Elaine Johanna Limkin

Roger Sun

Laurent Dercle

Evangelia I Zacharaki

Charlotte Robert

Sylvain Reuzé

Antoine Schernberg

Nikos Paragios

Eric Deutsch

Dr Charles Ferté
email: charles.ferte@gustaveroussy.fr

Promises and

Keywords: computational medical imaging, radiomics, quantitative imaging, precision medicine, tumor biology computational medical imaging, radiomics, quantitative imaging, precision medicine, tumor biology

come

Introduction

Medical images possess valuable information which can be harnessed through computer assisted interpretation. This technique, termed radiomics, is a rapidly-emerging discipline with the goal of extracting quantitative data from medical images to be used as clinical decision support tools [START_REF] Gillies | Radiomics: Images Are More than Pictures, They Are Data[END_REF][START_REF] Kumar | Radiomics: The Process and the Challenges[END_REF][START_REF] Lambin | Radiomics: Extracting more information from medical images using advanced feature analysis[END_REF]. In the context of oncology, information obtained from standard imaging modalities (Computed Tomography Scan (CT), Magnetic Resonance Imaging (MRI) and Positron Emission Tomography Scan (PET)), usually refers to simple traits such as gross shape, contrast enhancement, and size. However, imaging information is much richer, and the goal of radiomics is to extract high throughput quantitative features, covering the fields of texture, advanced shape modeling, and heterogeneity, to name a few. The increasing resolution quality has led to 3dimensional(3D) image acquisitions containing millions of voxels available for analysis, making the development of radiomics a natural progression, as more data necessitated increased computing capabilities to harness more information.

Radiomics has immense potential to improve knowledge in tumor biology and guide the management of patients at bedside [START_REF] Aerts | Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[END_REF]. Medical image analysis allows tumor monitoring across time, being routinely acquired throughout the course of treatment. Thus, imaging biomarkers may be used for and contribute to cancer detection, diagnosis, choice of therapeutic strategy, prognosis inference, prediction of response, and surveillance. Tumors exhibit spatial heterogeneity and temporal variation, recognized as a major cause of treatment failure and modulators of intrinsic tumor aggressiveness [START_REF] Heppner | Tumor heterogeneity[END_REF][START_REF] Junttila | Influence of tumour micro-environment heterogeneity on therapeutic response[END_REF][START_REF] O'connor | Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome[END_REF]. Imaging allows assessment of the entire tumor plus surrounding tissue -it is not blind to global heterogeneity -as opposed to invasive needle biopsies that are limited by sampling site. Radiomics could serve as a 'virtual biopsy' that would provide complementary information to, but not replace, conventional biopsies which remain vital in deep genomic analysis. This Review will systematically go through the radiomics pipeline, the significant milestones achieved in oncology, and future perspectives for improvement. The aim is to increase the awareness and interest of the Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology oncology community to radiomics, demystify the field for non-imaging experts, and engage the community to be involved in its further development and adoption in clinical practice. We will demonstrate the potential of radiomics to advance precision medicine.

Radiomics Pipeline

The process of radiomics consists of discrete steps: image acquisition and segmentation, feature extraction, statistical learning and 3D rendering (Figure 1).

Image acquisition and segmentation

Radiomics may be applied to different/multiple modalities, and selecting the one(s) to investigate depends on several factors. Radiomics quantitatively explores the distribution of signal intensities within a region or volume of interest (ROI/VOI). The spatial resolution of images varies, being around 1mm for CT and MRI and 4mm for PET. ROIs that are too small (e.g. sub-centimeter nodules) may not provide sufficient voxel information for analysis, whereas ROIs that are too large may be impacted by tumor heterogeneity (e.g. large tumors often present hypoxia in their centers). Standardization and calibration of non-ionizing procedures (Ultrasound, MRI) are intrinsically more complex than techniques based on photon detection (PET or CT) [START_REF] Bushberg | The essential physics of medical imaging[END_REF]. The minimal concentration of a molecule that is detectable on PET is 10 -12 moles compared to 10 -3 moles for MRI, meaning that the former might be 10 9 more sensitive for in vivo molecular imaging. Patient motion and respiration during acquisition affects the quality of reconstructed images; a CT is usually acquired in a few seconds whereas functional (MRI) or molecular (PET) imaging lasts several minutes. Data might be extracted from retrospective standard-of-care images, leading to large pool of patients. However, acquisition parameters vary considerably, which can introduce signal variations not due to biologic effects [START_REF] Bushberg | The essential physics of medical imaging[END_REF][START_REF] Galavis | Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters[END_REF][START_REF] Zhao | Reproducibility of radiomics for deciphering tumor phenotype with imaging[END_REF]. As with other high throughput technologies such as genomics, the aggregation of multiple datasets in radiomics can lead to substantial unwanted effects on the data. In determining the inclusion criteria for studies, an option is to have a large cohort in order to be less sensitive to variations due to acquisition/reconstruction parameters, or have a small cohort with homogenous data.

Crucial in the process is correct delineation of ROIs to be analyzed. Segmentations must be reproducible and reliable. Automatic methods are preferable for precision and efficiency. It has been proven that inter-and intraobserver variabilities are lower in automatic vs. semi-automatic vs. manual delineation [START_REF] Zhao | Reproducibility of radiomics for deciphering tumor phenotype with imaging[END_REF][START_REF] Balagurunathan | Test-Retest Reproducibility Analysis of Lung CT Image Features[END_REF][START_REF] Parmar | Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation[END_REF][START_REF] Velazquez | A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen[END_REF]. However, semi- Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology automatic delineation is usually mandatory, since automated methods are feasible only if there are strong signal differences between the lesion and the background. This is observed in PET and CT of pulmonary tumors, and possibly in certain MRI sequences. Commonly, such as in tumors surrounded by relatively homogenous normal structures, an experienced physician is required to correct contours, entailing computeraided outline detection followed by manual correction.

Currently, there are several open source platforms equipped with automatic and semi-automatic contouring functions, such as 3DSlicer (Growcut algorithm) [START_REF] Velazquez | Volumetric CT-based segmentation of NSCLC using 3D-Slicer[END_REF], which have active online support, and are continuously updated. Generated contours should be stored in an easily utilizable format for analysis across various platforms. Commonly used is DICOM-RTSTRUCT, which enables data sharing within and outside of the radiotherapy workflow, containing information on the images and ROIs [START_REF] Gorthi | Exporting Contours to DICOM-RT Structure Set[END_REF]. Other formats such as Analyze [START_REF]FormatAnalyze -MRC CBU Imaging Wiki[END_REF] and NIfTI are also used [START_REF]NIfTI-1 Data Format -Neuroimaging Informatics Technology Initiative[END_REF].

