is validating these trained model in an external independent dataset and also comparing various cross-validation method (5-fold, repeated 5-fold, 10 fold,...), which will be presented in future. To this end, we emphasized in the present study on the impact of taking into account the robustness of features into the selection and classification steps. Our results show that the models built using reliable features after discarding unreliable ones still reached satisfactory accuracy. We expect the model built using these robust features to have better performance when tested in an external validation cohort. Limitations of the present work include the validation on external test set, and the simplification of the problem as a binary classification task (survival is not considered as ac o n t i n u o u st i m e -d e p e n d e n tv a r i a b l e ) . O u rr e s u l t ss u g g e s tt h a tg l o b a l ,r e g i o n a l and local textural features quantifying heterogeneity in 4 MR sequences available in routine clinical acquisitions can provide complementary prognostic value. On the Contrary to textural features, the contextual and clinical geometrical features did not contribute significantly to the prognostic models as they were not retained by the feature selection step, or when they were, the overall performance of the model was not substantially affected. Overall, the performance reached by considering only these contextual and/or geometrical features was low (accuracy of 69.7-68% with SVM and 69.7-62% with RF).

Concluding Remarks

Our study confirmed the high prognostic value of textural features extracted from MRI multimodal sequences to predict survival in GBM with two different ma-123

Overview on next chapter

chine learning techniques, although SVM led to higher accuracy than RF (100% vs. 75%, using combination of T1C and FLAIR and, T2 and FLAIR, and (100% vs. 74%)using combination of four modalities). In addition, we identified features with different levels of reliability with varying sensitivity to differences in the segmentation mask. Comparing on images modalities by discarding features identified as unreliable still led to models with high accuracy with two machine learning approaches (100% vs. 72% using combination of two modalities T2 and FLAIR, and (100% vs. 71%)using combination of four modalities). Overall by comparison, SVM obtained a higher accuracy (100% using all features and as well as robust ones) compared to RF (76% and 74%). In future work we plan to test these models in a validation external cohort in order to investigate whether the models built using robust features perform better than these that include all features irrespective of their reliability.

Overview on next chapter

The next chapter is the second to last chapter of the thesis manuscript. The chapter deals with the study conducted on large cohort of 142 patients for the prognostic model building based on MRI-derived radiomics. The previously developed framework is exploited to solve the task. Finally, choosing the best prognostic model and testing with the external test set.

Overview

In this chapter, the prognostic models were built and tested in a larger cohort of 142 patients with GBM. The task uses the exact same framework that was previously developed and optimized on smaller groups of patients. The models were developed including pre-processing steps and reliability of features into account as described in chapter 4.

Materials and Methods

Patients Population and Imaging Data

The cohort consist of patients coming from various centres, the information of patients and images are described henceforth. The MR images of GBM patients (n=75) for this study were acquired from the Cancer Imaging Archive

University). All the patients were histopathologically diagnosed with GBM and treated with radiotherapy and chemotherapy. Another cohort of 47 patients was acquired from the university hospital CHRU Brest-Centre Hospitalier Régional et Universitaire de Brest. Finally, third cohort of 20 patients was acquired from CHU Rennes -Centre Hospitalier Universitaire de Rennes. The patients were histopathologically diagnosed with GBM and no information regarding treatment were available. All baseline MRI sequences consist of:1) T1-weighted postcontrast and 2) FLAIR available for all 142 patients. They were retrospectively collected and analyzed. Note that only three (T1, T1C and FLAIR) image modalities were available for all 122 patients and only two (T1C and FLAIR) image modalities were available for all the 142 patients cohort.

Methods

The experiments were based on the large cohort of 122 and 142 GBM patients using SVM and RF. The problem statement remains the same: classifying patients into long and short term based on median survival. Stratified sampling was performed to divide the cohort into training (80%) and testing (20%). As

Results

Classification Comparison

In order to exploit the robustness results from chapter 4 section 4.3.3 into the feature selection. The classifiers under comparison were run exploiting either the full set of features (the four groups including the unreliable ones) or only robust features (discarding unreliable ones). The leave-one-out cross-validation (LOOCV) for SVM and out-of-bag (OOB) error from bootstrap with 25 repetitions for RF were used to measure the performance in training. Finally, accuracy, sensitivity and specificity were used to measure the performance of the models with external test dataset.

Results

Figure 5.1 presents the results obtained on 122 patients (n=96 training and n=26 testing) cohort with 3 available modalities (T1, T1C and FLAIR). All the models were able to an achieve the accuracy above 60% and most of them with high CONTENTS 130 sensitivity (≥ 70%) in the test dataset. However, only the combination of T1C and FLAIR reached a higher accuracy of 73% (sensitivity 77% and specificity 69%) using RF and 69% (sensitivity 77% and specificity 62%) using SVM.

In the following, we thus present results obtained in a larger cohort of 142 patients using a larger training dataset (80%). The cohort were initially separated into training (60%, n=72) and testing (60%, n=70) and vise-versa. And model were tested on each other. However, the results were similar to previously obtained result. So, the cohort were again separated into training (80%, n=112) and testing (20%, n=30) using stratified sampling. First, we reported the results obtained from the two different machine learning algorithms (SVM and RF) using all the features irrespective of their reliability and in a second part, we present the results using only reliable features are reported. and FLAIR sequences based on SVM and RF algorithms. These models were built exploiting all sets of features, including unreliable features. The models with a training accuracy below 60% was discarded from further evaluation. As shown in figure 5.2, SVM always out performed RF when using one modality (T1C or FLAIR) or when combining two modalities (T1C and FLAIR). The accuracy reported were obtained using LOOCV for SVM and out-of-bag (OOB) error from bootstrap with 25 repetitions for RF in training and using independent test dataset in testing. T1C results: Figure 5.2a shows the influence of number of T1C-based radiomics features on resulting accuracy. The highest accuracy obtained using T1C was 83% (sensitivity 82% and specificity 84%, P-value: 1.254e-12) in training and accuracy of 80% (sensitivity 67% and specificity 93%, P-value: 0.0007) in testing with 25 features 131

Accuracy Results: All Features

Results

by SVM. By comparison, RF obtained an OOB accuracy of 66% (sensitivity 69% and specificity 63%, P-value: 0.000831 ) in training and accuracy of 70% (sensitivity 67% and specificity 73%, P-value: 0.02139 ) in testing with 4 features. When considering slightly lower validation accuracy with less number of features SVM obtained an accuracy of 77% (sensitivity 67% and specificity 87%, P-value: 0.002611) with 7 features. P-value is a one-sided test to see if the accuracy is better than the "no information rate". Figure, 5.3 & 5.4 and, table 5.4 shows the ROC curve and actual features of described optimal model. FLAIR results: Figure 5.2b shows the influence of number of FLAIR-based radiomics features on resulting accuracy. The highest accuracy obtained using FLAIR was 79% (sensitivity 84% and specificity 75%, P-value: 3.844e-10,) in training and accuracy of 80% (sensitivity 87% and specificity 73%, P-value: 0.0007) in testing combining 17 features by SVM. By comparison, RF obtained an OOB accuracy of 71% (sensitivity 75% and specificity 68%, P-value 7.77e-06) in training and accuracy of 70% (sensitivity 67% and specificity 73%, P-value: 0.02139) in testing combining 5 features. When considering slightly lower validation accuracy with less number of features SVM obtained an accuracy of 77% (sensitivity 80% and specificity 73%) combining 7 features. Figure, 5.3 & 5.4 and, table 5.4 shows the ROC curve and actual features of described optimal model. T1C & FLAIR combinations results: Figure 5.2c shows the influence of number of combined T1C & FLAIR-based radiomics features on resulting accuracy. The highest accuracy obtained using combination of T1C and FLAIR was 84% (sensitivity 81% and specificity 86%, Pvalue: 2.585e-13 ) in training and accuracy of 80% (sensitivity 73% and specificity 87%, P-value: 0.0007) in testing combining 18 features by SVM. By comparison, RF obtained an OOB accuracy of 72% (sensitivity 75% and specificity 73%, P-value: 3.095e-06) in training and accuracy of 73% (sensitivity 80% and specificity 67%, P-value: 0.008062) in testing combining 6 features. When considering slightly lower validation accuracy with less number of features SVM obtained an accuracy of 70% (sensitivity 73% and specificity 67%, P-value: 0.02139 ) combining 10 features. able, 3=moderately reliable, 4=combination of features very reliable & reliable and, 5=combination of features very reliable, reliable & moderately reliable). These five sets of robust imaging features (as described in chapter 4 section 4.3.1) depending on the T1C and FLAIR modality were combined with contextual features (Age, Gender). Then, these models were built by incorporating the various groups of features according to their reliability; group one incorporating only very reliable features, group two incorporating only moderately reliable features, group three incorporating only reliable features, group four incorporating reliable and moderately reliable features, and finally, group five incorporating all robust features (very reliable, moderately reliable and reliable). Results described henceforth were obtained after selecting, ranking and building the models by combining various number of features on the independent test set. Like before the models with a training accuracy below 60% was discarded from further evaluation. The accuracy mentioned were obtained using LOOCV in training and using independent test set in testing. T1C results: Figure 5.7a-5.8g shows the influence of number of T1C-based radiomics features on resulting accuracy based on various groups. When using T1C modality and features from various groups of reliability, different level of accuracy (70%-87%) were obtained by different groups. The highest accuracy was obtained by exploiting features from group 5 with an accuracy of 74% (sensitivity 82%, specificity 67%, P-value: 4.317e-07) in training and accuracy of 87% (sensitivity 100%, specificity 73%, P-value: 2.974e-05) by SVM using 7 features. By comparison, RF led to a lower accuracy of 66% (sensitivity 66%, specificity 66%) in training and accuracy of 73% (sensitivity 80%, specificity 67%, P-value: 0.002611) in testing with 4 features from group 5. Figure 5.5 & 5.6 and, table 5.5 shows the ROC curve and actual features of described optimal model. tures on resulting accuracy based on various groups. When using FLAIR modality and features from various groups of reliability, different level of accuracy (70%-83%) were obtained by different groups. The highest accuracy was obtained in FLAIR modality by exploiting features from group 3 with an accuracy of 74% (sensitivity 80%, specificity 68%, P-value: 4.317e-07) in training and accuracy of 83% (sensitivity 87%, specificity 80%, P-value: 0.0001625) by SVM using 13 features. By comparison, RF led to a lower accuracy of 63% (sensitivity 64%, specificity 65%) in training and accuracy of 70% (sensitivity 67%, specificity 73%) in testing with 6 features from group 3. When considering slightly lower validation accuracy with less number of features SVM obtained an accuracy of 77% (sensitivity 80% and specificity 73%, P-value: 0.002611 ) with 6 features from group 5. Figure 5.5 & 5.6 and, table 5.5 shows the ROC curve and actual features of described optimal model. T1C & FLAIR combinations results: Figure 5.8i-5.8m shows the influence of number of combined T1C & FLAIR-based radiomics features on resulting accuracy based on various groups. When using combination of T1C and FLAIR modalities and features from various groups of reliability, different level of accuracy (73%-83%) were obtained by different groups. The highest accuracy was obtained by exploiting features from group 5 with an accuracy of 75% (sensitivity 78%, specificity 72%, P-value: 1.508e-07) in training and accuracy of 83% (sensitivity 93%, specificity 73%, P-value: 0.0001625) by SVM using 6 features. By comparison, RF led to a lower accuracy of 68% (sensitivity 66%, specificity 70%) in training and accuracy of 73% (sensitivity 67%, specificity 80%) in testing with 11 features from group 3. Figure 5.5 & 5.6 and, table 5.5 shows the ROC curve and actual features of described optimal model. We emphasize that the goal here was also to select mo dels that reach a go o d accuracy combining the smallest number of features (usually less than 10). Hence, figure 5.5 & 5.6 and, table 5.5 shows the ROC curve of best chosen models and name of the actual features used to build them.

