Florent Tixier
email: <florent.tixier@univ-brest.fr

Catherine Cheze

Le Rest

Mathieu Hatt

Nidal M Albarghach

Olivier Pradier

Jean-Philippe Metges

Laurent Corcos

Dimitris Visvikis

Catherine Cheze Le Rest

Latim

M Decine Nucl Aire

Intratumor heterogeneity characterized by textural features on baseline 18 F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer

Keywords: Aged, Aged, 80 and over, Antineoplastic Combined Chemotherapy Protocols, therapeutic use, Carboplatin, administration & dosage, Cisplatin, administration & dosage, Combined Modality Therapy, Esophageal Neoplasms, radionuclide imaging, therapy, Female, Fluorodeoxyglucose F18, diagnostic use, Fluorouracil, administration & dosage, Humans, Image Enhancement, methods, Image Interpretation, Computer-Assisted, Male, Middle Aged, Positron-Emission Tomography, Prognosis, Radiopharmaceuticals, diagnostic use, Radiotherapy, Conformal, Reproducibility of Results, Sensitivity and Specificity, Treatment Outcome F-FDG PET 18, esophageal cancer, texture analysis, predictive value, response to therapy

Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts

response to concomitant radiochemotherapy in esophageal cancer.

Introduction

Esophageal cancer is associated with high mortality. In patients with early stage disease at presentation, esophagectomy is the treatment of choice and is potentially curative. Unfortunately most patients at presentation have already locally advanced esophageal cancer (LAEC) or distant metastases. In LAEC, preoperative chemotherapy or radio-chemotherapy will improve survival in patients who respond to induction therapy [START_REF] Gebski | Survival benefits from neoadjuvant chemoradiotherapy or chemotherapy in oesophageal carcinoma: a meta-analysis[END_REF][START_REF] Cunningham | MAGIC Trial Participants: Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer[END_REF]. On the other hand, patients who do not respond to neoadjuvant therapy may be affected unnecessarily by the toxicity of an inefficient therapy. Therefore the development of a diagnostic test capable of predicting non-invasively response to therapy early in the course of treatment is of great interest, potentially allowing personalization of patient management. In patients treated by exclusive conventional combined radio-chemotherapy, assessment of response is equally of great interest, since it could allow an early change in the management of non-responding patients. Such assessment becomes more critical when one considers the availability of new targeted therapies that could be tested with higher efficiency if applied early in diagnosis [START_REF] Fabio | The predictive value of 18F-FDG PET early evaluation in patients with metastatic gastric adenocarcinoma treated with chemotherapy plus cetuximab[END_REF][START_REF] Krause | 18F-FDG PET and 18F-FDG PET/CT for Assessing Response to Therapy in Esophageal Cancer[END_REF]. F-FDG Positron Emission Tomography (PET) is already well established for the initial staging of esophageal cancer, since it is 18 associated with a better sensitivity and specificity than combined use of CT and echo-endoscopy especially regarding detection of distant metastasis [START_REF] Van Westreenen | Systematic Review of the Staging Performance of 18F-Fluorodeoxyglucose Positron Emission Tomography in Esophageal Cancer[END_REF].

F-FDG PET imaging has been also used to assess response to therapy and patient outcome prognosis [START_REF] Krause | 18F-FDG PET and 18F-FDG PET/CT for Assessing Response to Therapy in Esophageal Cancer[END_REF][START_REF] Ben-Haim | 18F-FDG PET and PET/CT in the Evaluation of Cancer Treatment Response[END_REF]. Within this context, few [START_REF] Rajendran | Tumour hypoxia imaging with 18F fluoromisonidazole positron emission tomography in head and neck cancer[END_REF] studies have explored the potential prognostic value of a pre-treatment F-FDG PET scan, demonstrating that the level of activity 18 concentration on pre-operative PET, although not statistically significant, tends to predict overall survival (7 9).

-On the other hand, several studies have evaluated the role of PET in assessing treatment response based on FDG uptake changes between a pre-and a post-treatment PET scan performed during or after the treatment completion. Studies considering a second PET scan after treatment completion have shown that a complete metabolic response is associated with better outcome (10 12). However, since that information is of limited interest in patient management if acquired late, different attempts have been made to determine whether F-FDG [START_REF] Rajendran | Tumour hypoxia imaging with 18F fluoromisonidazole positron emission tomography in head and neck cancer[END_REF] PET could be used for assessing response to therapy earlier (usually within a few weeks) in the course of treatment (13 15), showing some promising results that need to be confirmed in multicenter trials [START_REF] Krause | 18F-FDG PET and 18F-FDG PET/CT for Assessing Response to Therapy in Esophageal Cancer[END_REF]. One of the highlighted issues is that early response prediction during combined chemo-radiotherapy, in contrast to chemotherapy alone, may be compromised by increased FDG tumor uptake resulting from radiotherapy induced inflammatory processes ( 4).

An alternative to monitoring changes during treatment is the potential of predicting response to therapy from the baseline F-FDG 18 PET scan alone, which may allow the best available therapy regime to be chosen for a given patient. However, to date there is only limited evidence that a measure of tumor activity concentration in a baseline PET scan in esophageal cancer can allow differentiating between different groups of patient response [START_REF] Javeri | Influence of the baseline 18FDG positron emission tomography results on survival and pathologic response in patients with gastroesophageal cancer undergoing chemoradiation[END_REF][START_REF] Rizk | Predictive value of initial PET SUVmax in patients with locally advanced esophageal and gastroesophageal junction adenocarcinoma[END_REF]. Within the same context, parameters derived from pre-treatment F-FDG PET has shown the 18 potential to differentiate between responders and non-responders in non-Hodgkin lymphoma patients [START_REF] Cazaentre | Pre-therapy 18F-FDG PET quantitative parameters help in predicting the response to radioimmunotherapy in non-Hodgkin lymphoma[END_REF].