Feature extraction

Preprocessing

Raw imaging data need pre-processing to discriminate the signal from the noise. One optional step is filtering the signal within the ROI, which defines the frequencies to be utilized for subsequent analysis [18]. The choice of filter is guided by the nature of the imaging modality and tumor tissue. Another is the discretization or resampling [START_REF] Leijenaar | The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis[END_REF][START_REF] Orlhac | 18F-FDG PET-Derived Textural Indices Reflect Tissue-Specific Uptake Pattern in Non-Small Cell Lung Cancer[END_REF] of signal intensities that partitions continuous voxel values to finite/nominal intervals called bins. Techniques involve either absolute (using a fixed bin size) or relative (using a fixed number of bins whose size depends on the minimum and maximum values within the tumor) discretization. The choice of method is crucial as extracted features vary accordingly [START_REF] Brooks | The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake[END_REF][START_REF] Orlhac | Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis[END_REF]. Several studies [START_REF] Leijenaar | The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis[END_REF][START_REF] Orlhac | 18F-FDG PET-Derived Textural Indices Reflect Tissue-Specific Uptake Pattern in Non-Small Cell Lung Cancer[END_REF]23] have shown that absolute discretization results to features with better repeatability and lower sensitivity to changes, with the added advantage of not being volume dependent.

Radiomics Features

Features may be classified into several categories. There are quantitatively-extracted descriptors of size, shape, and other radiologic terminologies which characterize the tumor surface. First-order statistics are used to study the distribution of voxel values without considering spatial relationships [24]; second order statistics characterize spatial relationships between voxels, initially described by Haralick [START_REF] Haralick | Textural Features for Image Classification[END_REF] such as the co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM) [START_REF] Galloway | Texture analysis using gray level run lengths[END_REF], gray-level size zone matrix (GLZLM) [START_REF] Thibault | Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification[END_REF], and the neighborhood gray-level different matrix (NGLDM) [START_REF] Sun | Neighboring gray level dependence matrix for texture classification[END_REF]. Filter grids such as Gabor and Fourier may be used both in the pre-processing step and for extracting spatial or spatio-temporal features [START_REF] Narang | Radiomics in glioblastoma: current status, challenges and potential opportunities[END_REF][START_REF] Zheng | STEP: Spatiotemporal enhancement pattern for MR-based breast tumor diagnosis[END_REF]. A limitation is some extracted values are dependent on the ROIs contoured.

The extracted features can be global (one value for the whole ROI), or local (a value per image patch) when inhomogeneous patterns are present in the image, where dimensionality significantly increases if simple concatenation of local descriptors is performed. For this, more advanced frameworks explore compact statistical representations based on coding structures/dictionaries. When visual vocabularies and visual word weights are learned jointly, performance can be improved, as shown in classification of breast tissue density in mammograms, lung tissue in high-resolution CT, and brain tissue in MRI [START_REF] Wang | Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification[END_REF]. A more detailed review on texture analysis methods focusing on microscopy images of cells or tissues can be found in [START_REF] Cataldo | Mining textural knowledge in biological images: applications, methods and trends[END_REF].

The stability and the accuracy of features should be confirmed through the use of test-retest datasets; a good practice policy is to eliminate features that prove to be unreliable in the test-retest. To this end, several datasets are publicly available. Of note is the RIDER [START_REF] Armato | The Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) project: a resource for the development of change-analysis software[END_REF] dataset, which allows validation of results in the same set of patients with two scans taken 15 minutes apart.

Statistical Learning

The impact of the high number of radiomics variables The current radiomics pipeline typically incorporates around 50-5000 quantitative features (p), and these are still expected to increase. Meanwhile, the number of patients (n) in studies remains small, leading to a situation where p>>n, or the "curse-of-dimensionality" [START_REF] Clarke | The properties of high-dimensional data spaces: implications for exploring gene and protein expression data[END_REF]; resulting in a high probability of false positive results [START_REF] Ferte | Impact of Bioinformatic Procedures in the Development and Translation of High-Throughput Molecular Classifiers in Oncology[END_REF].

Adjustments for multiple comparisons (Bonferroni correction [START_REF] Holm | A Simple Sequentially Rejective Multiple Test Procedure[END_REF]) and controlling the false discovery rate (Benjamini-Hochberg [START_REF] Hochberg | More powerful procedures for multiple significance testing[END_REF]) are commonly utilized methods to address this. Another issue is overfitting, which can reduced by cross-validation with independent training and validation datasets [START_REF] Clarke | The properties of high-dimensional data spaces: implications for exploring gene and protein expression data[END_REF]. Several techniques of dimensionality reduction can be used to reduce the number of variables for analysis by exploiting statistical correlations and data redundancy [START_REF] Kanas | Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma[END_REF]. Unsupervised techniques map the data through a linear (e.g. Prinicipal and Independent Component Analyses) or non-linear (e.g. ISOMAP, Locally Linear Embedding) transformation in a lower-dimensional space, such that information loss is minimized, whereas supervised techniques select a subset of the original variables such that prediction accuracy is maximized. Feature selection can be performed independently, before classification or regression, or be combined into a single mathematical problem (e.g. Lasso, ElasticNet) [START_REF] Parmar | Machine Learning methods for Quantitative Radiomic Biomarkers[END_REF]. Attention has to be given in the case of supervised learning and small datasets to overfit the data; feature selection should be performed externally to the cross-validation procedure to correctly estimate the empirical error.

Promising machine learning approaches for prediction and classification tasks Machine learning approaches [START_REF] Paragios | Handbook of Biomedical Imaging: Methodologies and Clinical Research[END_REF] such as decision trees and random forests [START_REF] Kumar | Discovery Radiomics for Computed Tomography Cancer Detection[END_REF][START_REF] Hawkins | Predicting Malignant Nodules from Screening CT Scans[END_REF][START_REF] Shen | Multi-scale Convolutional Neural Networks for Lung Nodule Classification[END_REF][START_REF] Zacharaki | Survival Analysis of Patients with High-Grade Gliomas Based on Data Mining of Imaging Variables[END_REF][START_REF] Zacharaki | Investigating machine learning techniques for MRI-based classification of brain neoplasms[END_REF][START_REF] Liu | Radiological Image traits Predictive of Cancer Status in Pulmonary Nodules[END_REF], support vector machines [START_REF] Parmar | Machine Learning methods for Quantitative Radiomic Biomarkers[END_REF][START_REF] Zacharaki | Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme[END_REF][START_REF] Wang | Prediction of malignant and benign of lung tumor using a quantitative radiomic method[END_REF] and more recently deep neural networks[49] appear to be promising in the domain of medical image computing. The recent increase of available annotated imaging data in public portals expedited the use of these techniques. In particular, deep convolutional neural networks (CNNs) have been shown to excel at learning a hierarchy of increasingly complex features directly from raw data, alleviating the explicit extraction of low-order, pre-defined features. In such frameworks, feature extraction and selection are performed jointly with classification within the optimization of the same deep architecture, thus performance Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology can be tuned in a systematic fashion. Common CNN schemes train patch-level classifiers [START_REF] Zhang | Deep learning based classification of breast tumors with shear-wave elastography[END_REF] that automatically locate discriminative regions and then aggregate local predictions. The learning process is usually facilitated by pre-training using standardized data followed by supervised training for fine-tuning.

Thus far, CNNs have been shown to excel in the detection and classification of pulmonary nodules in large series of lung CTs from the Lung Image Database Consortium (LIDC-IDRI) [START_REF] Kumar | Discovery Radiomics for Computed Tomography Cancer Detection[END_REF][START_REF] Shen | Multi-scale Convolutional Neural Networks for Lung Nodule Classification[END_REF][START_REF] Setio | Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks[END_REF]. A comparison in mortality prediction from chest CTs [START_REF] Carneiro | Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography[END_REF] between (i)a unified deep learning framework (features and classifier are automatically learned in a single optimization process) and (ii)a standard multi-stage framework (predefined radiomics features are introduced into a classifier), showed increased accuracy of the deep learning framework by 2.5%-12.5%. Similar concepts have been applied to mammograms for breast cancer screening [START_REF] Cheng | Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans[END_REF][START_REF] Kallenberg | Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring[END_REF].