FLAIR results:

Results

(a) SVM model accuracy obtained with different combinations of features from group 2 using T1C modality only. RF model accuracy were below 60% so, no further evaluation was done.

(b) SVM model accuracy obtained with different combinations of features from group 2 using FLAIR modality only. RF model accuracy were below 60% so, no further evaluation was done.

(c) SVM model accuracy obtained with different combinations of features from group 3 using T1C modality only. RF model accuracy were below 60% so, no further evaluation was done.

(d) SVM and RF model accuracies obtained with different combinations of features from group 3 using FLAIR modality only. [START_REF] Guyon | Gene selection for cancer classification using support vector machines[END_REF]

Results

(i) SVM model accuracy obtained with combinations of features from group 1 using T1C and FLAIR modalities. RF model accuracy were below 60% so, no further evaluation was done.

(j) SVM and RF model accuracies obtained with combinations of features from group using using 2 T1C and FLAIR modalities.

(k) SVM and RF model accuracies obtained with combinations of features from group 3 using T1C and FLAIR modalities.

(l) SVM model accuracy obtained with combinations of features from group 4 using T1C and FLAIR modalities. RF model accuracy were below 60% so, no further evaluation was done.

Discussion

The goal of the present chapter was to compare the performance of feature selection and classifier techniques on large cohort of 142 patients stratified into training set (n=112) and testing set (n=30). The choice of algorithm seemed to be the major factor driving the performance variation: Support vector machine (SVM) always performed better than Random Forest (SVM) in our dataset. Overall, the T1 post-contrast MRI sequence seemed to offer the most relevant information regarding prognosis. The best models combined features from second and third orders, and geometric that were always retained by both classifiers. We also observed that the models built using robust features only perform better than the models built exploiting all features irrespectively of their reliability. We also observed that sometimes cross-validation (LOOCV and OOB) accuracy is lower than testing accuracy. These kind of performance evaluation are generally not presented in literature, so further investigation is needed for these kind of models. In case of random forest there is also another possibility as per the original author Breiman et al., [194]O O Bp r e d i c t i o n sa r eb a s e do nar a t h e rs m a l l subsample of the trees in the forest and are thus at a disadvantage relative to predictions that can be legitimately based on the entire forest. In future work various cross-validation method (5-fold, repeated 5-fold, 10 fold,...) could be used to investigate if similar kind of difference in accuracy exist between cross-validation and testing. In term of model building, the additional two modalities (T1 and T2) were not available. So, these might have been a constrain to achieve even higher accuracy by building models from combining three and four modalities.

Conclusion

Our study confirmed the high prognostic value of textural features extracted from MRI multimodal sequences (T1C and FLAIR) to predict survival in GBM. Two different machine learning techniques were exploited where SVM led to higher accuracy than RF. The validation accuracy using SVM and RF reached upto 83%-77% depending on, the number of features and modality considered, when using all available features without taking into account their reliability. The validation accuracy using SVM and RF increased to 87%-77%, depending on, the number of features and modality considered, when using reliable features only.

Overview on next Chapter

The next chapter concludes the thesis by summarizing the contributions and providing some perspectives.

Like previously the tumor delineation was based on BraTumIA and 3DSlicer.

The GBM tumor segmentation task of CHU Brest cohort was performed by Gurvan Dissaux, radiologist residents.

3. This feature extraction work benefited from the use of radiomics in-house software developed using Insight Segmentation and Registration Toolkit (ITK).

4. The multi-platform configuration tool CMake was used for configuring ITK and facilitating its use from our project. CMake is an open source system and it is freely available at www.cmake.org.

Conclusion

The thesis envisioned, at a very top level, a framework for developing a comprehensive prognostic model for patients with glioblastoma multiforme from multimodal MRI-derived "radiomics" and "machine learning". As shown in Figure Within this framework, chapter 2 introduces the developed model using shape features and texture analysis in 3D, extracted based on first-(intensity histogram), textural second-(co-occurence matrix) and higher-order (grey-level run length and grey-level size zone matrices) statistics from each delineated tumor volume and in each of the four MRI sequences using modern machine learning approach with Support Vector for stratifying patients into long term or short term overall survival. Then, Chapter 3 shows due to the multi centric nature of the cohort, the variability in acquisition protocols and scanner models involved could lead to undesirable variability in the textural features, and a resulting bias in the classification performance. Therefore, this chapter investigates and highlighted the potential benefits and respective impact of several MRI pre-processing steps (spatial resampling of voxels, intensities quantization and normalization) to be performed before textural features computation. And chapter 4 determines whether choice of feature selection and classification methods for building a prognostic model in GBM effect the resulting accuracy of the model. Two benchmark machine learning techniques exploiting different paradigms to perform features selection and classification were evaluated: Support Vector Machines (SVM) and Random Forest. The classifiers under comparison were run exploiting either the full set of radiomics features (the four groups including the unreliable ones) or only robust radiomics features (discarding unreliable ones). Finally, in chapter 5 the developed framework was thoroughly assessed in a larger cohort of 142 GBM patients to explore in more details the training/validation issue. Our study confirmed the high prognostic value of textural features extracted from MRI multimodal sequences (T1C and FLAIR) to predict survival in GBM. The validation accuracy using SVM and RF reached upto 83%-77% depending on, the number of features and modality considered, when using all available features without taking into account their reliability. The validation accuracy using SVM and RF increased to 87%-77%, depending on, the number of features and modality considered, when using reliable features only.

Perspectives

As shown in figure 6.2 the framework we developed during the thesis could be used to build a unified prognostic model exploiting various patients data (imaging, genomics, demographic...). It can also be used to link the radiologic tumor properties (radiomics) to underlying genetic alterations (the field is called radiogenomics). Associating imaging with genomic or finding the complementary value is of great interest and recent trend in radiogenomics community. For example, Ki-67 is the prognostic marker for survival and tumor recurrence, Methylation of the MGMT gene promoter inhibits the repair of therapeutic DNA damage induced by TMZ thus rendering a drug resistant cancer more sensitive to chemotherapy [START_REF] Hau | Mgmt methylation status: the advent of stratified therapy in glioblastoma?[END_REF]. Therefore, a sensitive and specific test that reliably predicts these genes of a given GBM would be a helpful diagnostic alternative to the standard physical biopsy currently employed to diagnose the genes status of glioblastomas.