The PET image index predominantly used in such studies for assessment of metabolic response is the normalized mean tumor activity concentration known as the mean Standardized Uptake Value (SUV ), within a region of interest (ROI) around the tumor, and/or the mean maximum standardized uptake value corresponding to the highest activity pixel value (SUV ). However, FDG tumor uptake has been max associated not only with increased metabolism, but also with several other physiological parameters such as perfusion, cell proliferation [START_REF] Vesselle | Lung cancer proliferation correlates with 18FDG uptake by positron emission tomography[END_REF], tumor viability, aggressiveness or hypoxia [START_REF] Rajendran | Tumour hypoxia imaging with 18F fluoromisonidazole positron emission tomography in head and neck cancer[END_REF][START_REF] Kunkel | Overexpression of Glut-1 and increased glucose metabolism in tumours are associated with a poor prognosis in patients with oral squamous cell carcinoma[END_REF] all of which may in turn be responsible for tumor uptake heterogeneity. Therefore the hypothesis can be made that characterizing tumor FDG distribution, through its relationship to underlying tumor biological characteristics, may be useful in predicting therapy response. FDG tumor activity distribution may be assessed in a global, regional, and/or local fashion allowing in turn assessing corresponding global, regional or local patterns of biological heterogeneity. Although the measurement of such features have been previously explored in anatomical imaging (20 22), they have not to date been widely used in -PET imaging. Until now only one study has considered the use of some textural features to predict treatment outcome from baseline 18 F-FDG PET images with encouraging results in cervix and head and neck cancer [START_REF] El Naqa | Exploring feature-based approaches in PET images for predicting cancer treatment outcomes[END_REF], while the assessment of spatial heterogeneity was also shown to be significantly associated with survival in sarcoma patients [START_REF] Eary | Spatial Heterogeneity in Sarcoma 18F-FDG Uptake as a Predictor of Patient Outcome[END_REF]. However, the potential predictive value of tumor heterogeneity characterization on a baseline F-FDG PET scan has never been assessed.

The objective of this current study was therefore to assess the predictive value of FDG uptake heterogeneity characterized by textural features extracted from pre-therapy F-FDG PET images of patients with esophageal carcinoma by assessing the ability of each parameter 18 to identify different categories of responders. The predictive value of these parameters was compared to the use of standard image activity concentration indices (SUV , SUV ). The potential prognostic value of such image derived parameters for assessing overall patient max mean survival was not assessed in this study. beam radiotherapy and chemotherapy with alkylatin-like agents (5FU-cisplatin or 5FU-carboplatin). A median radiation dose of 60Gy was delivered in 180cGy daily fractions (5 days a week and 6 7 weeks in total). One month after the completion of the treatment, patients were reassessed in order to determine response to therapy using thoraco-abdominal CT and endoscopy. Patients were subsequently classified as complete responders (CR), partial responders (PR), stable disease (SD) or progressive disease (PD). Response was assessed using pre-treatment and post-treatment CT scans by evaluating the increase (or decrease) in the sum of the longest diameters for all target lesions and the appearance, persistence or disappearance of non-target lesions, according to the RECIST criteria [START_REF] Therasse | New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada[END_REF]. Considering the small number of patients in the SD (7) and PD (4) groups, these patients were eventually combined into one non-responders group (NR).

Materials and Methods

All patients underwent a pre-treatment whole-body F-FDG PET scan for staging purposes. Patients were instructed to fast for a 18 minimum of six hours before the injection of 18F-FDG. The dose of administered 18F-FDG was 5 MBq/kg, and static emission images were acquired from thigh to head, on average 54 minutes after injection, on a Philips GEMINI PET/CT (Philips Medical Systems, Cleveland, OH USA). In addition to the emission PET scan, a low dose CT scan was acquired for attenuation correction purposes. Images were reconstructed with the RAMLA 3D algorithm using standard clinical protocol parameters (2 iterations, relaxation parameter of 0.05 and a 5mm FWHM 3D Gaussian post-filtering). The current data analysis was carried out after an approval by the institutional review board.

Tumor analysis

For each patient primary tumors were identified on F-FDG PET images by an experienced nuclear physician. Tumor delineation was 18 then performed automatically using the previously validated Fuzzy Locally Adaptive Bayesian (FLAB) algorithm [START_REF] Hatt | A Fuzzy Locally Adaptive Bayesian segmentation Approach for Volume Determination in PET[END_REF]. All parameters were subsequently extracted from this delineated volume. Only the primary tumors were considered because texture analysis cannot be reliably performed on small lesions (nodal or distant metastases) due to the small number of voxels involved.

SUV analysis

The following SUV parameters were extracted from each patient s baseline PET images: maximum SUV (SUV ), peak SUV (SUV ' max ) defined as the mean of the voxel of maximum value and its 26 neighbors (in 3D), and mean SUV within the delineated tumor (SUV peak ). The SUV was considered in addition to SUV in order to investigate the impact of reducing the potential bias in the SUV mean peak max max measurements as a result of its sensitivity to noise.

Texture analysis

We define as texture a spatial arrangement of a predefined number of voxels allowing the extraction of complex image properties, and a textural feature as a measurement computed using a texture matrix. The method used was realized in two steps. First, matrices describing textures on images were extracted from tumors and textural features were subsequently computed using theses matrices. All these parameters characterize in some way tumor heterogeneity at local and regional (using texture matrices) or global scales (using image voxel intensity histograms).

A number of different textures listed on table 2 (left column) were computed. Voxel values within the segmented tumors (see figure 1(A) and 1(B)) were resampled to yield a finite range of values (see figure 1(C)) allowing texture analysis using: From each of the extracted texture matrices, different features summarized in table 2 (middle column) were computed. Depending on the way the matrix is analyzed, it is possible to extract features of local or regional nature. Six features highlighting local variations of voxel intensities within the image were extracted from the co-occurrence matrices M1 (see figure 2C). For example, using the matrices M1, the local entropy and homogeneity are calculated using equations ( 2) and (3) respectively:

(2)

(3)
where, M1 is a co-occurrence matrix, i, j, are the rows and columns index and M1(i,j) is an element of the matrix.

In addition, M3 matrices were used to extract busyness (quantifying sharp intensity variations), as well as contrast and coarseness (quantifying tumor granularity). These features allow extracting measurements describing tumor local heterogeneity proportional to variations of FDG uptake between individual voxels.