Three-dimensional rendering

The visualization of the entire tumor, commonly performed in diagnostic radiology, is possible through 3D rendering [START_REF] Calhoun | Three-dimensional Volume Rendering of Spiral CT Data: Theory and Method1[END_REF]. Radiomics takes this a step further through visualization and assessment of tumor heterogeneity which provides invaluable information in clinical oncology and in cancer research. For instance, this can direct which intra-tumoral region is best to biopsy for adequate samples. In radiotherapy dose painting, rendering is instrumental in visualizing treatment-resistant regions, facilitating dose escalation and consequently decreasing normal tissue toxicity [START_REF] Sun | In Regard to Mattonen et al[END_REF]. Theragnostic imaging [START_REF] Bentzen | Theragnostic imaging for radiation oncology: dose-painting by numbers[END_REF], the use of molecular/functional imaging to prescribe radiation distribution, will benefit from radiomics features reflecting phenotypes associated with poor radioresponsiveness [START_REF] Bentzen | Molecular-imaging-based dose painting -a novel paradigm for radiation therapy prescription[END_REF].

Significant milestones of radiomics in oncology

Radiomics is substantial in oncology, not surprising given the increased availability of and information in patient imaging data. Oncologic imaging is a substantial part of a radiologists' daily workload. More than 128 million imaging examinations were realized in the United States in 2015, greater than 60% of which were CTs (Figure 2B); and an increase of >36% in use of advanced imaging was seen from 2005 to 2015[59], reiterating the vast amount of available data. This section aims to depict the impact of radiomics in each stage of cancer care, with tables outlining key details.

Advancements in the understanding of tumor biology

Tumor heterogeneity

The extensive genetic and molecular landscape within tumors -tumor heterogeneity-is known to be specific to the malignant process. Intra-tumoral heterogeneity is dynamic and is modified by therapeutic effects.

Heterogeneity may portend treatment resistance and poor outcomes due to the emergence of resistant subclonal populations [START_REF] Eskey | 2H-nuclear magnetic resonance imaging of tumor blood flow: spatial and temporal heterogeneity in a tissue-isolated mammary adenocarcinoma[END_REF]. Through quantitative serial analysis of imaging, both temporal and spatial heterogeneities may be analyzed. In particular, texture analysis is emerging as an effective method to quantify heterogeneity. Table 1 displays some representative work. To illustrate, in CTs of lung adenocarcinomas, entropies of the tumor core and boundary regions were computed separately, with results showing that a higher ratio between the two are associated with poorer outcome [START_REF] Grove | Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma[END_REF]. Thus, this imaging phenotype may reflect distinct traits such as necrosis in the core and proliferation in the periphery. In PET, texture analysis showed that healthy lung tissues are more homogenous than malignancies, and texture features differentiated tumor histologies [START_REF] Orlhac | 18F-FDG PET-Derived Textural Indices Reflect Tissue-Specific Uptake Pattern in Non-Small Cell Lung Cancer[END_REF]. Similar studies on texture heterogeneity, with or without other feature classes, depict quantitative imaging traits mirroring genomic and molecular phenotypes [START_REF] Li | Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set[END_REF][START_REF] Ganeshan | Non-Small Cell Lung Cancer: Histopathologic Correlates for Texture Parameters at CT[END_REF][64][START_REF] Mazurowski | Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging[END_REF][START_REF] Gevaert | Non-Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data-Methods and Preliminary Results[END_REF][START_REF] Jamshidi | Illuminating radiogenomic characteristics of glioblastoma multiforme through integration of MR imaging, messenger RNA expression, and DNA copy number variation[END_REF][START_REF] Hayano | Texture Analysis of Non-Contrast-Enhanced Computed Tomography for Assessing Angiogenesis and Survival of Soft Tissue Sarcoma[END_REF][START_REF] Zhu | Identifying molecular genetic features and oncogenic pathways of clear cell renal cell carcinoma through the anatomical (PADUA) scoring system[END_REF].

Modeling of key oncogenic processes

Several biological processes are known to be necessary components of oncogenesis [START_REF] Hanahan | The Hallmarks of Cancer[END_REF][START_REF] Hanahan | Hallmarks of Cancer: The Next Generation[END_REF]. Various molecular biology techniques have been developed to better appraise biological and genetic causes of these, A study in lung cancer used an angiogenesis-related marker injected in specimens, and linear correlations were shown between CT texture heterogeneity features and percentage of the tumor stained for the marker [START_REF] Ganeshan | 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]. In glioblastomas, the 'contrast enhancement imaging phenotype' was significantly associated with tumor angiogenesis [64]. In breast cancers, certain computer extracted imaging phenotypes of MRIs have been shown to differentiate subtypes [START_REF] Li | Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set[END_REF].

In lung cancers, multi-categorical quantitative imaging features were shown to reflect tumor hypoxia [START_REF] Ganeshan | Non-Small Cell Lung Cancer: Histopathologic Correlates for Texture Parameters at CT[END_REF][START_REF] Gevaert | Non-Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data-Methods and Preliminary Results[END_REF]. In the preclinical setting, causal relationship between genetic changes and imaging features was demonstrated by pre-imaging administration of doxycycline which induces hypoxic changes [START_REF] Panth | Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells[END_REF]. Clinically, these results may be helpful in identifying tumor resistance and the need for intensified regimens.

Further, radiomics phenotypes have been shown to correlate with mRNA and protein expression in breast

MRIs [START_REF] Zhu | Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma[END_REF], alluding to the underlying mechanisms of tumor invasion. In glioblastomas, peritumoral fluid attenuation inversion recovery MRI signal abnormalities were found to reflect genes and microRNAs accounting for cellular migration and invasion [START_REF] Zinn | Radiogenomic mapping of edema/cellular invasion MRIphenotypes in glioblastoma multiforme[END_REF]. In addition, lymphocyte infiltration has been reflected in imaging features of HER2+ breast cancers [START_REF] Basavanhally | Computerized Image-Based Detection and Grading of Lymphocytic Infiltration in HER2+ Breast Cancer Histopathology[END_REF].

Radiomics features also appear to model tumor proliferation. A landmark study in liver cancer demonstrated

that the combination of 28 traits could reconstruct as much as 78% of transcriptome variation, and were specifically associated with genes involving cell proliferation [START_REF] Segal | Decoding global gene expression programs in liver cancer by noninvasive imaging[END_REF]. In breast cancers, analysis of TCGA-TCIA data revealed clear associations between radiomics phenotypes and proliferation at the protein and gene expression levels. Imaging phenotypes of increased tumor size were also associated with P-cadherin expression, shown to correlate with increased proliferation [START_REF] Zhu | Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma[END_REF].

Better implementation of precision medicine

Precision medicine is an approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle; integrating information from multiple sources with the end goal of personalized management [START_REF]Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology 79. Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening[END_REF]. In this section, some key radiomics applications are described.

Diagnosis

One of the biggest challenges in oncology is the development of accurate, cost-effective screening procedures.