In case of new algorithm development we look forward to incorporate Ensemble models and the deep learning architecture. Ensemble models in their simplest form are just averaging (regression) or voting (classification) a prediction from multiple models (SVM, RF, Logistic Regression, Linear Discriminate Analysis) or using more advance technique such as Stacking [START_REF] Thomas Navin Lal | Embedded methods[END_REF]. Moreover, the plausible approach will be to use wrapper or/and embedded technique for feature selection with each algorithm and to use Ensemble technique for predictions. Furthermore, in very recent years, biologically-inspired convolutional neural networks (CNN) have shown the ability to learn hierarchically-organised, low-to high-level features from raw images [START_REF] Fukushima | Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position[END_REF][START_REF] Lecun | Gradientbased learning applied to document recognition[END_REF], and yield state-of-the-art performance in the classification of both natural and medical images [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF][START_REF] Dan C Cireşan | Mitosis detection in breast cancer histology images with deep neural networks[END_REF][START_REF] Ciregan | Multi-column deep neural networks for image classification[END_REF]. Deep learning is also particularly promising research direction that could potentially overcome the limitations like feature extraction, pre-processing and segmentation which is prone to variability in radiomics features across centres. This will allow us to jointly benefit from methods that relies upon different definitions of image processing and deploys different predictive models, simultaneously. Nuclei could be considered as small magnetized tops with magnetic dipole µ and angular momentum L and the other that wants to maintain their angular momentum. The net result is that the nuclei spin like tops as shown in the figure 1.1(c). The spins do not align themselves with B 0 irrespective of their initial orientation and spins pointing up and down are absent. Spins will precess at the angle they were on intialization of the magnetic field B 0 .S e v e r a lm a g n e t i cfi e l d s are present that act on the spins. B 0 is constant and generates the precession ω 0 and the other fluctuates depending on the molecular anisotropy and the environment. These make the spins attain all possible orientations with respect to B 0 in a certain amount of time. This is shown in the figure 1.1(d).

Decomposing the magnetic dipole µ vector in xy and z plane we observe that the resultant vector of xy -plane is zero leaving out the resultant in z direction that gives the net magnetization of the magnetic dipole. Figure 1.2(a) shows net magnetization vector and 1.2(b) shows the decomposition of the net magnetization vector.

The system is in equilibrium and a B 1 pulse is applied to the system to perturb the net magnetization field M 0 of the system. An oscillating electromagnetic radiation generated by an alternating current is used to generate the B 1 pulse. A9 0d e g r e ep u l s ei nt h exd i r e c t i o nr o t a t e sM 0 90 degree so that the magnitude of the net magnetization in the transverse plane, pointed in the positive y direction, is equal to the initial z magnetization or M y = M z as shown in figure. Similarly, a 180 degree pulse rotates positive M 0 180 degree to negative M 0 as shown in gure1.2(d). The frequency of the alternating current ω 0 and frequency of the right vector B 1 (ω 0 ) produces a resonant condition. The alternating current, magnetic field and the all magnetic dipoles interact, generate a torque initiating rotation. Transverse magnetization M xy is generated due to rotation of M 0 around the y axis as shown in figure 1.3(a). Current in the coil is generated from the oscillation of M xy that is a fluctuating magnetic field as shown in the figure 1.3(b). In absence of the external magnetic field B 1 , M xy will return to align with the z-axis, the equilibrium position as shown in figure 1.3(c). Longitudnal or T1 relaxation refers to the return to thermal equilibrium of the spin population following pertubation. T1 relaxation represents a loss of energy from the spins to the surroundings. The return to equlibrium after 90 degree pulse is shown in the figure. Transverse or T2 relaxation refers to loss of phase coherence in the transverse xy-plane. This loss of coherence is due to the local magnetic field differences experienced by the nuclei due to magnetic field inhomogeneity and due to the fluctuating magnetic fields produced by the nuclei themselves. T2 relaxation is shown in the figure. The two phenomena T1 and T2 relaxation takes place simultaneously but not at the same time. Signal produced in T1 and T2 relaxation last for only few micro seconds. In order to facilitate the acquisition of nuclear magnetic resonance signal a complex radio frequency pulse is necessary. In order to acquire the nuclear magnetic signal and to reduce the effect of heteregeneous magnetic field a complex radio frequency pulse is used known as the spin echo method. In spin echo method a 90 degree pulse is followed by a 180 degree pulse. The 90 degree pulse is applied along x-axis at t =0then a1 8 0d e g r e ep u l s ei sa p p l i e da l o n gt h ey-axis to invert the phase acquired by the phase. The spins then rephrase producing an echo. Regularly repeated radio frequency pulse of 180 degree rephases the spins generating a decreasing signal .

The longitudinal relaxation or T1 time, the transversal relaxation or T2 time and the proton density are three magnetic parameters of the tissue. The repetition time of the magnetic pulse or TR and the spin echo time TE could be controlled to obtain either T1-or T2-weighted images. The longitudinal and the transversal relaxation time, T1 and T2 are different depending on the tissue. Shorter T1

1 1 CONTENTS 2 1. 4

 1124 Brain and Gliobastoma (GBM) ................... 1.1.1 Structure of Brain ...................... 1.1.2 Gliobastoma Multiforme (GBM) ............... 1.1.3 Census on Incident Rate and Survival ........... 1.1.4 Grading of Brain Tumor ................... 1.1.5 Treatment .......................... 1.1.6 Tumor Response Evaluation Criteria ............ 1.2 State-of-the-Art Multiparametric Imaging of GBM ......... 1.2.1 Magnetic Resonance Imaging (MRI) ............ 1.2.2 Computed Tomography (CT) ................ 1.2.3 Positron Emission Tomography (PET) ........... 1.3 Radiomics: "more than meets the eye" ............... 1.3.1 VASARI ............................ Structure of the Manuscript and Objective/ Contribution ..... 32 1.5 List of Publications and Award ................... 35

Figure 1 . 1 :

 11 Figure 1.1: Lobes of the cerebral cortex

Figure 1 . 2 :Figure 1 . 3 :

 1213 Figure 1.2: Neuron general structure

Figure 1 . 4 :

 14 Figure 1.4: Brain axial section gray matter and white matter. Micrograph showing normal white matter (left of image -lighter shade of pink) and normal grey matter (right of image -dark shade of pink)

Figure 1 . 5 :

 15 Figure 1.5: (Right) Gliobastoma multiforme (Left) Histopathology showing nuclear atypia of GBM

Figure 1 . 6 :

 16 Figure 1.6: Survival of 98 patients with unresectable GBM according to the treatment modalities: (circle) survival of patients without any specific therapy (n =3 6 ) ,( c r o s s )s u r v i v a lo fp a t i e n t sr e c e i v i n gr a d i o t h e r a p y( n=2 4 )a n d( t r i a n g l e ) survival of patients receiving combined RCT (n = 38), p<0.001. Reprinted from [23,F a z e n y -D ö r n e ret al., 2003]

Figure 1 . 7 :

 17 Figure 1.7: Glioblastoma multiforme incident rate. Reprinted from [21,D o l e c e k et al. 2012]

Figure 1 . 8 :

 18 Figure 1.8: Kaplan-Meier survival plots for GBM cases. The x-axes indicate completed months of follow-up. Patients treated with termozolomide (2005-2007) and without (1993-1998). Age group: (A) 20-44 years. (B) 45-64 years. (C) 65-79 years. (D) 80+ years. Reprinted from [25,D a r e f s k yet al., 2012]

  Pre-contrast ImagingT1 weighted pre-contrast (T1) images is a part of all MRI protocol (spin echo [SE], turbo spin echo [TSE], gradient echo,...) and it is the most anatomical of images. T1W MRI refers to spin-lattice relaxation time. The tissue contrast in T1 is formed on the basis of differences in the T1 relaxation times of tissues, where fat has high signal and appears bright, and water has low signal and appears dark. Therefore, in T1 images of normal brain, Cerebrospinal fluid (CSF) has low signal intensity and appears black, grey matter has intermediate signal intensity and appears grey and white matter has hyperintense compared to grey matter and appears whiteish. Highly proliferative active tumors such as glioblastomas CONTENTS 20 are not clearly visible (Fig.1.10)in T1-weighted pre-contrast images.[START_REF] Westbrook | MRI in Practice[END_REF][START_REF] Mark | Mr imaging abbreviations, definitions, and descriptions: A review 1[END_REF][START_REF] Westbrook | MRI at a glance[END_REF] (Fig.1.10).

Figure 1 .

 1 Figure 1.10: Standard MRI of GBM used in clinical practice. Axial contrast enhanced T1-w image displays almost no contrast, enhancement is seen in contrast enhanced T1-w image, it displays irregular peripheral enhancement in tumour

Figure 1 . 11 : 23 1. 2

 111232 Figure 1.11: Axial SWI of GBM used in clinical practice. Case courtesy of Dr Mohammad A. ElBeialy, Radiopaedia.org, rID: 23765.

Figure 1 . 12 :

 112 Figure 1.12: Standard MRS of GBM used in clinical practice. Case courtesy of Dr Mohammad A. ElBeialy, Radiopaedia.org, rID: 23765

Figure 1 . 13 :

 113 Figure 1.13: Diffusion-weighting. (a) Different degrees of diffusion-weighted images obtained using different b values, the signal in structures with fast diffusion (for example, water-filled ventricular cavities) decays rapidly with increasing b, whereas the signal in tissues with low diffusion (for example, grey and white matter) decreases more slowly. (b) Diffusion images (ADC maps)showing high rates of diffusion -as in the ventricular cavities-appear as bright areas, whereas areas with low rates of diffusion are dark, reprinted[START_REF] Le | Looking into the functional architecture of the brain with diffusion mri[END_REF]

Figure 1 .

 1 Figure 1.15: T1 weighted post-contrast MRI showing alteration of the blood-brain barrier and the extent of peritumoral edema. PET imaging using [ 18 F]FDG, [ 11 C]MET, and [ 18 F]FLT as specific tracers for glucose consumption, amino acid transport and DNA synthesis shows signs of increased cell proliferation. Reprinted from [51].