On the other hand, the M2 and M4 matrices were used to extract regional tumor uptake characteristics, representing regional heterogeneity such as variation of intensity between regions as well as in the size and alignment of homogeneous areas. For example, the M4 matrix links the homogeneous tumor regions to their intensity (see figure 2B). It was hence used to calculate the variability in the size and the intensity of identified homogenous tumor zones according to equations ( 4) and ( 5) respectively: A total of 38 features were extracted from the four different texture matrices and intensity histograms. Seven characterize the uptake distribution within the entire tumor (using the intensity histogram), while nine describe local voxel arrangements (using matrices M1 and M3), and 22 are related to the organization of voxels at a regional scale (using matrices M2 and M4).

Statistical Analysis

The capacity of each feature to classify patients with respect to therapy response was investigated on the primary tumor using the Kruskal-Wallis test [START_REF] Javeri | Influence of the baseline 18FDG positron emission tomography results on survival and pathologic response in patients with gastroesophageal cancer undergoing chemoradiation[END_REF]. P-values <0.05 were considered statistically significant. Specificity and sensitivity (including 95 confidence % intervals, CI) for each of the studied parameters were derived using Receiver Operating Characteristic (ROC) curves measuring associated areas under the ROC curves (AUC). Textures results were compared to those of SUV , SUV and SUV for their ability to max mean peak distinguish between responders (PR and CR) and non responders, between CR and non-CR (PR, NR), and between all three groups separately.

Results

Patients were evaluated one month after the completion of combined radio-chemotherapy. 9 patients [START_REF] Segal | Decoding global gene expression programs in liver cancer by noninvasive imaging[END_REF] had no evidence of disease % after treatment and were considered as complete responders. Radio-chemotherapy led to partial response in 21 (51 ) patients, while 11 (27

%
) were stable or progressed under treatment according to the RECIST criteria [START_REF] Therasse | New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada[END_REF].

%

Results of the Kruskal-Wallis test show that SUV (see figure 3) and SUV were capable of only differentiating CR from NR and max mean PR patients. Within this context all SUV measurements were significant predictive factors of response (p 0.034, 0.044 and 0.012 for the = SUV , SUV and SUV respectively). However, only SUV was a significant predictive factor (p 0.045) when considering the max mean peak peak = differentiation of three patient response groups (i.e. NR, PR and CR), while SUV and SUV were not (p>0.05).

max mean

Figure 4 shows examples of different extracted features and associated values for tumors of complete, partial and non-responding patients. The Kruskal-Wallis tests revealed no statistically significant differences in the textural parameters derived using different resampling values [START_REF] Cazaentre | Pre-therapy 18F-FDG PET quantitative parameters help in predicting the response to radioimmunotherapy in non-Hodgkin lymphoma[END_REF][START_REF] Erasmus | Preoperative chemo-radiation-induced ulceration in patients with esophageal cancer: a confounding factor in tumor response assessment in integrated computed tomography-positron emission tomography imaging[END_REF]64 or 128 discrete values). All subsequent reported results were obtained using 64 discrete values in the resampling normalisation process. This value was chosen as it allows for 0.25 SUV increments which were considered sufficient given the range of SUV values encountered (from ~4 to 20).

None of the global features extracted from the intensity histogram within the tumor was a significant predictive factor of response to therapy. However, considering local variation of FDG uptake, a high predictive value (p<0.0007) was found using the co-occurrence features, particularly considering the use of the average feature values computed using M1 matrices (see table 3). All these features offered statistically significant differentiation of non-responders and responders (considering both CR and PR).

Regarding local features, the busyness and contrast computed on M3 matrices were not statistically significant predictive factors of response but the coarseness, reflecting the local granularity of the tumor functional image, was found to be significant (p 0.0002).

=

Amongst the local measures of functional tumor characteristics computed using M1 matrices the measure of local entropy was the only one allowing statistically significant differentiation of all three patient groups (p 0.0006, see figure 3).

=

Since the features computed on M2 and M4 matrices, used to highlight regional variability in the FDG distribution, were strongly correlated (r>0.9), only features based on M4 were used in the subsequent analysis. Regional measures of tumor characteristics extracted from these M4 matrices, such as the variability in the size and the intensity of identified homogenous tumor zones were statistically significant in predicting therapy response (p 0.0002) allowing to differentiate all three patient response groups (see figure 3).

=

The ROC curve analysis for SUV , SUV , local homogeneity, local entropy, and regional tumor characteristics such as the max peak variability in size and intensity of identified homogeneous tumor areas are presented in figure 5. Table 3 summarizes the ROC curve analysis results comparing the performance of the different studied parameters in terms of sensitivity and specificity in identifying on the one hand complete response patients and on the other differentiate responders (PR and CR).

Firstly, based on the ROC curve analysis textural parameters can identify CR patients better than the SUV based measurements, as demonstrated by the respective AUCs (figure 5). For example, SUV , with an AUC 0.7, allowed indentifying CR with a maximum max = sensitivity of 46 and specificity of 91 using a threshold of 6. On the other hand, the variability in the size of the uniform tumor zones % % (AUC 0.85) allowed extracting CR patients with the best accuracy (sensitivity of 92 and specificity of 69 ).

= % %

Secondly, as figure 5 shows, textural features were most efficient in identifying responders (CR and PR patients), while for the same task the performance of SUV measurements was limited. For the differentiation of the patient responders the AUC was <0.6 for the different SUV parameters considered in comparison to an AUC of >0.82 for the use of the texture parameters. For example, the AUC of the SUV was 0.59 allowing a sensitivity of 53 and specificity of 73 in the differentiation of responders using an optimal threshold of max % % 9.1. On the other hand, for the same task the local homogeneity had a specificity and sensitivity of 88 and 73 respectively (AUC 0.89).