In the National Lung Cancer Screening Trial (NLST), low-dose CTs interpreted by radiologists have been shown to be beneficial in mortality reduction in a cohort of 53,000 patients[79]. These however exhibit high false-positive rates (>90%); wherein radiomics could be effective in improving specificity. Using NLST data, patients with screening-detected lung cancer were matched with subjects with benign nodules. Accuracies of 80% and 79% were found for predicting nodules that will become cancerous at one and two years, respectively [START_REF] Hawkins | Predicting Malignant Nodules from Screening CT Scans[END_REF]. Using LIDC-IDRI patients with a total of 42,340 lesions (through data augmentation), deep convolutional sequencers detected malignant nodules with diagnostic accuracy, sensitivity and specificity >75% [START_REF] Kumar | Discovery Radiomics for Computed Tomography Cancer Detection[END_REF]. Two studies [START_REF] Shen | Multi-scale Convolutional Neural Networks for Lung Nodule Classification[END_REF][START_REF] Zinovev | Probabilistic lung nodule classification with belief decision trees[END_REF] with 914 and 1375 lesions, respectively, published promising results with predictive performances ranging from 60 to 80%. Notably, multi-scale CNNs attained 86.84% accuracy on classification with automatic nodule detection and segmentation [START_REF] Shen | Multi-scale Convolutional Neural Networks for Lung Nodule Classification[END_REF]. Other publications in lung carcinomas reveal similar findings [START_REF] Liu | Radiological Image traits Predictive of Cancer Status in Pulmonary Nodules[END_REF][START_REF] Wu | Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology[END_REF][START_REF] Maldonado | Noninvasive Characterization of the Histopathologic Features of Pulmonary Nodules of the Lung Adenocarcinoma Spectrum using Computer-Aided Nodule Assessment and Risk Yield (CANARY)-A Pilot Study[END_REF]. Table 2 presents representative publications concerning diagnosis.

Staging and prognosis

Accurate staging determines the aggressiveness of therapeutic strategy and spells the difference between curative and palliative treatments, requiring constant advancements in imaging techniques to improve decisionmaking. In colorectal carcinomas, for which liver metastases are frequent, radiomics features extracted from unenhanced hepatic CT scans showed texture abnormalities suggestive of metastases in apparently diseasefree areas [START_REF] Ganeshan | Texture analysis in non-contrast enhanced CT: Impact of malignancy on texture in apparently disease-free areas of the liver[END_REF]. This could streamline the staging process and minimize treatment delays with the information In prognostication, increased shape complexity in lung adenocarcinoma [START_REF] Grove | Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma[END_REF] was associated with poorer clinical outcomes. Morphologically similar tumors by visual inspection turned out to have large differences in quantitative parameters, denoting that radiomics can supplement radiologists' interpretations. Another study [START_REF] Coroller | CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma[END_REF] demonstrated that radiomics features are prognostic for both distant metastasis and survival, and a radiomics signature significantly improves prognostication when added to clinical data. Similar studies were published in colorectal cancers [START_REF] Huang | Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer[END_REF][START_REF] Liang | The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer[END_REF]. In a large cohort involving multiple datasets with varying tumor types [START_REF] Aerts | Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[END_REF], differences in imaging phenotypes showed clinical significance and impact on prognosis. These results were reiterated in a dataset of almost 900 patients [START_REF] Parmar | Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer[END_REF] and externally validated in oropharyngeal cancers [START_REF] Leijenaar | External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma[END_REF]. Table 3 shows recent radiomics publications on tumor staging and prognosis.

Prediction of treatment response

Radiomics features could be used to predict which patients would respond to a treatment regimen. Table 4 summarizes notable publications. In glioblastomas, a robust association between radio-phenotypes and gene expression has been shown, including a link with epidermal growth factor receptor (EGFR) overexpression [64,[START_REF] Lee | Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme[END_REF]. Radiomics features have been shown to stratify treatment outcomes from angiogenic therapy in recurrent glioblastomas [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]. EGFR mutation [START_REF] Liu | CT Features Associated with Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma[END_REF] and response to Gefinitib [START_REF] Aerts | Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC[END_REF] were also reflected in a combination of features in lung cancers. With the rapid rise in targeted therapy, it is worthwhile to continue discovering radiogenomic associations that may influence management. An imaging surrogate to could aid patient selection and avoid unwarranted expense and toxicities for non-responders.

In the neoadjuvant chemotherapy setting, a multiparametric model in breast MRIs identified non-responders with 84% sensitivity [START_REF] Michoux | Texture analysis on MR images helps predicting nonresponse to NAC in breast cancer[END_REF], with the goal of developing a computer-assisted prediction solution, which may be more cost-effective than molecular assessments. In rectal carcinomas, a combination of radiomics features Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology was seen to be predictive of pathologic response to neoadjuvant treatment [49]. Studies in other cancer localizations [START_REF] Ahmed | Texture analysis in assessment and prediction of chemotherapy response in breast cancer[END_REF][START_REF] Cook | Non-Small Cell Lung Cancer Treated with Erlotinib: Heterogeneity of (18)F-FDG Uptake at PET-Association with Treatment Response and Prognosis[END_REF][START_REF] Goh | Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker[END_REF][START_REF] Bundschuh | Textural Parameters of Tumor Heterogeneity in 18 F-FDG PET/CT for Therapy Response Assessment and Prognosis in Patients with Locally Advanced Rectal Cancer[END_REF] have shown the possibility of assessing treatment response using imaging markers.

In the advent of immunotherapy, there have been been patients who experience pseudoprogression (PSPD) [START_REF] Chiou | Pseudoprogression and Immune-Related Response in Solid Tumors[END_REF],which has been shown to be due to lymphocyte infiltration in and around the tumor [START_REF] Tumeh | PD-1 blockade induces responses by inhibiting adaptive immune resistance[END_REF]. A study demonstrated that a radiomics signature from CTs could reflect tumors with increased lymphocytes and discriminate PSPD from true progression[100], possibly aiding decision-making for equivocal cases.

Disease monitoring and surveillance

Inflammation leads to post-treatment reactions that might complicate response evaluation by imaging. In this context, functional imaging helps differentiate scar tissue from viable tumor, but equivocal cases remain.

Radiomics can further enhance evaluation. For instance, texture features from CT images of lung cancer treated with stereotactic ablative body radiotherapy (SABR) showed that the ground glass appearance (an area of hazy increased lung opacity through which vessels and bronchial structures may still be seen

Perspectives related to the effective translation of the radiomics biomarkers into the clinic

There is convincing evidence that radiomics could be an invaluable tool in revolutionizing oncology. Significant progress has been made, but further improvements are imperative to achieve routine utilization from bench to bedside.

Standardization and the perspective relative to molecular biomarkers

Radiomics literature is constantly growing, and we predict that it will follow the curve of the molecular biomarkers as interests and funding increase (Figure 2A). At present, however, the existing level of evidence is insufficient (Figure 3). There are notable differences in terms of sample size, methodology, performance metrics, and clinical utility; reiterating that improvements are essential. To ensure robustness and dissemination of radiomics-based predictive tools, standardization of imaging protocols and feature calculations is ideal, but seldom attainable [START_REF] Zhao | Reproducibility of radiomics for deciphering tumor phenotype with imaging[END_REF]113]. Imaging performed at different centers leads to bias [START_REF] Zhao | Reproducibility of radiomics for deciphering tumor phenotype with imaging[END_REF]114]. In fact, significant variation of radiomics features was observed across different CT scans through a phantom experiment [113]. The use of credentialing scanners, careful study design, noise reduction, and statistical analyses adjusted to account for unwanted effects are possible solutions. Table 5 highlights the current challenges and corresponding recommendations in radiomics.