Figure 1 .

 1 Figure 1.14: (a) CT before and (b) after contrast administration. The neoplasm is clearly demonstrated on post-contrast CT showing irregular peripheral enhancement. Reprinted from[START_REF] Drevelegas | Imaging modalities in brain tumors[END_REF]

Figure 2 . 1 :

 21 Figure 2.1: The proposed workflow for GBM prognostic model development and validation.

Figure 2 . 2 :

 22 Figure 2.2: The spatial relationship of neighboring voxels in 3D. (a) 90 • and (b) 45 • .

Figure 2 .

 2 Figure 2.3 shows an example to construct a 2D GLRLM. Condider a 4 × 4 image (Fig. 2.3 .A) with gray level from 1 to 4 (Fig. 2.3B). A generalized GLRLM

.20) 3 .

 3 The work of Dasarathy et al. [153]a l s oa d d e df o u ra d d i t i o n a lf e a t u r e s .

Figure 2 .

 2 Figure 2.4 shows an example to construct a 2D GLSZM. Condider a 4 × 4 image (Fig. 2.4 .A) with gray level from 1 to 4 . A generalized GLSZM is shown in (Fig. 2.4.B) where entry of GLSZM matrix is filled whenever the number of times zones j is present representing intensity i. Likewise, a single GLSZM matrix is calculated using all the 13 directions in this work.

Figure 2 . 4 :

 24 Figure 2.4: The spatial relationship of neighboring voxels in 3D (0 • ).

Figure 2 .

 2 Figure 2.5: A simplified illustration of how an SVM might work in distinguishing between poor prognosis and excellent prognosis using tumor volume/KPS support vectors. In this simple case the SVM has identified a hyperplane (actually a line) which maximizes the separation between the two classes.

Figure 2 . 6 :

 26 Figure 2.6: LOOCV classification accuracy obtained by the model when using various bin values and modality.

Figure 2 . 7 :

 27 Figure 2.7: Model accuracy, specificity and sensitivity assessed by LOOCV. Left: using 1 modality only, right: all combinations of 2 modalities.

Figure 2 . 8 :

 28 Figure 2.8: Kaplan-Meier survival curves for patients according to left: classification using the median survival and right: classification obtained by the model. Misclassified patients are indicated by black circles.

Figure 3 . 1 :

 31 Figure 3.1: Kaplan-Meier survival curves for patients according to left: classification using the median survival and right: classification obtained by the model. Misclassified patients are indicated by both black and green circles, whereas correctly classified patients into their respective group after added pre-processing steps are represent by only green circle.

Figure 4 .

 4 Figure 4.1 presents the impact of the different pre-processing steps on the resulting accuracy of our workflow to identify patients with overall survival above or below the median survival of 12 months.Figure 3.1 presents the Kaplan-Meier

Figure 3 .Figure 3 . 2 :

 332 Figure 4.1 presents the impact of the different pre-processing steps on the resulting accuracy of our workflow to identify patients with overall survival above or below the median survival of 12 months.Figure 3.1 presents the Kaplan-Meier

Figure 3 . 3 :

 33 Figure 3.3: Mean and standard deviation of accuracies for models assessed by LOOCV by expanding or shrinking tumor contours by 20%.

Figure 4 . 1 :

 41 Figure 4.1: The proposed workflow for GBM prognostic model development and validation.

95 4. 2

 952 Figure 4.3: Digital phantom. Voxels outside of the ROI are greyed out. (reprinted after permission from author)

  (a) MR modalities. (b) Original mask (center), Eroded mask (white) and Dilated mask (light grey)

103 4. 3

 1033 Results(c) Variability of Geometric features in the eroded mask compared to the original mask. Features are ranked left to right from highest to lowest repeatability.(d) Variability of Geometric features in the dilated mask compared to the original mask. Features are ranked left to right from highest to lowest repeatability.

Figure 4 . 4 :

 44 Figure 4.4: Robust features in various modality as assessed by Bland-Altman analysis.

Fig. 4 .

 4 Figure 4.4 and table 4.2 presents the results of robustness analysis using Bland-Altman for all four modalites based on the features in table 4.1. Among all 76 features (62 statical features + 14 geometric features), When considering only one modality T1 pre-contrast 51 features (Fig. 4.4e, 4.4f &T a b . 4.2)w e r ef o u n d to be reliable rest 11 features were grouped as unreliable. Regarding T1C 52 features (Fig. 4.4g, 4.4h &T a b . 4.2)w e r ef o u n dt ob er o b u s tr e s t1 0f e a t u r e s were grouped as unreliable. Likewise, for T2 modality 56 features (Fig. 4.4i, 4.4j &T a b . 4.2)w e r er e l i a b l ea n dr e s t6f e a t u r e sw e r eg r o u p e da su n r e l i a b l ea n di n FLAIR 53 features (Fig. 4.4k, 4.4l &T a b . 4.2)w e r ef o u n dt ob er e l i a b l er e s t9 features were grouped as unreliable. All the geometric features were categorize as reliable (Fig. 4.4c, 4.4d &T a b . 4.2). Note that, these reliability does not tell anything about their relevance to the clinical endpoint (OS) but only their level of dependency on variable segmentation masks.

Figure 4 .

 4 Figure 4.5 presents the classification results obtained from SVM and RF approaches by considering the full set of features (including unreliable ones). We emphasize that the results described henceforth are obtained after selecting, ranking and building the models by combining various number of features from different MR modalities, in total we have compare 3440 machine learning models. The leave-one-out cross-validation (LOOCV) is used to measure the SVM per-

Figure 4 . 5 :

 45 Figure 4.5: SVM and RF model accuracies obtained with different combination of features using single and combination of various MR modality as assessed by LOOCV.

121

 121

Figure 4 . 6 :

 46 Figure 4.6: SVM and RF model accuracies obtained with different combination of features from various group using single and combination of various MR modality as assessed by LOOCV.

Figure 5 . 1 :

 51 Figure 5.1: SVM and RF model accuracies obtained with testing (n=26) using single and combinations of various MR modalities.

Figure 5 .

 5 Figure 5.2 shows training and testing accuracy curves and associated features used to build the prognostic models from T1C, FLAIR and combinations of T1C and FLAIR sequences based on SVM and RF algorithms. These models were built exploiting all sets of features, including unreliable features. The models with a training accuracy below 60% was discarded from further evaluation. As shown in figure5.2, SVM always out performed RF when using one modality (T1C or FLAIR) or when combining two modalities (T1C and FLAIR). The accuracy reported were obtained using LOOCV for SVM and out-of-bag (OOB) error from bootstrap with 25 repetitions for RF in training and using independent test dataset in testing.

  Figure, 5.3 & 5.4 and, table5.4 shows the ROC curve and actual features of described optimal model.We emphasize the goal is to select mo dels that reach a go o d accuracy combining the smallest number of features (usually less than 10). Hence, figure 5.3 & 5.4 and, table 5.4 also shows the ROC curve of best chosen models and name of the actual features used to build them.

Figure 5 . 3 :Figure 5 . 4 :

 5354 Figure 5.3: ROC of test set (n=30) as obtained by SVM based prognostic models using T1C, FLAIR and combinations of T1C and FLAIR

Figure 5 .

 5 Figure 5.8 presents the classification results obtained from SVM and RF approaches by considering only the robust features (group 1=very reliable, 2=reli-

Figure 5 . 2 :

 52 Figure 5.2: SVM and RF model accuracies obtained with different combinations of features using single and combinations of two MR modalities.

Figure 5 . 137 5. 3

 51373 7b-5.8h shows the influence of number of FLAIR-based radiomics fea-Results

141

 141

147 5. 3

 1473 Results(m) SVM and RF model accuracies obtained with combination of features from various group 5 using T1C and FLAIR modalities.

Figure 5 . 8 :

 58 Figure 5.8: SVM and RF model accuracies obtained with different combinations of features from various group using single or combinations of two MR modalities.

Figure 6 . 1 :

 61 Figure 6.1: The proposed Framework for GBM prognostic model development and validation.

Figure 6 . 2 :

 62 Figure 6.2: The proposed research methodologies for GBM prognostic model development and validation.

Figure 6 . 3 :

 63 Figure 6.3: The proposed methodological workflow for ensemble algorithm development.

  Figure A.1 given below shows the orientation.

Figure A. 1 :

 1 Figure A.1: Precessiona and Spinning Hydrogen Nuclei

Figure A. 2 :

 2 Figure A.2: Net Magnetization and Magnetic Pertubation

Figure A. 3 :

 3 Figure A.3: NMR Signal Detection and Return to Equilibrium

Figure A. 4 : 165 LIST OF FIGURES 1 . 7

 416517 Figure A.4: T1 and T2 Relaxation

Table 1 .

 1

	1.2 State-of-the-Art Multiparametric Imaging of GBM
	1.2 State-of-the-Art Multiparametric Imaging of
	GBM
	Nowadays, clinical management of brain tumor patients is highly dependent on
	modern neuroimaging technique. Multiparametric technique such as Magnetic
	Resonance Imaging (MRI), Computed Tomography (CT), Diffusion Weighted
	Imaging (DWI) techniques, MR Spectroscopy (MRS), Perfusion Imaging and
	Positron Emission Tomography (PET) allow a much deeper and noninvasive in-
	sight into interpretation of brain lesions, resulting in greater

2: Resp onse assessment criteria in the first-line treatment of glioblastoma. Reprinted [29,C h i n o tet al., 2013]

Table 1 .