% % =

Discussion

Assessment of tumor response to therapy plays a central role in drug development as well as in patient clinical management. Currently the response is mainly assessed by measuring anatomical tumor size and classifying tumor shrinkage according to standard criteria. Since metabolic changes often occur before morphological changes, metabolic imaging appears to be a valuable tool for monitoring various treatments in different cancer types. Within this context F-FDG PET has shown promising results in assessing response to therapy and 18 prognosis. In esophageal cancer, quantitative changes in FDG uptake two weeks after the start of therapy have been shown to correlate well with subsequent tumor shrinkage and patient survival (4). This approach has still limitations especially if patients undergo radiotherapy treatment. Hautzel et al. have shown that even low irradiation may enhance tumor uptake and inflammatory changes may contribute very early to this increase, yielding inaccurate information about treatment response [START_REF] Hautzel | Early changes in fluorine-18-FDG uptake during radiotherapy[END_REF]. Within the same context, induced ulceration may also impair response assessment using PET [START_REF] Erasmus | Preoperative chemo-radiation-induced ulceration in patients with esophageal cancer: a confounding factor in tumor response assessment in integrated computed tomography-positron emission tomography imaging[END_REF].

On the other hand, the prediction of response prior to treatment initiation may be of great interest optimizing patient management.

With such an endpoint few authors have studied the predictive value of initial FDG uptake for therapy response. Rizk et al. reported an SUV more than 4.5 to be a reliable predictor of pathologic response (9), while Javeri et al. [START_REF] Javeri | Influence of the baseline 18FDG positron emission tomography results on survival and pathologic response in patients with gastroesophageal cancer undergoing chemoradiation[END_REF] demonstrated in a larger group of patients max a trend of greater rate of response obtained after combined chemo-radiotherapy in patients who had an initial SUV higher than 10.

max Similarly in our study, initial SUV , SUV and SUV were also predictors of complete response. However, in general these indices mean max peak did not allow differentiating non-responding patients from partial responders, a distinction that could be useful for patient management.

For instance, within the patient population of our study the identification of partial responders before any treatment could allow the definition of a subpopulation for which the use of conventional radio-chemotherapy should be directly replaced by another option, such as for example a new targeted therapy.

A few studies have already focused on the link between image analysis and tumor biological parameters. Gillies et al. [START_REF] Gillies | The biology underlying molecular imaging in oncology: from genome to anatome and back again[END_REF] suggested that imaging can longitudinally characterize spatial variations in the tumor phenotype and its microenvironment so that the system dynamics over time can be quantitatively captured. In our study, the value of textural feature analysis was explored on the pre-treatment F-FDG PET scans for predicting response to 18 combined chemo-radiotherapy. Global tumor metabolic features based on intensity histogram were computed directly on the original image. As such they were therefore highly correlated with FDG uptake which could explain why these textures could only predict complete responders but could not identify non-responders from partial responders similarly to SUV measurements. The other features evaluated in this study highlight tumor heterogeneity at a local and regional level, characterized in several ways depending on the type of matrix used and the kind of feature computed on this matrix. Consequently, whereas a single feature cannot be directly linked to a specific biological process, one could assume that a combination of textural parameters may be closely related to underlying physiological processes, such as vascularization, perfusion, tumor aggressiveness or hypoxia [START_REF] Rajendran | Tumour hypoxia imaging with 18F fluoromisonidazole positron emission tomography in head and neck cancer[END_REF][START_REF] Kunkel | Overexpression of Glut-1 and increased glucose metabolism in tumours are associated with a poor prognosis in patients with oral squamous cell carcinoma[END_REF]. Therefore textural features could be correlated to physiological processes related with response of combined radio-chemotherapy. For example, one could reasonably expect that a tumor exhibiting a heterogeneous, in comparison to a homogeneous, FDG distribution may respond less favorably to a uniformly distributed radiotherapy dose. We could also hypothesize that underlying neoangiogenesis contributes to tumor FDG uptake heterogeneity while it is now widely accepted that neoangiogenesis is associated with reduced effectiveness of conventional chemotherapy. However the exact relationship between the proposed image derived indices and underlying tumor biology can only be established on carefully designed prospective studies.

In this work, the co-occurrences features analyzing inter-relationships between pairs of voxels, corresponding to the characterization of local non-uniformities, were able to significantly differentiate non-responders from other patient groups. The measurement of local homogeneity and entropy gave the best results for this class of textures. Although in most of the cases responders (PR and CR) were associated with greater local heterogeneity than NR, these features were less efficient in discriminating CR from PR.

The two features facilitating the best patient stratification were both associated with regional tumor characterization. Both the intensity and size variability of uniform zones identified within the tumor, representing a measure of regional tumor heterogeneity, were significant predictors of response to therapy. ROC curve analysis showed that these features can identify NR patients with similar performance to co-occurrences features, but they can in addition distinguish between PR and CR with higher sensitivity and specificity than SUV measurements. These results suggest that regional (in terms of intensity and size of homogenous areas) rather than local heterogeneity offers a superior differentiation of esophageal carcinoma patient groups in terms of response to combined chemo-radiotherapy treatment than any other global tumor metabolic activity measurement currently used in routine clinical practice, such as SUVs.

A limitation of the present study is that it is retrospective considering a relatively small patient cohort. Therefore the potential of new image derived indices characterizing tumor FDG distribution for prediction of response to therapy studies demonstrated in this work, need to be validated by a prospective study on a larger patient cohort.

Conclusion

We have demonstrated that textural analysis of the intra-tumor tracer uptake heterogeneity on baseline F-FDG PET scans can predict 18 response to combined chemo-radiation treatment in esophageal cancer. Textural features derived from co-occurrence matrices strongly differentiated non-responders from partial responders, providing useful information for personalizing patient management. These results suggest that regional and local characterization of F-FDG PET tracer heterogeneity in tumors, exploring processes underlying the FDG 18 uptake and distribution within tumors, are more powerful than global measurements currently employed in clinical practice, holding the potential to revolutionize the predictive role of PET imaging in cancer treatment. Finally, although only FDG images in esophageal cancer have been considered here, clearly the same indices applied in other PET radiotracer studies in the same or different tumor types may help creating even stronger links between imaging and underlying tumor biology. difference between voxels and its neighbors, and (d). zones of voxels with the same intensity.