Actual data access

Improving data management is challenging for a number of reasons including (i)administrative (manpower), (ii)ethical (patient privacy), and (iii)personal/institutional (intellectual property). One successful undertaking is TCIA[115], through which investigators have access to robust anonymized imaging data in easily utilizable DICOM format.

Researchers should be encouraged to submit data to a centralized online radiomics repository akin to the Gene Expression Omnibus[116] for microarrays. A standardized non-software dependent format of storing and annotating data will facilitate multi-platform utilization. These will not only be useful before starting research projects, but also during and after; ensuring the integrity and availability of information. These must allow incorporation of image features, annotations, medical information, and genetic data in order to create prognostic and predictive models correlating imaging with genetic phenotypes and clinical outcomes. It is critical that the fidelity of the data is maintained and access is regulated, for which competent system administrators are mandatory. These are big steps, but we believe they are essential for the advancement of the discipline. Forming a multinational consortium may be a prudent solution, some of whose functions would Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology be to (i)draft guidelines on data collection, anonymization, and sharing, (ii)to standardize reporting of radiomics studies. More initiatives are necessary, with multidisciplinary working groups that include oncologists, radiologists, medical physicists, applied mathematicians, and computer scientists, to improve the field and educate people on its use such that it can become a reliable part of a decision support system in oncology.

Improving multidisciplinary network and dissemination of radiomics

Radiomics has been gaining ground in terms of exposure and interest in recent scientific congresses, with the number of publications per year almost doubling in the last three and almost tripling in the last five years (197 in 2015, an increase of 77% since 2013 and 186% since 2011) (Figure 2A). This is translated in increased exposure of radiomics in current radiology meetings (e.g. RSNA) and in major oncology meetings (e.g. ASCO, ESMO, AACR, ASTRO). This is a formidable start but efforts need to be increased.

Conclusion

Imaging biomarkers constructed from quantitative image analysis have great potential to advance precision medicine and to enhance cancer biology knowledge. As radiomics cements its position in translational cancer research to attain utilization at bedside, we anticipate radiomics data being integrated and analyzed with genomics, proteomics and other -omics; providing information invaluable in personalized medicine. Radiomics will certainly progress further with the advent of more imaging data, better algorithms, and availability of other data types such as coherent datasets integrating imaging, clinical, and genomic information.

6

 6 Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

8

 8 Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

9

 9 Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

11

 11 Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

12

 12 Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology and the emergence of quantitative imaging analysis is a promising tool to complement and enhance existing techniques.

13

 13 Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

14

 14 Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology steering the clinician towards doing confirmatory examinations for patients at risk. Plus, early detection of metastases may increase chances of complete tumor eradication.

16

 16 [101]), following SABR predicts recurrence versus radiation-induced lung injury, which has a similar radiologic picture[102]. This is particularly useful because lung cancer typically progresses quickly; hence the decision for salvage therapy is most valuable if made early, likely providing more treatment options compared to late-detected disease. In addition, a pilot study on renal cell cancers has demonstrated the possibility of capturing sub-visual treatmentrelated imaging changes[103].Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

17

 17 As investigators have learned from discovery of biomarkers[104,105], there are pitfalls to be avoided. In the same way as the REporting recommendations for tumour MARKer prognostic studies (REMARK)[106] or the Minimum information about a microarray experiment (MIAME)[107] guidelines, recommendations specific to radiomics are necessary. In reporting, key elements should be sufficiently detailed and made available to allow comparisons and validation: (i)raw imaging data including acquisition parameters, (ii)ROIs, (iii)radiomics features and the extraction software, methods, formulae used and (iv)statistical learning methods. Meriting attention is the non-standardized names of extracted features, such that two publications might discuss a feature with the same formula/definition but call these differently.The recently published roadmap for imaging biomarkers[108] is a notable advancement, showcasing key recommendations for clinical translation of radiomics. Also admirable are large initiatives aiming to develop automatic segmentation solutions such as the Google-National Health Service partnership DeepMind Health project[109]. Another is the Grand Challenges in Biomedical Image Analysis[110], with goals of developing algorithms for specific problems such as the Lung Nodule Analysis (LUNA) Challenge[111], a large-scale Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology automatic nodule detection with 888 patients and The Digital Mammography (DREAM) Challenge[112] aiming to improve predictive accuracy of digital mammography with over 640,000 images.

  Significant efforts have been made to address these issues. The National Cancer Institute, in cooperation with other societies like the Canadian Institute of Health Research, Cancer Research United Kingdom and American College of Radiology Imaging Network, have supported initiatives, including the Quantitative Imaging Network (QIN), to promote the development of QI methods, annotated image databases, and QI standards[117]. Other significant efforts include the Quantitative Imaging Biomarkers Alliance (QIBA), a critical component of US' Cancer Moonshot initiative[118], and the Euregional Computer Assisted Theragnostics project (EuroCAT)[119], whose goals include enhancing data sharing and facilitating patient recruitment in clinical trials.

19

 19 Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

20 21

 2021 Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

Figure 1 :Figure 2A :

 12A Figure 1: The radiomics pipeline, showing the major steps: image acquisition and segmentation, feature extraction, three-dimensional (3D) rendering and statistical learning. Important to note is that radiomics data is meant to be integrated and analyzed with clinical, pathologic and -omics data to improve precision medicine. Figure 1 96x46mm (300 x 300 DPI)

Figure 3 :

 3 Figure 3: Comparison of key biomarker and radiomics studies Figure 3 223x241mm (300 x 300 DPI)

  18. Wells WM. Efficient Synthesis of Gaussian Filters by Cascaded Uniform Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 1986; PAMI-8(2):234-239. Connor JPB, Aboagye EO, Adams JE et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 2016. doi:10.1038/nrclinonc.2016.162.

	F o F o F o F o F o F o
	r r r r r r
	P P P P P P
	e e r e e r e e r e e r e e r e e r
	R e v i e w R e v i e w R e v i e w R e v i e w R e v i e w R e v i e w

27

Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology 108. O'

Table 1 . Advancements in the prediction of tumor phenotypes, genotypes, molecular subtypes through radiomics Tumor type and imaging modality Author, Year Training/ vali-dation Sets (N) Extracted radiomics features Feature selection and statistical learning Biologic correlation and relevance

 1 Computed tomography scan, b MRI: Magnetic resonance imaging, c PET: Positron emission tomography, d CE: Contrast-enhanced, e TCGA -TCIA: The Cancer Genome Atlas-The Cancer Imaging Archive, f CEIPs: computer-extracted image phenotypes, g ER: estrogen receptor, h PR: progesterone receptor, i CNVs: copy number variations, j NSCLC: Non-small cell lung cancer, k SRE: Short-Run Emphasis, l LRE: Long-Run Emphasis, m RLNU: Run Length Non-Uniformity, n LGLZE: Low Gray-Level Zone Emphasis, o HGLZE: High Gray-level Zone Emphasis, p SUV :standardized uptake value, q EGFR: epidermal growth factor receptor, r GSEA: Gene Set Enrichment analysis, s AUC: area under the curve, t VEGF: vascular endothelial growth factor