 1 3: Imaging metho ds and their ma jor utility in brain tumor Imaging[START_REF] Marc C Mabray | Modern brain tumor imaging[END_REF].

	Imaging technique	Major utility in brain tumor imaging
	CT	Mass effect, herniation, hydrocephalus,
		haemorrhage, calcifications
	Pre-and post-contrast T1	Enhancement characteristics, necrosis,
		extent of the enhancing portion of the
		tumor
	T2/FLAIR	Peri-tumoral edema (vasogenic and
		infiltrative), non-enhancing tumor
	T2* susceptibility sequence (SWI) Blood products, calcifications, radiation
		induced chronic micro-haemorrhages
	DWI/ADC	Reduced in highly cellular portion of
		tumor, post-operative injury
	DTI	Tractography for surgical planning/
		navigation
	Perfusion(generally DSC)	Tumor/tissue vascularity
	MR spectroscopy	Metabolic profile
	fMRI	Pre-operative functional mapping,
		research into treatment effects
	PET	functional volume and distribution

patients, Molina et al. LOOCV on 79 patients , Mazurowski et

	al. [67]u s eo n l ys h a p ef e a t u r ew i t hL O O C Vo n6 8p a t i e n t s . O t h e rs t u d i e sl i k e
	Mcgarry et al. [68] and Cui et al.

  Overview on next chapter ...................... 66

	1.5 List of Publications and Award CONTENTS
	1.5 List of Publications and Award 2.3.3 Tumor Delineation ...................... 44
	2.3.4 Intensity Standardization within Delineated Lesions .... 44 Journal Articles 2.4 Feature Extraction .......................... 45
	• [126] Taman Upadhaya, Yannick Morvan, Eric Stindel, Le Reste, Math-2.4.1 Textural Features ....................... 48
	ieu Hatt. A framework for multimodal imaging-based prognostic model build-2.5 Machine Learning ........................... 58
	ing: Preliminary study on multimodal MRI in glioblastoma multiforme. IRBM, 2.5.1 Supervised Learning: Support Vector Machine(SVM) ... 59 36(6):345350, 2015. 2.5.2 Feature Selection ....................... 61
	Selective Peer-Reviewed International Conference Papers 2.5.3 Features Ranking ...................... 62
	2.5.4 Classification for Prognosis .................. 62 • [65] Taman Upadhaya, Yannick Morvan, Eric Stindel, Le Reste, Mathieu Hatt. 2.6 Results ................................. 63 Prognostic value of multimodal MRI tumor features in glioblastoma multiforme 2.7 Discussion-Conclusion ........................ 65 using textural features analysis. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, pages 5054. IEEE, 2015. 2.8
	• [125] Taman Upadhaya, Yannick Morvan, Eric Stindel, Le Reste, Mathieu
	Hatt. Prognosis classification in glioblastoma multiforme using multimodal mri
	derived heterogeneity textural features: impact of pre-processing choices. In
	SPIE Medical Imaging, 2016.
	• [128] Taman Upadhaya, Yannick Morvan, Eric Stindel, Le Reste, Mathieu
	Hatt, et al. Multimodal mri radiomics in gbm: a comparative investigation of
	feature selection and classification techniques for prognostic models including ro-
	bustness assessment. In IEEE Nuclear Science Symposium and Medical Imaging
	Conference (NSS/MIC), 2016.
	Award
	• "France Life Imaging (FLI) international publication grant " for the conference
	SPIE Medical Imaging 2016, California, USA for the paper: "Prognosis classifi-
	cation in glioblastoma multiforme using multimodal MRI derived heterogeneity
	textural features: impact of pre-processing choices." [125]
	• IEEE Nuclear Science Symposium and Medical Imaging Conference 2016, student
	team-publication grant.

Table 2 .

 2

		1: Cohort information
	Characteristics Type	patients(%), N = 40
		short term	20 (50)
	Global survival long term	20 (50)
		median (days)	365
	Gender	male	26 (65)
		female	14 (35)
	Age (years)	interval	31 -80
		60 ± 12
	KPS	interval	60 -100
		median	80
	Treatments	resection + radiotherapy + chemotherapy 40 (100)
	MRI sequences T1,T1C,T2,FLAIR	58 (100)

were acquired from the Cancer Imaging Archive 1 ,animagingportalconsistingof images from four centers (Henry Ford Hospital, University of California San Francisco, M.D. Anderson Cancer Center, and Emory University). The MR images are anonymized and publicly available. Forty patients with histopathologically diagnosed GBM and treated with radiotherapy and chemotherapy were retrospectively analyzed. Mean age of patients was 59y (median 60y, range 31-80y). There were 25 males and 15 females with median survival of 14.8m o n t h s( m e a n 15.5, range 2.8-51.2). All baseline MRI sequences consist of:1) T1-weighted pre contrast, 2) T1-weighted post contrast, 3) T2-weighted, and 4) FLAIR available for all 40 patients.

Table 2 .

 2 2: 34 textural features were computed ; 6 local scale, 21 regional scale, 7 global scale quantifying textural properties of delineated tumor volumes.

  Introduction .............................. 3.2 Materials and Methods ........................ 3.2.1 Patient Cohort and Imaging Data ............. 3.2.2 Framework for Radiomics Analysis ............. 3.3 Pre-processing Schemes ........................ Isotropic Voxels ........................ 75 3.3.2 Normalization ......................... 76 3.3.3 Quantization ......................... 76 3.4 Results and Discussion ........................ 77 3.5 Conclusion ............................... 85 3.6 Overview on next chapter ...................... 85

	69	CONTENTS
	3.3.1
	Glioblastoma Multiforme using
	Multimodal MRI Derived
	Heterogeneity Textural Features:
	Impact of Pre-processing Choices
	Contents
	3.1 68

Table 3 .

 3

	1: List of textural features (6 lo cal, 21 regional, 7 global) considered in
	this work.

  of this article were obtained from the Cancer Imaging Archive (www.cancerimagingarchive.net), an imaging portal consisting anonymized and publicly available images.

3. The tumor delineation work benefied from the use of BraTumIA, for Brain Tumor Image Analysis, is a software dedicated to multimodal image analysis of brain tumor studies. publicly available software free for non-commercial use only available at www.nitrc.org/projects/bratumia/ 4. This pre-processing work benefited from the use of 3DSlicer, is a free, open source software package for visualization and image analysis. 3D Slicer is natively designed to be available on multiple platforms, including Windows, Linux and Mac Os X, available at https://www.slicer.org/ 5. This feature extraction work benefited from the use of the Insight Segmentation and Registration Toolkit (ITK), an open source software and freely available at www.itk.org.

Table 4 .

 4 1: Standardised 14 geometric features and textural features; 16 lo cal, 33 regional, 13 global scale.

  Variability of features calculated on T1 images in the eroded mask compared to the original mask. Features are ranked left to right from highest to lowest repeatability.

	100
	90
	80
	Difference in %
	Maximum	3DSingle_ShortRunEmphasis	3DAVG_ShortRunEmphasis	3DSingle_Joint_entropy	Entropy	Percentile_90	Mean	Median	3DSingle_Small_Zone_Emphasis	Percentile_10	3DSingle_LongRunEmphasis	3DAVG_LongRunEmphasis	3DSingle_Inverse_difference_moment	3DAVG_Inverse_difference_moment	3DAVG_HighGreyLevelRunEmphasis	3DSingle_HighGreyLevelRunEmphasis	3DAVG_ShortRunHighGreyLevelEmphasis	3DSingle_ShortRunHighGreyLevelEmphasis	Uniformity	Interquantile_range	3DSingle_High_Gray_level_Zone_Emphasis	3DSingle_Small_Zone_High_Gray_level_Emphasis	3DSingle_LongRunHighGreyLevelEmphasis	3DAVG_LongRunHighGreyLevelEmphasis	3DAVG_angular_second_moment_or_Energy	3DAVG_Joint_entropy	3Single_angular_second_moment_or_Energy	3DAVG_Correlation	3Single_Correlation	3DSingle_Zone_Percentage_Iso_size_equality	3DSingle_Low_Gray_level_Zone_Emphasis	Variance	3DSingle_Contrast_or_Inertia	3DAVG_Contrast_or_Inertia	3DAVG_HaralickCorrelation	3DSingle_HaralickCorrelation	3DSingle_Small_Zone_Low_Gray_level_Emphasis	3DSingle_Homogeneity_or_Cluster_prominence	3DAVG_Homogeneity_or_Cluster_prominence	3DSingle_Gray_Level_Non_Uniform_Spectral_homogeneity	3DAVG_GreyLevelNonuniformity	3DSingle_GreyLevelNonuniformity	Energy
																					Feature names
	10 20 30 70 80 90 100 (e) 0 40 50 60 Difference in %
	Maximum	3DAVG_ShortRunEmphasis	3DSingle_ShortRunEmphasis	Entropy	3DSingle_Joint_entropy	3DSingle_Small_Zone_Emphasis	Percentile_90	3DSingle_LongRunEmphasis	3DAVG_LongRunEmphasis	Uniformity	Median	Mean	Percentile_10	Interquantile_range	3DAVG_HighGreyLevelRunEmphasis		3DSingle_HighGreyLevelRunEmphasis	3DAVG_ShortRunHighGreyLevelEmphasis	3DSingle_ShortRunHighGreyLevelEmphasis	3DAVG_angular_second_moment_or_Energy	3DAVG_Joint_entropy	3Single_angular_second_moment_or_Energy	3DSingle_Zone_Percentage_Iso_size_equality	3DSingle_Gray_Level_Non_Uniform_Spectral_homogeneity	Variance	3DSingle_LongRunHighGreyLevelEmphasis	3DAVG_LongRunHighGreyLevelEmphasis	3DSingle_Small_Zone_High_Gray_level_Emphasis	3DSingle_High_Gray_level_Zone_Emphasis	3DSingle_Inverse_difference_moment	3DAVG_Inverse_difference_moment	Energy	3DAVG_HaralickCorrelation	3DSingle_HaralickCorrelation	3DSingle_Size_Zone_Non_Uniform_SZNU_Uniformity		3DAVG_TotalNumberofRun	3DSingle_TotalNumberofRun	3DAVG_GreyLevelNonuniformity	3DSingle_GreyLevelNonuniformity	3DSingle_RunLengthNonuniformity	3DAVG_RunLengthNonuniformity	3DSingle_Low_Gray_level_Zone_Emphasis
																					Feature names