Figure 2

Examples of features extracted from a tumor resampled on 4 values: three global features computed using the intensity histogram, two regional features computed using the M4 matrix and two local features computed using the M1 texture matrices.

( 1 )

 1 where, 2 represents the number of discrete values (16 to 128), I is the intensity of the original image and is the set of voxels in the S Ω delineated volume. This resampling step on the delineated tumor volume, necessary for the computation of the textural analysis, has two effects: it reduces the noise in the image by clustering voxels with similar intensities and it normalizes the tumor voxel intensities across patients, which in turn facilitates the comparison of the textural features. Local and regional features were computed with different resampling considering 16, 32, 64 and 128 discrete values to investigate the potential impact of this resampling parameter. All considered textures were originally described for 2D (27 30) and were therefore adapted in this work for 3D. The co-occurrence matrix (M1, figure 1(D)a) describing pair-wise arrangement of voxels, and the matrix describing the alignment of voxels with same intensity (M2, figure 1(D)b) were computed considering 13 different angular directions. Finally, 3D matrices describing differences between each voxel and its neighbors (M3, figure 1(D)c) and characteristics of homogenous zones (M4, figure 1(D)d) were computed considering for each voxel the neighbors in the two adjacent planes adapting the normalizing factors to 3D.

( 5 )

 5 where, represents the number of homogeneous areas in the resampled tumor, M the number of distinct intensity values within the Θ tumor and N the size of the largest homogeneous area in the matrix M4.Finally, global features are computed on the original image voxels intensity distribution by analyzing the characteristics of the intensity values histogram within the segmented tumor (see figure2A).

Figure 1 (

 1 Figure 1 (A). Whole-body F-FDG PET scan (B). tumor segmentation (C). voxel intensity resampling allowing (D). the extraction of different 18features by analysis of (a). consecutive voxels in a direction (for co-occurrence matrices), (b). alignment of voxels with same intensity, (c).

Figure 3 Figure 4

 34 Figure 3 Box-plot representation of parameters values in function of patient response (0: non-responder, 1: partial responder, 2: complete responder) for (a). SUV (p 0.106), (b). SUV (p 0.045), (c). local entropy (p 0.0006), (d). regional intensity variability (p 0.0002). max = peak = = =

Figure 5

 5 Figure 5 ROC curves for the SUV , SUV , SUV , local homogeneity, uniform tumor areas intensity variability and size-zone variability, for max mean peak identification of (a). CR patients, and (b). responders (PR or CR)

  Segal et al. (22) showed that contrast-enhanced CT image characteristics (such as texture heterogeneity score or estimated percentage of necrosis correlate with most of the liver global gene expression profiles, revealing cell proliferation, liver synthetic function, and patient prognosis. Within the same context, Diehn et al. (34) mapped neuroimaging parameters with gene-expression patterns in glioblastoma, while Strauss et al (35) combined dynamic PET kinetic parameters with gene array techniques. Finally, Eary et al. (20) previously demonstrated that a globally assessed FDG distribution heterogeneity in sarcoma is a potential prognostic factor.

Table 1

 1 Patients Characteristics

	Characteristics

[START_REF] Kim | Value of complete metabolic response by 18F-fluorodeoxyglucose-positron emission tomography in oesophageal cancer for prediction of pathologic response and survival after preoperative chemoradiotherapy[END_REF]

[START_REF] Kim | Value of complete metabolic response by 18F-fluorodeoxyglucose-positron emission tomography in oesophageal cancer for prediction of pathologic response and survival after preoperative chemoradiotherapy[END_REF]

[START_REF] Kim | Value of complete metabolic response by 18F-fluorodeoxyglucose-positron emission tomography in oesophageal cancer for prediction of pathologic response and survival after preoperative chemoradiotherapy[END_REF]

[START_REF] Kim | Value of complete metabolic response by 18F-fluorodeoxyglucose-positron emission tomography in oesophageal cancer for prediction of pathologic response and survival after preoperative chemoradiotherapy[END_REF]

[START_REF] Kim | Value of complete metabolic response by 18F-fluorodeoxyglucose-positron emission tomography in oesophageal cancer for prediction of pathologic response and survival after preoperative chemoradiotherapy[END_REF]

è ô ' One of us (Florent Tixier) is supported by a fellowship from the French Ministry of Education and Research.

[START_REF] Kim | Value of complete metabolic response by 18F-fluorodeoxyglucose-positron emission tomography in oesophageal cancer for prediction of pathologic response and survival after preoperative chemoradiotherapy[END_REF]

[START_REF] Kim | Value of complete metabolic response by 18F-fluorodeoxyglucose-positron emission tomography in oesophageal cancer for prediction of pathologic response and survival after preoperative chemoradiotherapy[END_REF]

Acknowledgements:

This study was supported by a grant from the Ligue Contre le Cancer (Finist re and C tes d Armor Committees), and the IFR148-ScInBioS.

 11 12PR SD (NR) 7 [START_REF] Vesselle | Lung cancer proliferation correlates with 18FDG uptake by positron emission tomography[END_REF] PD (NR) 4 (10) CR: complete responder. PR: partial responder. SD: stable disease. PD: progressive disease. NR: non-responder


Mathieu Hatt
email: hatt@univ-brest.fr

PhD Catherine Cheze Le Rest

PhD, MD Nidal Albarghach

MD Olivier Pradier

PhD, MD Dimitris Visvikis

Hatt Mathieu

Latim

PET functional volume delineation: a robustness and repeatability study

Keywords: PET uptake volume determination, robustness, repeatability, FLAB, thresholding

Purpose: Current state of the art algorithms for functional uptake volume segmentation in PET imaging consist of threshold-based approaches, whose parameters often require specific optimization for a given scanner and associated reconstruction algorithms. Different advanced image segmentation approaches previously proposed and extensively validated, such as among others the fuzzy Cmeans (FCM) clustering, or the Fuzzy Locally Adaptive Bayesian (FLAB) have the potential to improve robustness of functional uptake volume measurements. The objective of this study was to investigate robustness and repeatability with respect to various scanner models, reconstruction algorithms and acquisition conditions.