	Breast cancer	Li,	T: 91	38 CEIPs f (size, shape,	Leave-one-case-out	CEIPs f are able to predict breast cancer
	(TCGA-	2016 [62]		morphology, enhancement texture,	with stepwise feature	molecular subtypes (ER g , PR h ,
	TCIA e			kinetic curve, enhancement-	selection and linear	HER2Neu, basal-like) with AUCs s from
	dataset)			variance kinetics)	discriminant analysis,	0.67 to 0.89.
					logistic regression
	CE d MRI b
	Breast	Zhu,	T: 91	38 radiomics features	Linear regression,	Landmark description of the
	Cancer	2015 [74]		(size, shape, morphology,	Benjamini-Hochberg	associations between radiomics
	(TCGA -TCIA			enhancement texture, kinetic curve,	adjustment, affinity	features and somatic mutations, CNVs i ,
	e dataset)			enhancement-variance kinetics)	propagation clustering	gene expression and miRNA. Radiomic
						phenotypes correlated with key
	MRI b					oncogenic pathways such as DNA
						replication.
	Breast cancer	Mazurowski,	T: 48	23 initial features:	Logistic regression	Radiomics features have significant
	(TCGA -TCIA	2014 [65]		4 geometric, 14 Haralick texture, 1	analysis,	association with the luminal B breast
	e dataset)			kinetic and 4 dynamic	Bonferroni correction	cancer molecular subtype (p=0.0015)
	CE d MRI b
	NSCLC j	Orlhac,	T: 48	7 Textural indices (homogeneity,	Spearman	Texture features differentiate between
		2015 [20]		entropy, SRE k , LRE l , RLNU m ,	correlations,	benign and malignant tissues, and
	PET c		V:	LGLZE n , HGLZE o );	no adjustment for	between different cancer histological
			phantoms	SUV p max and metabolic volume; 3	multiple testing	subtypes. The presence of necrosis
				texture matrices (co-occurrence,		was significantly different between
				gray-level run length and gray-level		squamous and adenocarcinomas,
				zone length matrices)		unlike the T or N stage.
	NSCLC	Ganeshan,	T: 14	Low order texture features	Linear mixed-effects	Texture features (standard deviation
		2013 [63]		computed upon the pixel intensity	model approach,	and mean value of positive pixels) are
	CT a			distribution. Laplacian of Gaussian	Holm-adjustment	associated with tumor hypoxia [Glut1/
				transformation, spatial scale filters.		pimonidazole] and angiogenesis [CD34]
	NSCLC	Gevaert,	T: 26	153 computational image features,	Generalized linear	Image features associate with publicly
		2012 [66]		26 semantic image features, and a	regression with lasso	available gene expression pathways
	CT a			PET SUV p	regularization	(Hypoxia, KRAS pathway) with
	PET c					accuracies of 59 -83%
	Colorectal	Panth,	33 mice	625 radiomics features (intensity,	Intra-class correlation	Radiomics features demonstrate the
	cancer	2015,		shape, texture, wavelet features,	coefficiant rank, based	causality between genomics changes
	xenografts	(pre-		Laplacian of Gaussian features)	on the 50% top-	and imaging features
		clinical) [73]			ranked features
	CE d CT a
	Glioblastoma	Jamshidi,	T: 23	(1) infiltrative versus edematous T2	Resampling statistics,	Gene-to-trait associations were found
	multiforme	2014 [67]		abnormality, (2) degree of contrast	analyse of variance,	such as contrast-to-necrosis ratio with
				enhancement, (3) necrosis, (4)	Pearson correlation	KLK3 and RUNX3; SVZ involvement
	CE d MRI b			supraventricular zone (SVZ)	coefficient	with the Ras oncogene family and the
				involvement, (5) mass effect, and		metabolic enzyme TYMS; and
				(6) contrast-to-necrosis ratio.		vasogenic edema with the oncogene
						FOXP1 and PIK3IP1
	Glioblastoma	Diehn,	T: 22	10 binary imaging traits	Unsupervised	Significant associations were found
	Multiforme	2008 [64]		(enhancement, necrosis, mass	hierarchical clustering;	between: angiogenesis and tumor
			V: 110	effect,T2 edema, cortical	Spearman rank-	hypoxia with the contrast enhancement
	CE d MRI b			involvement, SVZ involvement, C:N	correlation coefficient	imaging phenotype (p=0.012);
				ratio, contrast/T2 ratio,T2 edema,		proliferation gene-expression signature
				T2 heterogeneity)		and mass effect phenotype (p=0.0017);
						EGFR q protein overexpression and
						contrast:necrosis imaging trait
						(p<0.002)
	Glioblastoma	Zinn,	T: 26	Quantitative models of	For imaging-genomic	Imaging traits associated with
	Multiforme	2011 [75]		edema/invasion, enhancing tumor,	analysis: Comparative	upregulation of mRNA involved in
			V: 26	necrosis	Marker Selection,	cellular migration/invasion
	CE d MRI b				Ingenuity Pathway	(PERIOSTIN), which was seen to
					Analysis	correlate with decreased survival
						(p=0.0008)
	Soft tissue	Hayano,	T: 20	Low order texture features	Multivariate Cox	Tumor texture features are associated
	sarcoma	2015 [68]		computed upon the pixel intensity	regression model; no	with microvessel density, VEGF t ,
				distribution. Laplacian of Gaussian	correction for multiple	soluble VEGF t receptor-1, and overall
	CT a			transformation, spatial scale filters	testing	survival, and that the mean value of
						positive pixels is an independent
						prognostic factor in multivariate analysis
						(p=0.01)