  Variability of features calculated on T1C images in the eroded mask compared to the original mask. Features are ranked left to right from highest to lowest repeatability. Variability of features calculated on T2 images in the eroded mask compared to the original mask. Features are ranked left to right from highest to lowest repeatability. Variability of features calculated on T2 images in the dilated mask compared to the original mask. Features are ranked left to right from highest to lowest repeatability. Variability of features calculated on FLAIR images in the eroded mask compared to the original mask. Features are ranked left to right from highest to lowest repeatability. Variability of features calculated on FLAIR images in the dilated mask compared to the original mask. Features are ranked left to right from highest to lowest repeatability.

	3DSingle_ShortRunEmphasis 3DAVG_ShortRunEmphasis Mean Percentile_90 3DSingle_ShortRunEmphasis 3DSingle_ShortRunEmphasis (j) 4.3 Results 3DAVG_ShortRunEmphasis Entropy 3DSingle_Joint_entropy 3DSingle_Small_Zone_Emphasis Percentile_90 Mean Median 3DSingle_Inverse_difference_moment 3DAVG_Inverse_difference_moment 3DSingle_High_Gray_level_Zone_Emphasis 3DSingle_Small_Zone_High_Gray_level_Emphasis 3DSingle_LongRunEmphasis 3DAVG_LongRunEmphasis Uniformity 3DSingle_Zone_Percentage_Iso_size_equality Interquantile_range Percentile_10 3DAVG_HighGreyLevelRunEmphasis 3DSingle_HighGreyLevelRunEmphasis 3DAVG_ShortRunHighGreyLevelEmphasis 3DSingle_ShortRunHighGreyLevelEmphasis 3DSingle_LongRunHighGreyLevelEmphasis 3DAVG_LongRunHighGreyLevelEmphasis 3DAVG_Contrast_or_Inertia 3DSingle_Contrast_or_Inertia Variance 3DAVG_Correlation 3Single_Correlation 3DSingle_Low_Gray_level_Zone_Emphasis 3DSingle_Small_Zone_Low_Gray_level_Emphasis 3DAVG_angular_second_moment_or_Energy 3DAVG_Joint_entropy 3Single_angular_second_moment_or_Energy 3DAVG_HaralickCorrelation 3DSingle_HaralickCorrelation 3DAVG_Homogeneity_or_Cluster_prominence 3DSingle_Homogeneity_or_Cluster_prominence 3DSingle_Large_Zone_High_Gray_level_Emphasis 3DSingle_Large_Zone_Emphasis 3DSingle_Gray_Level_Non_Uniform_Spectral_homogeneity Energy Feature names Difference in % (g) Maximum 3DSingle_ShortRunEmphasis Entropy 3DSingle_Small_Zone_Emphasis 3DSingle_Joint_entropy Percentile_90 3DSingle_LongRunEmphasis 3DAVG_LongRunEmphasis Interquantile_range Mean 3DSingle_High_Gray_level_Zone_Emphasis Uniformity Percentile_10 3DSingle_Small_Zone_High_Gray_level_Emphasis Median 3DSingle_Gray_Level_Non_Uniform_Spectral_homogeneity Variance 3DSingle_Size_Zone_Non_Uniform_SZNU_Uniformity 3DSingle_HighGreyLevelRunEmphasis 3DAVG_HighGreyLevelRunEmphasis 3DAVG_ShortRunHighGreyLevelEmphasis 3DSingle_ShortRunHighGreyLevelEmphasis 3DSingle_Inverse_difference_moment 3DAVG_Inverse_difference_moment 3DSingle_LongRunHighGreyLevelEmphasis 3DAVG_LongRunHighGreyLevelEmphasis 3DSingle_Zone_Percentage_Iso_size_equality 3DAVG_angular_second_moment_or_Energy 3DAVG_Joint_entropy 3Single_angular_second_moment_or_Energy 3DAVG_HaralickCorrelation 3DSingle_HaralickCorrelation Energy 3DSingle_Small_Zone_Low_Gray_level_Emphasis 3DAVG_Correlation 3Single_Correlation 3DSingle_RunLengthNonuniformity 3DAVG_RunLengthNonuniformity 3DAVG_TotalNumberofRun 3DSingle_TotalNumberofRun 3DSingle_Contrast_or_Inertia 3DAVG_Contrast_or_Inertia 3DSingle_GreyLevelNonuniformity 3DAVG_GreyLevelNonuniformity Feature names Difference in % CONTENTS 106 0 10 20 30 40 50 60 70 80 90 100 Maximum 3DSingle_ShortRunEmphasis 3DAVG_ShortRunEmphasis 3DSingle_Joint_entropy Entropy Percentile_90 3DSingle_Small_Zone_Emphasis 3DAVG_ShortRunHighGreyLevelEmphasis 3DSingle_ShortRunHighGreyLevelEmphasis Median 3DAVG_HighGreyLevelRunEmphasis 3DSingle_HighGreyLevelRunEmphasis 3DSingle_Inverse_difference_moment 3DAVG_Inverse_difference_moment 3DSingle_LongRunEmphasis 3DAVG_LongRunEmphasis 3DSingle_Zone_Percentage_Iso_size_equality Percentile_10 Uniformity 3DSingle_LongRunHighGreyLevelEmphasis 3DSingle_Small_Zone_High_Gray_level_Emphasis 3DAVG_LongRunHighGreyLevelEmphasis 3DSingle_High_Gray_level_Zone_Emphasis Interquantile_range 3Single_Correlation 3DAVG_Correlation 3DSingle_Contrast_or_Inertia 3DAVG_Contrast_or_Inertia 3DAVG_angular_second_moment_or_Energy 3DAVG_Joint_entropy 3Single_angular_second_moment_or_Energy Variance 3DSingle_Homogeneity_or_Cluster_prominence 3DAVG_Homogeneity_or_Cluster_prominence 3DAVG_HaralickCorrelation 3DSingle_HaralickCorrelation 3DSingle_Low_Gray_level_Zone_Emphasis 3DSingle_Small_Zone_Low_Gray_level_Emphasis 3DSingle_Gray_Level_Non_Uniform_Spectral_homogeneity Energy 3DSingle_Large_Zone_High_Gray_level_Emphasis 3DAVG_ShortRunLowGreyLevelEmphasis 3DSingle_ShortRunLowGreyLevelEmphasis 3DAVG_GreyLevelNonuniformity 3DSingle_GreyLevelNonuniformity 3DAVG_LowGreyLevelRunEmphasis 3DSingle_LowGreyLevelRunEmphasis Minimum 3DAVG_LongRunLowGreyLevelEmphasis 3DSingle_LongRunLowGreyLevelEmphasis 3DSingle_Large_Zone_Emphasis Feature names Difference in % (i) 0 10 20 30 40 50 60 70 80 90 100 Maximum 3DAVG_ShortRunEmphasis 3DSingle_ShortRunEmphasis 3DSingle_Joint_entropy 3DSingle_Small_Zone_Emphasis Entropy 3DSingle_LongRunEmphasis 3DAVG_LongRunEmphasis Median Mean Interquantile_range Uniformity 3DAVG_HighGreyLevelRunEmphasis 3DSingle_HighGreyLevelRunEmphasis 3DAVG_ShortRunHighGreyLevelEmphasis 3DSingle_ShortRunHighGreyLevelEmphasis 3DAVG_LongRunHighGreyLevelEmphasis 3DSingle_LongRunHighGreyLevelEmphasis 3DSingle_Zone_Percentage_Iso_size_equality 3DSingle_Inverse_difference_moment 3DAVG_Inverse_difference_moment Percentile_10 3DSingle_Small_Zone_High_Gray_level_Emphasis 3DAVG_angular_second_moment_or_Energy 3DAVG_Joint_entropy 3DSingle_High_Gray_level_Zone_Emphasis 3Single_angular_second_moment_or_Energy 3DSingle_Gray_Level_Non_Uniform_Spectral_homogeneity Variance Energy 3DAVG_HaralickCorrelation 3DSingle_HaralickCorrelation 3DSingle_GreyLevelNonuniformity 3DAVG_GreyLevelNonuniformity 3Single_Correlation 3DSingle_Size_Zone_Non_Uniform_SZNU_Uniformity 3DAVG_Correlation 3DSingle_Large_Zone_High_Gray_level_Emphasis 3DAVG_TotalNumberofRun 3DSingle_TotalNumberofRun 3DAVG_RunLengthNonuniformity 3DSingle_RunLengthNonuniformity 3DSingle_Contrast_or_Inertia 3DAVG_Contrast_or_Inertia 3DSingle_Small_Zone_Low_Gray_level_Emphasis Feature names Difference in % 0 10 20 30 40 50 60 70 80 90 100 Maximum 3DAVG_ShortRunEmphasis 3DSingle_Joint_entropy Percentile_90 Entropy 3DSingle_Small_Zone_Emphasis Mean Median 3DSingle_LongRunEmphasis 3DAVG_LongRunEmphasis 3DSingle_Inverse_difference_moment 3DAVG_Inverse_difference_moment 3DAVG_HighGreyLevelRunEmphasis 3DSingle_HighGreyLevelRunEmphasis 3DSingle_ShortRunHighGreyLevelEmphasis 3DAVG_ShortRunHighGreyLevelEmphasis 3DAVG_LongRunHighGreyLevelEmphasis 3DSingle_LongRunHighGreyLevelEmphasis Percentile_10 Uniformity Interquantile_range 3DSingle_Zone_Percentage_Iso_size_equality 3DAVG_Correlation 3Single_Correlation 3DSingle_Small_Zone_High_Gray_level_Emphasis 3DSingle_High_Gray_level_Zone_Emphasis 3DSingle_Contrast_or_Inertia 3DAVG_Contrast_or_Inertia 3Single_angular_second_moment_or_Energy 3DAVG_angular_second_moment_or_Energy 3DAVG_Joint_entropy Variance 3DSingle_Homogeneity_or_Cluster_prominence 3DAVG_Homogeneity_or_Cluster_prominence 3DSingle_Low_Gray_level_Zone_Emphasis 3DAVG_HaralickCorrelation 3DSingle_HaralickCorrelation 3DSingle_Small_Zone_Low_Gray_level_Emphasis Minimum 3DAVG_GreyLevelNonuniformity 3DSingle_GreyLevelNonuniformity 3DSingle_Gray_Level_Non_Uniform_Spectral_homogeneity Energy 3DSingle_Large_Zone_High_Gray_level_Emphasis 3DSingle_Large_Zone_Emphasis 3DSingle_LongRunLowGreyLevelEmphasis 3DAVG_LongRunLowGreyLevelEmphasis Feature names Difference in % (k) 0 10 20 30 40 50 60 70 80 90 100 Maximum 3DAVG_ShortRunEmphasis 3DSingle_Joint_entropy 3DSingle_Small_Zone_Emphasis Entropy Percentile_90 3DSingle_LongRunEmphasis 3DAVG_LongRunEmphasis Median Mean Uniformity Interquantile_range 3DSingle_Zone_Percentage_Iso_size_equality Percentile_10 3DAVG_HighGreyLevelRunEmphasis 3DSingle_HighGreyLevelRunEmphasis 3DSingle_ShortRunHighGreyLevelEmphasis 3DAVG_ShortRunHighGreyLevelEmphasis 3DSingle_Inverse_difference_moment 3DAVG_Inverse_difference_moment 3DAVG_angular_second_moment_or_Energy 3DAVG_Joint_entropy 3Single_angular_second_moment_or_Energy 3DAVG_LongRunHighGreyLevelEmphasis 3DSingle_LongRunHighGreyLevelEmphasis 3DSingle_Gray_Level_Non_Uniform_Spectral_homogeneity 3DSingle_High_Gray_level_Zone_Emphasis 3DSingle_Small_Zone_High_Gray_level_Emphasis Energy Variance 3DSingle_GreyLevelNonuniformity 3DAVG_GreyLevelNonuniformity 3DAVG_HaralickCorrelation 3DSingle_HaralickCorrelation 3Single_Correlation 3DAVG_Correlation 3DSingle_Size_Zone_Non_Uniform_SZNU_Uniformity 3DSingle_Contrast_or_Inertia 3DAVG_Contrast_or_Inertia 3DAVG_TotalNumberofRun 3DSingle_TotalNumberofRun 3DAVG_RunLengthNonuniformity 3DSingle_RunLengthNonuniformity 3DSingle_Large_Zone_High_Gray_level_Emphasis Feature names Difference in % (l)
	(h) Variability of features calculated on T1C images in the dilated mask compared to
	the original mask. Features are ranked left to right from highest to lowest repeatability.