Methods and materials: Robustness was evaluated using a series of IEC phantom acquisitions carried out on different PET/CT scanners (Philips Gemini and Gemini Time-of-Flight, Siemens Biograph and GE Discovery LS) with their associated reconstruction algorithms (RAMLA, TF MLEM, OSEM). A range of acquisition (contrast, duration) and reconstruction (voxel size) parameters were considered for each scanner model. On the other hand, the repeatability of each method was evaluated on simulated and clinical tumors and compared to manual delineation.

Results: For all the scanner models, acquisition parameters and reconstruction algorithms considered, FLAB demonstrated higher robustness in delineation of the spheres with low mean errors (10%) and variability (5%), with respect to thresholdbased methodologies and FCM. The repeatability provided by all segmentation algorithms considered was very high with a negligible variability of <5% in comparison to that associated with manual delineation (5-35%).

Conclusion:

The use of advanced image segmentation algorithms may allow not only high accuracy as previously demonstrated, but also provide a robust and repeatable tool to aid physicians as an initial guess in determining functional volumes in PET.

Introduction

Accurate, robust, reproducible and fast delineation of functional tumor uptake volumes in three dimensions using positron emission tomography (PET) has been identified as a pressing challenge for an increasing number of oncology applications, such as image-guided radiotherapy [START_REF] Lucignani | SUV segmentation: pressing challenges in tumor assessment and treatment[END_REF][START_REF] Jarritt | The role of PET/CT scanning in radiotherapy planning[END_REF][START_REF] Pan | PET/CT in radiation oncology[END_REF], diagnosis, prognosis and therapy response assessment [START_REF] Krak | Effects of ROI definition and reconstruction method on quantitative outcome and applicability in a response monitoring trial[END_REF][START_REF] Jerusalem | The value of positron emission tomography (PET) imaging in disease staging and therapy assessment[END_REF]. On the one hand, manual delineation of functional uptake volumes using PET images is tedious and associated with very low repeatability due to high inter-and intra-observer variability [START_REF] Krak | Effects of ROI definition and reconstruction method on quantitative outcome and applicability in a response monitoring trial[END_REF], principally arising from the poor quality of PET images. On the other hand, current state-of-the-art algorithms for functional uptake volume segmentation using PET images consist of fixed [START_REF] Erdi | Segmentation of Lung Lesion Volume by Positron Emission Tomography Image Thresholding[END_REF] or adaptive thresholding approaches [START_REF] Daisne | Tri-dimensional automatic segmentation of PET volumes based on measured source-to-background ratios: influence of reconstruction algorithms[END_REF][START_REF] Nestle | Comparison of Different Methods for Delineation of 18F-FDG PET-Positive Tissue for Target Volume Definition in Radiotherapy of Patients with Non-Small Cell Lung Cancer[END_REF]. Regarding the use of fixed threshold, numerous studies have shown the need for variable threshold, depending on numerous factors, such as among them, lesion contrast, lesion size, and image noise [START_REF] Biehl | 18F-FDG PET definition of gross tumor volume for radiotherapy of non-small cell lung cancer: is a single standardized uptake value threshold approach appropriate?[END_REF]. As a solution, in the case of adaptive thresholding, the applied threshold depends on the measured contrast between the object to delineate and its surrounding background, as well as parameters requiring optimisation on phantom acquisitions. This optimisation has to be performed for each scanner model and associated reconstruction and correction algorithms, making these approaches system-dependent. In addition, recent studies show that even considering the same scanner model, a significant variation of the "ideal" threshold may exist due to differences in clinical acquisition and reconstruction protocols [START_REF] Oellers | The integration of PET-CT scans from different hospitals into radiotherapy treatment planning[END_REF] underlying the possibility that such deterministic approaches may not be sufficiently robust and reproducible for functional uptake volume determination.

Recently several advanced image segmentation algorithms have been proposed in the literature for PET volume delineation [START_REF] El Naqa | Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning[END_REF][START_REF] Montgomery | Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model[END_REF][13][START_REF] Hatt | A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET[END_REF][15][16]. The physical accuracy of these algorithms in differentiating the uptake signal from its surrounding background has, in most cases, already been assessed with respect to ground-truth, provided by a combination of realistic simulated or acquired phantom images as well as, in some cases, clinical tumors with associated histopathology measurements.

However, apart from physical accuracy, different characteristics can be equally important in terms of assessing the performance of such advanced image segmentation algorithms, which in principle have the potential of being more robust and repeatable than "threshold-based" approaches. A robust and repeatable performance may facilitate their use with images acquired on different scanner models without any previous optimization to individual image quality, providing a less hardware dependent solution to the problem of 3D functional uptake segmentation.

However none of these methodologies have been shown to be system independent, considering the potential variability that can be observed in PET image characteristics, depending on the scanner or associated reconstruction and correction algorithms used. Such an evaluation is essential for the efficient application of these approaches to the different clinical applications targeted, not simply within a given institution but also concerning their use within a multi-center trial context. Finally, such a robustness analysis could provide some insight regarding the potential behavior of a given segmentation algorithm considering the use of different tracers. On the one hand, the PET scanner properties in terms of spatial resolution will be similar for acquisitions performed with the same radioisotope, therefore resulting in similar magnitude partial volume effects. On the other hand, acquisitions performed using different radiotracers lead to different properties of uptake intensity and therefore subsequent different contrast and noise level characteristics for a given tumor uptake. For instance, 18F-FLT and 18F-FMISO images are usually characterized by higher noise levels and reduced tumor uptake contrast with respect to what is usually observed in 18F-FDG images [17,[START_REF] Hatt | Reproducibility of 18F-FDG and 18F-FLT PET tumor volume measurements[END_REF]. Therefore, by studying the behavior of automated algorithms dedicated to the delineation of elevated activity in 18F-FDG images, considering variable contrast and noise levels, one could gain an insight on the potential behavior of such algorithms when applied to other 18Flabeled PET tracers.