Table 2 . Advancements in oncologic diagnosis in radiomics

 2

	Tumor	Author,	Training/	Extracted radiomics	Feature selection and	Clinical utility for diagnosis
	type and	Year	validation Sets	features	statistical learning
	imaging		(N patient)
	modality
	Lung	Liu,	T: 102	24 semantic radiological	Accuracy/AUROC e ,	Radiological image traits are
	cancer	2016 [46]		traits ( location, size, shape,	Hold-out cross-validation,	useful in predicting malignancy
			V: 70	margin, density, internal	Bootstrap, Youden J index	in lung nodules
	Low dose			features, external features,		(accuracy 70-77%)
	CT a			associated findings) used to
				create a linear classification
				model
	Lung	Hawkins,	malignant:	219 three-dimensional	Hierarchical learning	Radiomics features are
	cancer	2016 [42]	104(T) + 92(V);	features (size, shape,	framework-Multi-scale	effective in the prediction of
	(NLST f		benign:	location and texture	Convolutional Neural Networks,	malignant vs. benign lung
	dataset)		208(T) + 196 (V)	matrices)	Random Forest classifier	nodules with accuracies of
						80% and 79%.
	CT a
	Lung	Wu W.,	T: 198	440 radiomic features (voxel	Correlation-based feature	Radiomics features predict
	cancer	2016 [81]		intensity distribution, shape,	elimination and univariate feature	NSCLC histology (highest
			V: 152	texture matrices)	selection. 24 feature selection	achieved AUC i = 0.72)
	CE d CT a				and 3 classification methods
					tested
	Lung	Kumar,	93 patients,	abstract imaging-based	Deep convolutional neural	Method used outperforms the
	cancer	2015 [41]	42,340 lesions	features learned from a deep	network; binary decision tree	state-of-the art approach for
	(LIDC-IDRI			convolutional neural network	classifier	lung nodule benign vs.
	dataset)		T: 34,295	learning architecture		malignant classification
						(accuracy 77%, sensitivity
	CT a		V: 3,810			79%, specificity 76% )
	Lung	Shen,	T: 1100 nodules	Learned classifiers from the	Multi-scale Convolutional	The convolutional neural
	cancer	2015 [43]		neural network algorithm	Neural Networks	network method used showed
	(LIDC-IDRI		V: 275 nodules			86% accuracy for pulmonary
	dataset)					nodule classification
			(with
	CT a		augmentation)
	Lung	Maldonado,	T: 54 nodules	Computer-aided nodule	Affinity propagation	CANARY can noninvasively
	adenocar-	2013 [82]		assessment and risk yield	(unsupervised clustering),	characterize pulmonary
	cinoma		V: 86 nodules	(CANARY) : representative	Multinomial logistic regression,	nodules of the adeno-
				exemplars of the spectrum of	nonparametric Spearman	carcinoma spectrum, with the
	High			solid and ground glass	correlation	exemplar distribution within
	resolution			components of nodules		each nodule correlating well
	CT a					with the proportion of histologic
						tissue invasion (p<0.0001)
	Lung	Zinovev,	914 instances	63 2D low-level image	Multiple-label belief decision	Multiple-label classification
	cancer	2011 [80]		features from four	trees,	algorithms are an appropriate
	(LIDC-IDRI		T: 90%	categories: shape, texture,	5-fold cross validation	method of representing the
	dataset)			intensity, size		diagnoses of radiologists on
			V: 10%			lung CT scans (AUC i 69%)
	CT a
	Prostate	Fehr,	T: 147	first and second order	Oversampling approach,	Texture features predicts
	cancer	2015 [120]		texture statistics from	Support Vector Machine	benign vs malignant prostate
				intensity distribution(mean,		lesions and Gleason score
	MRI b			SD h , skewness,kurtosis);		prediction of malignant
				Haralick features (energy,		tumors, with a better accuracy
				entropy, correlation,		(93%) than using diffusion MRI
				homogeneity, contrast)		alone
	* a CT: Computed tomography scan, b MRI: Magnetic resonance imaging, d CE: Contrast-enhanced, e AUROC: area under the receiver operating characteristic,
	f NLST: National Lung Cancer Screening Trial, g LIDC-IDRI: Lung Image Database Consortium, h SD: standard deviation, i AUC: area under the curve

Table 3 . Recent radiomics works in staging and prognosis

 3 Computed tomography scan, b MRI: Magnetic resonance imaging, d CE: Contrast-enhanced , e TCGA -TCIA: The Cancer Genome Atlas-The Cancer Imaging Archive, f NSCLC: Non-small cell lung cancer, g HNSCC: Head and Neck Squamous cell carcinoma, h CI: Concordance Index, i AUC: area under the curve, j LASSO: least absolute shrinkage and selection operator, k PFS: progression-free survival, l OS: overall survival, m CEIPs: computer-extracted image phenotypes

	Tumor type	Author, Year	Training/ validation	Extracted radiomics	Feature selection	Utility and Significance
	and imaging		Sets (N)	features	and statistical
	modality				learning
	NSCLC f and	Aerts,	T: 31, 21, 422	440 radiomics image	Selection of the most	Significant associations were
	HNSCC g	2014 [4]		features (intensity,	stable variables	found between imaging
			V: 225, 136, 95, 89	shape,texture, multiscale	(n=100),	features and tumor stage and
	CE d CT a			wavelet)	unsupervised	overall survival, with the
					clustering; Friedman	radiomics signature having a
					test, bootstrap	CI h = 0.65 (NSCLC), CI h = 0.69
					approach	(HNSCC)
	NSCLC and	Parmar,	T: Lung -422	440 radiomics features	Consensus clustering Significant agreement in the
	HNSCC	2015 [87]	HN -136	(tumor intensity, shape,		clusters between training and
				texture, wavelet)		validation sets was seen.
	CE d CT a		V: Lung 2 -225			Features were associated with
			HN2 -95			stage (lung AUC i = 0.61, H&N
						AUC i = 0.77), HPV status (H&N
						AUC i = 0.58) and prognosis
						(lung AUC = 0.60, H&N AUC i =
						0.68) .
	Lung adeno-	Coroller,	T: 98	635 radiomics features	Bootstrapping,	Imaging features aid in the
	carcinoma	2015 [84]		(intensity, shape, texture ,	Benjamini-Hochberg	identification of patients at risk of
			V: 84	Laplacian of Gaussian and	correction	developing distant metastases
	CT a			Wavelet filtered)		(CI h = 0.61), facilitating individual
						treatment decisions
	Lung adeno-	Grove,	T:: (cohort 1 = 61;	shape complexity	Concordance	Increasing tumor entropy and
	carcinoma	2015 [61]	cohort 2 = 47)	(convexity) and	correlation coefficient,	lower convexity are associated
				intratumoral density	Dynamic Range,	with overall survival, even after
	CE d CT a		V: 32 (RIDER test-	variation (entropy ratio)	correlation matrix	adjustment for tumor stage.
			retest)
	Glioblastoma	Kickingereder,	T: 112	4,842 total	Supervised principal	Radiomics-based classification
	Multiforme	2016 [90]		17 first-order	component analysis,	of recurrent glioblastoma
			V: 60	features ,9 volume and	Cox proportional	permits the prediction of
	CE d MRI b			shape features, 162	hazard models,	treatment outcome to
				texture features	Integrated Brier	antiangiogenic therapy through
					scores	PFS k (P= 0.030) and OS l (P
						=0.001).
	Colorectal	Huang,	T: 326	150 texture features, 24	LASSO j ; Multivariate	Radiomics nomogram predicts
	cancer	2016 [85]		feature-based radiomics	binary logistic	lymph node metastases
			V: 200	signature; Radiomics	regression,	(CI h =0.78), beneficial in pre-
	CE d CT a			nomogram	nomograms and	treatment decisions.
					calibration plots
	Colorectal	Liang,	T 286	150 texture features;	LASSO j ;	Texture features can be utilized
	cancer	2016 [86]		16-feature-based	the radiomics score	in preoperative staging of
			V: 208	radiomics signature		colorectal carcinomas (AUC i =
						0.71).
	CE d CT a
	Oropharyngeal	Leijenaar,	V: 542	radiomics signature	Cox proportional	Results show the applicability
	Squamous cell	2015 [88]		(energy, Compactness,	hazards model	as a prognostic index of a
	Cancer			Gray level non-uniformity,		radiomics signature which was
	(OPSCC)			Wavelet)		trained in lung and H&N and
						cancer validated well in an
	CTa					external cohort of OPSCC (CI h
						= 0.63)
	Breast Cancer	Li,	T: 84	38 CEIPs m (size, shape,	Leave one-case-out	CEIPs predict the recurrence
	(TCGA -TCIA e	2016 [121]		morphology,	cross-validation	risk as assessed by Oncotype
	dataset)			enhancement texture,	analysis with logistic	Dx, PAM50 or Mammaprint
				kinetic curve,	regression	(AUCs i from 0.55 to 0.88)
	CE d MRI b			enhancement-variance
				kinetics)
	* a CT:

Table 4 . Publications on the prediction of treatment response through radiomics

 4 a CT: Computed tomography scan, b MRI: Magnetic resonance imaging, c PET: Positron emission tomography, d CE: Contrast-enhanced, e TCGA -TCIA: The Cancer Genome Atlas-The Cancer Imaging Archive, f NSCLC: Non-small cell lung cancer, g GLCM: gray level co-concurrence matrix, h SABR: Stereotactic ablative body radiotherapy i EGFR: epidermal growth factor receptor, j AUC: area under the curve, k T2 Weighted MRI, l ADC: apparent diffusion coefficient, m AUROC: Area under the receiver operating characteristic

	Tumor type	Author,	Training/	Extracted radiomics	Feature selection and	Utility and Significance
	and imaging	Year	validation	features	statistical learning
	modality		Sets (N)
	Lung adeno-	Aerts,	T: 47	183 initial features,	Spearman rank statistic,	Radiomics features predict EGFR i
	carcinomas	2016 [92]		11 independent features	AUC, intraclass correlation	mutation status and associated
			V: 31 (Rider	used (volume, Gabor	coefficient	response to Gefitinib in baseline
	High resolution CT a		Test-retest)	Energy, Sigmoid Function, Shape Index, Boundary Radius,		(AUC j = 0.67) and in change in pre-and post treatment (AUC i = 0.74-0.91) CT scans of NSCLC
				GLCM g , Laws Energy)
	Lung adeno-	Y Liu,	T: 385	Semantic radiologic	Multiple logistic regression	CT features of lung adeno-
	carcinomas	2016 [91]		features (location, size, shape, margin,	analyses, backward elimination	carcinomas can be an image biomarker for EGFR i mutation
	CE d CT a			attenuation, internal, enhancement, external,	method, AUROC	status, with the use of clinical variables combined with CT
				associated findings)		features (AUROC m = 0.778) being
				assessed by 3		superior to use of clinical variables
				radiologists		alone (AUROC m = 0.690)
	NSCLC	Cook,	T: 47	First-order and high-order	Cox and logistic regression	Reduced quantitative heterogeneity
		2015 [95]		primary tumor texture	analyses	features (percentage change in
	PET c			features		entropy) in PET scans are
						associated with time to disease
						progression (p=0.03) and treatment
						response (p=0.01)
	NSCLC	Mattonen,	T: 22	mean density, first-order	Independent samples t-test	First and second-order texture
		2015 [102]		texture, energy, entropy,	with unequal variances,	features can predict eventual
	CE d CT a			correlation, inverse	Kolmogorov-Smirnov test,	cancer recurrence based on CT
				difference moment,	Wilcoxon signed rank test	images acquired within 5 months of
				inertia, cluster shade, and	linear Bayes normal	SABR h treatment (accuracies of 73-
				cluster prominence	classifier, Spearman rank	77%)
					correlation coefficients
	Glioblastoma	Lee,	T: 65	36 spatial habitat	Overall coefficient of	Features had association with
	Multiforme	2015 [89]		diversity (regions with	variation; symbolic	overall survival (AUC=0.74) and
	(TCGA -TCIA e			distinctly different	regression method	EGFR+ (AUC=0.85) status and
	dataset)			intensity characteristics)		could be a useful prognostic tool for
				features based on pixel		MRIs of patients with gloiblastomas
	CE d MRI b			abundances w/in ROIs
	Breast cancer	Michoux,	T: 69	20 texture, 3 kinetic, BI-	Logistic regression model,	Radiomics features predicts
		2015 [93]		RADS and biologic	k-means clustering	response to neoadjuvant
	CE d MRI b			parameters	algorithm based on a	chemotherapy (accuracy = 68%),
					nearest-cluster approach;	may be used in treatment decision-
					leave-one-out cross	making
					validation
	Breast cancer	Ahmed,	T: 100	Texture features based	Mann-Whitney, t-tests	Certain texture parameters are
		2012 [94]		on co-occurrence	Categorized according to	significantly associated with
	CE d MRI b			matrices (Haralick	their chemotherapeutic	treatment response (best-
				features, cluster shade,	response and	performing features p= 0.039-0.048)
				cluster prominence)	histopathology	and tumor histology (best-
						performing features p=0.001-0.012)
	Rectal cancer	Nie,	T: 48	103 imaging features	Mean-value based and	Features reflect response to
		2016 [49]		(texture, shape,	voxelized analysis	neoadjuvant therapy (AUC = 0.71 to
	CE d MRI b			histogram)	techniques; three-layer	0.79 in voxelized analysis) and
					perceptron artificial neural	could influence treatment plans
					network, feed-forward
					-back-propagation learning
	Rectal cancer	Bunds-	T: 27	First-order primary tumor	ROC analysis, Youden	Texture features reflect response to
		chuh,		texture features	index	neoadjuvant chemoradiotherapy
	PET c	2014 [97]		(coefficient of variation,		and prognostic capability for
				skewness, kurtosis)		disease progression (AUC = .89 for
						the coefficient of variation feature)

*

Table 5 . Issues and solutions for radiomics studies STEP PITFALL SOLUTION Image acquisition and reconstruction

 5 Differences in acquisition parametersNeed for standardized protocols adapted to each modality (CT, PET, MRI); and comprehensive description of the parameters being used.Exclusion of images with outlier acquisition parameters. Prinicipal Component Analysis, b Independent Component Analyses, c Locally Linear Embedding

		Contrast enhancement protocols vary across machines	Standardized control ROIs such as muscle
		and across patients (sarcopenia, adipose level, heart
		rate, etc).
	Image Segmentation	Intra/inter-observer variability	Semi-automatic	segmentation	with	human
			correction/improvement
		Time consuming contouring methods	Development of (semi-)automated contouring methods (ideally
			open source)
	Feature Extraction	Large range of voxel intensities and image noise	Filtering procedure aiming to preserve the signal and reduce the
			unwanted noise
		Different discretization methods producing different	Using fixed bin sizes (absolute discretization)
		results
		Volume dependence	Testing for correlation between radiomics variables and volume
	Statistical Learning	Large number of features, small population (p>>n)	Bonferroni, Benjamini-Hochberg corrections
		resulting into a high probability of false positives results and overfitting, the "curse of dimensionality"	Cross-validation
			Dimensionality reduction through supervised and unsupervised
			(PCA a , ICA b , ISOMAP,LLE c ) techniques
		Feature selection and classification uncertainties,	Advanced machine learning approaches such as neural
		susceptibility to human error	networks
	General	Reproducibility is limited	Publications should include access to raw data, segmented
			ROIs, methods used for feature extraction
			A repository should be initiated containing imaging data,
			radiomics features, extraction software, methods, formulae and
			statistical learning methods

a

Acknowledgments

The authors wish to thank C. Verjat for preparation of the figures.

Funding

The authors of this review received no grant from any funding agency; no grant number is applicable.

Disclosure/ Conflict of interest:

All authors have declared no conflicts of interest.