Table 4 .

 4 Figure 4.4 and table4.2 presents the results of robustness analysis using Bland-Altman for all four modalites based on the features in table 4.1. Among all 76 features (62 statical features + 14 geometric features), When considering only one modality T1 pre-contrast 51 features (

	2: Total numb er of reliable features categorized according to MR mo dality
	Modality	Very reliable Reliable Moderately reliable Unreliable Total
						features
	T1-C	11	16	24	11	62
	T1	14	13	25	10	62
	T2	10	17	29	6	62
	FLAIR	11	15	27	9	62
	Geometric 2	7	5	0	14
	4.3.1 Robustness Results

  Materials and Methods ........................ 126 5.2.1 Patients Population and Imaging Data ........... 126 5.2.2 Methods ............................ 128 5.2.3 Classification Comparison .................. 129 5.3 Results ................................. 129 5.3.1 Accuracy Results: All Features ............... 130 5.3.2 Accuracy Results: Robust Features only .......... 132 5.4 Discussion ............................... 148 5.5 Conclusion ............................... 148 5.6 Overview on next Chapter ...................... 149

	CONTENTS	126
	5.2
	3. Like previously the tumor delineation was based on BraTumIA and 3DSlicer. Results on Large Cohort:
	The GBM tumor segmentation task of CHU Brest cohort was performed by Gurvan Dissaux, radiologist residents. Comparison between Support
	4. This pre-processing work benefited from the use of 3DSlicer, is a free, open Vector Machine (SVM) and
	source software package for visualization and image analysis. 3D Slicer is natively designed to be available on multiple platforms, including Windows, Random Forest (RF) for Building
	Linux and Mac Os X, available at https://www.slicer.org/
	5. This feature extraction work benefited from the use of the Insight Segmen-GBM prognostic model based on
	tation and Registration Toolkit (ITK), an open source software and freely available at www.itk.org. Multimodal MRI-derived
	6. The multi-platform configuration tool CMake was used for configuring ITK and facilitating its use from our project. CMake is an open source system Radiomics
	and it is freely available at www.cmake.org.
	7. This machine learning work benefited from the use of the CARET package
	in R. The caret package (short for Classification And REgression Training)
	is a set of functions that attempt to streamline the process for creating predictive models and freely available at Contents
	https://cran.r-project.org/web/packages/caret/index.html
	5.1 Overview ................................ 126
	125

Table 5 .

 5 1: Total 142 patients cohort information. "Note that some clinical data such as treatment modality and KPS score was missing for 20 patients". However we would like to emphasize these parameters were never retained in the models building and are likely with little impact on survival.

	1 ,a n

Table 5 .

 5 2: Training cohort information, 112 (80%) of total 142 patients. "Note that some clinical data such as treatment modality and KPS score was missing for 14 patients". However we would like to emphasize these parameters were never retained in the models building and are likely with little impact on survival.

	Characteristics Value	Patients, N = 112
		short OS (≤ median value)	56
	Global survival long OS (> median value)	56
		Median is 365 days (12 months)
	Gender	male	66
		female	46
	Age	27 -81 years
	KPS	60-100 score	98 (14 missing)
	Treatment	resection + radiotherapy + chemotherapy 98 (14 missing)
	MRI sequences T1C and FLAIR	98 (14 missing)

Table 5 .

 5 5: MRI modalities and reliable radiomics features combined by RF and SVM to build the prognostic model.

		1							1
	Sensitivity	0.5			AUC: 0.836 AUC: 0.836		Sensitivity	0.5			AUC: 0.742 AUC: 0.782
					AUC: 0.742						AUC: 0.640
		0							0
					T1C & FLAIR					T1C & FLAIR
					T1C						T1C
					FLAIR						FLAIR
		0	0.2	0.4	0.6	0.8	1		0	0.2	0.4	0.6	0.8	1
				1 -Specificity						1 -Specificity
	Figure 5.5: ROC of test set (n=30)	Figure 5.6: ROC of test set (n=30)
	as obtained by SVM based prognos-	as obtained by RF based prognostic
	tic models using T1C, FLAIR and	models using T1C, FLAIR and com-
	combinations of T1C and FLAIR us-	binations of T1C and FLAIR using
	ing reliable group				reliable group

  5. This machine learning work benefited from the use of the CARET package in R. The caret package (short for Classification And REgression Training)

	Conclusions and Perspectives

"We can only see a short distance ahead, but we can see plenty there that needs to be done." -Alan Turing, Computing Machinery and Intelligence Contents 6.1 Conclusion ............................... 151 6.2 Perspectives .............................. 153

Table 1.1: WHO Grading of Tumor of CNS. Reprinted from [?]

www.cancerimagingarchive.net

Nicole et el. Multimodal glioblastoma segmentation. Software available at http://istbsoftware.unibe.ch/bratumia/requirement.php.