The objectives of this study were to (i). provide a robustness and repeatability evaluation framework, and (ii). assess within this framework the performance of different advanced and threshold-based segmentation algorithms in delineating elevated activity distributions in a PET image.

Materials and methods

Segmentation algorithms

Threshold-based and more advanced approaches were considered in this work. Two different fixed thresholds were considered, at 42% (T42) and 50% (T50) of the maximum tumor value, using a region growing algorithm with the maximum intensity voxel as seed [START_REF] Krak | Effects of ROI definition and reconstruction method on quantitative outcome and applicability in a response monitoring trial[END_REF]. An adaptive thresholding approach (TSBR) [START_REF] Daisne | Tri-dimensional automatic segmentation of PET volumes based on measured source-to-background ratios: influence of reconstruction algorithms[END_REF] was also included:

1 threshold I a b SBR = + (1)
SBR is the tumor-to-background ratio determined by ROI analysis, and the couple of parameters (a,b) is optimized for each scanner using phantom acquisitions of spheres.

In terms of more advanced image segmentation approaches, the Fuzzy C-Means (FCM) [16] clustering, previously used for functional volume segmentation tasks in both brain and oncology applications [START_REF] Hatt | A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET[END_REF]15,[START_REF] Zhu | segmentation of PET image for brain tumors[END_REF][START_REF] Belhassen | A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET[END_REF], was considered. This algorithm iteratively estimates clusters' "centroids" (centers of mass) in the image, computing a voxel's membership between 0 and 1 to a given cluster depending on the distance between the voxel's value and the clusters' centroids. However, FCM lacks explicit noise and spatial correlation modeling. The second advanced algorithm considered was an unsupervised Bayesian segmentation, known as Fuzzy Locally Adaptive Bayesian (FLAB) [START_REF] Hatt | A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET[END_REF]15]. It computes, for each voxel, a probability of belonging to a given "class" (for instance, tumor, background or a given uptake level within a tumor). This probability takes into account the voxel intensity, spatial correlation with surrounding voxels (the assumption being that voxels of similar intensities and close to each other have higher probability belonging to the same class) as well as the overall statistical distributions in the regions of the image by estimating the mean and variance for each class. FLAB automatically estimates the parameters of interest (number of classes, classes' mean and variance, spatial correlation of each voxel) within a Stochastic Expectation Maximization (SEM) framework [START_REF] Celeux | L'algorithme SEM : Un algorithme d'apprentissage probabiliste pour la reconnaissance mélanges de densités[END_REF]. In order to deal with the inherent blurry properties of PET images due to the limited spatial resolution of the scanners, the algorithm considers that each voxel may contain a mixture of classes by modeling both spatial correlation and statistical distributions with a combination of Dirac "hard" and Lebesgue "fuzzy" measures. This enables a classification of voxels as belonging to what we denote as "hard classes" or "fuzzy transitions", the first referring to fairly homogeneous regions, the second to blurred areas occurring at the frontier between two homogeneous regions. FLAB is therefore able to accurately differentiate if necessary both the overall tumor spatial extent from its surrounding background as well as tumor subvolumes with different uptakes. The accuracy of FLAB has been previously extensively investigated for both homogeneous [START_REF] Hatt | A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET[END_REF] and heterogeneous non spherical tumors [15] and demonstrated satisfactory accuracy even for small (<2 cm in diameter) volumes of interest (both overall tumors or tumor sub-volumes), short acquisition durations (associated with higher noise levels) or low (<4:1) contrast (both for overall tumors with respect to their surrounding background or between a tumor and its smaller sub-volumes).

Accuracy, robustness, repeatability: definitions

For a given segmentation algorithm we define accuracy as the precision of retrieving the true 3D object spatial extent, shape and volume based on the reconstructed activity distribution in a PET image, irrespectively of the correlation between this distribution and the underlying physiological process. Thus an image segmentation algorithm is not expected to differentiate specific from non-specific tracer uptake (for example inflammation and tumor in the case of FDG) if they are of the same intensity. The defined accuracy of each of the methodologies considered, has been determined as in previous studies [START_REF] Hatt | A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET[END_REF]15] by calculating the classification errors (CE, see section 2.4).

We define as robustness the ability of a given methodology to generate accurate segmented volumes under varying acquisition and image reconstruction conditions. This robustness is determined as the variability of the segmentation results when a method is applied without prior optimization on images acquired using various scanners, and for each scanner under various contrast and noise conditions, using different reconstruction and associated correction algorithms. A dataset consisting of multiple phantom acquisitions performed on various scanner models (see section 2.3) was used for this task. These phantom studies were used to assess robustness as they are consistently employed for optimization purposes with most of the functional volume segmentation algorithms.

Within the context of this study, repeatability is defined as the ability of a given algorithm to reach the same result when applied multiple times on a single image. In such a task, deterministic fixed threshold approaches will always give the same result. On the other hand, more advanced methods are susceptible to give different results when applied multiple times on the same image. For example, the adaptive thresholding segmentation may depend on a manually drawn background ROI and may thus result in variable delineations depending on the choice of this ROI. Finally, manual delineation may be considered as the least repeatable, even when considering a single operator (intra-operator variability). In order to compare the performance of the different segmentation algorithms considered in terms of repeatability, we used a series of simulated tumor images [START_REF] Maitre | Incorporating patient specific variability in the simulation of realistic whole body 18F-FDG distributions for oncology applications[END_REF], as well as fifteen different clinical cases (see section 2.3).