Chang C-C, Lin C-J. LIBSVM: a library for support vector machines, 2011. Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm/

Nicole et al. Multimodal glioblastoma segmentation. Software available at http://istb-software.unibe.ch/bratumia/requirement.php.

(f) Mean accuracies when using combination of three modalities (g) Accuracies standard deviation when using combination of three modalities

https://wiki.cancerimagingarchive.net/display/Public/Collections

(a) SVM and RF model accuracies obtained with different combination of features when using one modality only.(b) SVM and RF model accuracies obtained with different combination of features using combination of two MR modalities.

(a) SVM and RF model accuracies obtained with different combination of features from various group using T1 modality only.(b) SVM and RF model accuracies obtained with different combination of features from various group using T1C modality only.

(c) SVM and RF model accuracies obtained with different combination of features from various group using T2 modality only.(d) SVM and RF model accuracies obtained with different combination of features from various group using FLAIR modality only.

(g) SVM and RF model accuracies obtained with combination of features from various group using T1C and T2 modalities.(h) SVM and RF model accuracies obtained with combination of features from various group using using T1 and T2 modalities.

(i) SVM and RF model accuracies obtained with combination of features from various group using T1 and FLAIR modalities.(j) SVM and RF model accuracies obtained with combination of features from various group using T2 and FLAIR modalities.

(k) SVM and RF model accuracies obtained with combination of features from various group using T1C, T2 and FLAIR modalities.(l) SVM and RF model accuracies obtained with combination of features from various group using T1C, T2 and T1 modalities.

(m) SVM and RF model accuracies obtained with combination of features from various group using T1, T1C and FLAIR modalities.(n) SVM and RF model accuracies obtained with combination of features from various group using T2, T1 and FLAIR modalities.

(a) SVM and RF model accuracies obtained with different combinations of features when using one modality only. (b) SVM and RF model accuracies obtained with different combinations of features using combination of two MR modalities.

(a) SVM and RF model accuracies obtained with different combinations of features from group 1 using T1C modality only.(b) SVM model accuracy obtained with different combinations of features from group 1 using FLAIR modality only. RF model accuracy were below 60% so, no further evaluation was done.

(e) SVM and RF model accuracies obtained with different combinations of features from group 4 using T1C modality only.(f) SVM model accuracy obtained with different combinations of features from group 4 using FLAIR modality only. RF model accuracy were below 60% so, no further evaluation was done.

Acknowledgments

Acknowledgements 1. Data used in the preparation of this article were obtained from the Cancer Imaging Archive (www.cancerimagingarchive.net), an imaging portal consisting anonymized and publicly available images. 2. This feature extraction work benefited from the use of the Insight Segmentation and Registration Toolkit (ITK), an open source software and freely available at www.itk.org. 3. The multi-platform configuration tool CMake was used for configuring ITK and facilitating its use from our project. CMake is an open source system and it is freely available at www.cmake.org. 4. This machine learning work benefited from the use of the LIBSVM package in R ('e1071'). R is a free software environment for statistical computing and graphics and freely available at www.r-project.org Acknowledgements 1. The authors would like to thank Martin Vallières for his support regarding the choices of pre-processing for MR images. Acknowledgements 1. The authors would like to thank Alex Zwanenburg for his support regarding the "Multicentre Initiative for Standardisation of Radiomics". 2. Data used in the preparation of this article were obtained from the Cancer Imaging Archive (www.cancerimagingarchive.net), an imaging portal consisting anonymized and publicly available images and CHRU Brest: Centre Hospitalier Rgional et Universitaire de Brest. Acknowledgements 1. Data used in the preparation of this article were obtained from the Cancer Imaging Archive (www.cancerimagingarchive.net), an imaging portal consisting anonymized and publicly available images from four centers, also dataset CHRU Brest-Centre Hospitalier Rgional et Universitaire de Brest and CHU Rennes-Centre Hospitalier Universitaire de Rennes for GBM patients.

. I would also like to thank Pierre-Jean Le Reste for his support. I am grateful to Fabrice Meriaudeau and Samuel Valable for accepting to review my PhD thesis, reading it and providing important reports.

This research was funded by b<>com. Not only this funding supported me financially, but created the opportunities to travel for conferences, summer school, and partner universities. My visit to Maastro clinic (Maastricht University, faculty of Health,

Figure 4.2: Authors and affiliations of IBSI. Name First name Affiliation (primary) Affiliation 2 Affiliation 3 Leijenaar Ralph T.H.

decrease impurity is used to calculate the importance score for a variable as:

where, ntree is number of trees, Sx i is the set of nodes split by X i (i ∈ 1, ...,total number features) and Gain R (X i ,v)r e p r e s e n t st h ef e a t u r ew i t hm a x i m u mg a i n .

Model Building

As an integral part of the above variable selection process, two different classifiers were evaluated. They were chosen based on their popularity in the literature and because they exploit different paradigms to perform the classification task.

Support Vector Machine (SVM).

SVM is also called maximum margin classifier as it works by separating the classes through an optimal hyperplane margin that is as wide as possible. The "support vectors" are the points supporting the hyperplane. For prediction, new samples are then projected into the same space and assigned to a class based on which side of the margin they fall. In this study, the choice of a linear kernel was made because its paradigm also facilitates feature selection and ranking. For the model building the features are ranked (section 4.2.5)a n da d d e di nas t e p -w i s e manner until the highest classification accuracy is reached, for more details refer to chapter 2.P r e -p r o c e s s i n gl i k er e m o v i n gz e r o -v a r i a n c ef e a t u r e sa n d ,c e n t r i n g (mean zero and standard deviation one) and scaling (interval between zero and one) were done before feeding features into model as SVM is scale depended. The algorithm was implemented using the open-source caret package in R [179].

Random Forest.

Random forest is an algorithm for classification and regression developed by Leo Breiman [START_REF] Breiman | Random forests[END_REF]t h a tu s e sa ne n s e m b l em e t h o df o rc l a s s i fi c a t i o n . T h ec l a s s i fi e r The human body is mainly made up of fat and water. Both fat and water have hydrogen atoms resulting in body composition of around 63% of hydrogen atoms [START_REF] Ross | Magnetic resonance imaging in human body composition research: from quantitative to qualitative tissue measurement[END_REF]. Hydrogen nuclei have high affinity for alignment with magnetic field. For this reason, magnetic Resonance Imaging primarily images the Nuclear Magnetic Resonance from the hydrogen nuclei. MRI is based in the absorption and emission of energy in the radio frequency range of the electromagnetic spectrum.

Results

In Magnetic Resonance Imaging body tissues are exposed to magnetic field varying between 1 to 3 Tesla. All body tissues contain water and hence hydrogen nuclei [START_REF] Christopher C Rowe | Amyloid imaging results from the australian imaging, biomarkers and lifestyle (aibl) study of aging[END_REF]. Hydrogen nuclei in the tissues resonate and emit radiofrequency energy as they try to reorient themselves to steady states. The MRI machine detects this emitted energy and converts it to an image where the intensity of grey level is proportional to energy emitted by hydrogen nuclei. In absence of external magnetic field the distribution of resulting magnetic moment of an ensemble of hydrogen nuclei is isotropic. Due to angular momentum of the nuclear spin, in a static magnetic field, nuclear magnetic moment precess about the applied field. The nuclear spin in order to minimize energy, precess the applied magnetic field, which is assumed to be in the positive z direction. A bar magnet in a magnetic List of Tables

Multimodal Radiomics in Neuro-oncology

Abstract : Glioblastoma multiforme (GBM) is a WHO grade IV tumor that represents 49% of ail brain tumours. Despite aggressive treatment modalities (radiotherapy, chemotherapy and surgical resections) the prognosis is poor, as médian overall survival (OS) is 12-14 months. GBM's neuroimaging (non-invasive) features can provide opportunities for subclassification, prognostication, and the development of targeted thérapies that could advance the clinical practice. This thesis focuses on developing a prognostic model based on multimodal MRI-derived (Tl pre-and post-contrast, T2 and FLAIR) radiomics in GBM. The proposed method-ological framework consists in i) registering the available 3D multimodal MR images and segmenting the tumor volume, ii) extracting radiomics iii) building and validating a prognostic model using machine learning algorithms applied to multicentric clinical cohorts of patients. The core component of the framework rely on extracting radiomics (including intensity, shape and textural metrics) and building prognostic models using two different machine learning algorithms (Sup¬port Vector Machine (SVM) and Random Forest (RF)) that were compared by selecting, ranking and combining optimal features. The potential benefits and respective impact of several MRI pre-processing steps (spatial resampling of the voxels, intensities quantization and normalization, segmentation) for reliable ex¬traction of radiomics was thoroughly assessed. Moreover, the standardization of the radiomics features among methodological teams was done by contribut-ing to Multicentre Initiative for Standardisation of Radiomics . The accuracy obtained on the independent test dataset using SVM and RF reached upto 83%-77% when combining ail available features and upto 87%-77% when using only reliable features previously identified as robust, depending on number of features and modality. In this thesis, I developed a framework for developing a compré¬hensive prognostic model for patients with GBM from multimodal MRI-derived radiomics and machine learning. The future work will consists in building a uni-fied prognostic model exploiting other contextual data such as genomics. In case of new algorithm development we look forward to develop the Ensemble models and deep learning-based techniques.

Keywords : Glioblastoma multiforme, prognosis, radiomics, machine learn¬ing, prognostic model, SVM, RF, feature sélection, supervised learning.