Validation studies

Four different PET/CT scanners currently used in clinical practice were considered for the robustness study; namely the Philips Gemini and Gemini TF (Philips Medical Systems, Cleveland, OH USA), the Siemens Biograph (SIEMENS Medical Solutions, Knoxville, USA) and the GE Discovery LS (GE Healthcare, Milwaukee, USA). In each case, acquisitions of the IEC phantom containing spheres of various diameters (10, 13, 17, 22, 28, 37 mm) filled with 18F and placed on a hot uniform background were carried out. A standard protocol was designed to generate the following acquisitions for each scanner model: (a). two different SBR (4:1 and 8:1), (b). three different acquisition durations (1, 2 and 5 min) to study the effect of noise, and (c). two different voxel volumes used in the reconstruction (between 2x2x2mm 3 and 4.3x4.3x4.25mm 3 ). All acquisitions were performed in 3D mode and listmode format facilitating the generation of 1, 2 and 5 minutes realizations from one single five minutes acquisition. In addition to the standard CT acquisition used for attenuation correction, a CT scan at high resolution was acquired for each PET/CT acquisition in order to generate (after registration) a ground-truth defining the true spatial extent (the interior of the sphere) of the tracer uptake at the voxel-by-voxel level [START_REF] Hatt | A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET[END_REF]. This is subsequently used to compute the accuracy of each algorithm through classification errors (see section 2.4).

Routine clinical image reconstruction protocols were used for all scanners. For the Philips GEMINI and GEMINI TF, data were reconstructed using the RAMLA 3D (2 iterations, relaxation parameter of 0.05 and a 5mm FWHM 3D Gaussian postfiltering) and the TF ML-EM algorithm respectively. In the case of the Siemens Biograph and GE Discovery LS, images were reconstructed with Fourier rebinning (FORE) followed by OSEM (4 iterations, 8 subsets (4i/8s) with a 5mm FWHM 3D Gaussian post-filtering and 2i/8s for the Biograph and Discovery systems respectively). All acquisitions were corrected for attenuation (using the corresponding CT image), as well as for scattered and random coincidences. Table I contains a summary of the parameters for each of the datasets obtained using the different scanners considered. Figures 1 and2 illustrate the various images obtained. Note that in the case of the Philips GEMINI acquisitions, the 37mm sphere was not in the same plane as the others, thus appears visually smaller in the selected slice, while the 28mm sphere was missing in the phantom used for the GE Discovery LS acquisitions.

Regarding the repeatability study, two different datasets were used. The first one consisted of ten tumors extracted from a database of realistically simulated PET scans based on clinical whole body images using the NCAT (NURBS cardiac-torso) phantom, a model of the Philips GEMINI scanner and GATE (Geant4 Application for Tomography Emission). The procedure for the generation of these images, reconstructed using OPL-EM (7i/1s) with 4x4x4mm 3 voxels, has been previously described in detail [START_REF] Maitre | Incorporating patient specific variability in the simulation of realistic whole body 18F-FDG distributions for oncology applications[END_REF]. In the second part of the repeatability study a number of clinical cases were selected from datasets acquired on various scanner models: 4 esophagus and 4 follicular lymphoma patients were acquired on the Philips GEMINI PET/CT scanner with 2min acquisition per bed position, 60min after FDG injection of 6MBq/kg. 3 Non-Small Cell Lung Cancer (NSCLC) were acquired on the Siemens Biograph (5min per bed position, 45min after 5MBq/kg of FDG injection) and the GE Discovery LS (3min per bed position, 60min after 5MBq/kg of FDG injection) respectively.

Analysis

For the phantom images used in the robustness study each sphere was processed separately. The images corresponding to the region containing each sphere were segmented in two classes (sphere and background), using each of the methods under evaluation (FCM, FLAB, T42 and T50). A voxel-to-voxel ground-truth based on the corresponding CT datasets as described previously [START_REF] Hatt | A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET[END_REF], was used in the robustness evaluation of the different methodologies considered, through the determination of the segmentation accuracy with the computation of classification errors (CE):

{ } { } | 100 | 1 t t t card t c x CE card t x ≠ = × = (2)
where, t c is the class assigned by the classification of voxel t , and t x is its true class ( 1 t x = for the sphere and 0 t x = for the background) and card{} is the cardinal.

The errors are computed based on all misclassified voxels, either background voxels classified as the sphere or vice versa, divided by the total number of voxels defining the sphere volume.

Mean CE and associated standard deviation (SD) were obtained for each sphere and for each segmentation approach, thus providing a measure of the robustness of the different segmentation algorithms, when applied without specific optimization for a given scanner model or associated reconstruction algorithm under different imaging conditions (contrast and noise). The 10 mm sphere was not included in the analysis because it was not clearly visible in several of the phantom acquisitions and therefore not possible to segment particularly when using 4x4x4mm 3 and 5x5x5mm 3 reconstruction voxel size by any of the segmentation algorithms considered. Adaptive thresholding could not be compared directly with the other methodologies since it is optimized on each of the individual scanner datasets, with the parameters (a,b) optimized for each imaging device shown in table II. However, in order to assess the robustness of such approaches depending on the imaging system used we applied the adaptive thresholding using the parameters optimized on other scanners to the image datasets acquired with the Siemens Biograph.

For the repeatability evaluation, the simulated and clinical tumors were segmented ten times each with FCM, FLAB, and TSBR (fixed thresholding was not included since it always gives the same volume). In addition, manual delineation was carried out by two nuclear medicine experts with similar experience (more than 10 years) and training. More specifically the two experts were instructed to delineate the elevated uptakes in the images by performing ten different slice-by-slice manual delineations for the different lesions considered in a randomised fashion, ensuring a minimum of a week between two consecutive segmentations of the same lesion. All these manual segmentations were carried out under the same conditions of full range contrast display. The mean percentage variability and associated standard deviation with respect to the mean segmented volume was computed for each of the lesions and segmentation approaches across the ten executions and across the ten manual delineations, in order to assess the repeatability of the approaches for each of the images. The repeatability of the manual delineations from the two experts were compared separately (intra-observer variability) and to each other (inter-observer variability). For the entire range of sphere sizes (37 to 13 mm), better accuracy and variability through smaller overall mean errors and SD can be seen for the FLAB algorithm (8.7±4.5%) relative to the other advanced segmentation algorithm (27.8±25.6% for FCM) as well as relative to the fixed threshold approaches (20.3±18.5% and 42.6±51.6% for T50 and T42 respectively). These latter were also more sensitive to variations of the parameters as shown in figure 4(a). The results suggest that T50 is clearly more robust than T42 (SD of 19% compared to 52%). This is explained by the fact that the 50% threshold is more restrictive and hence leads to smaller over-estimation for the smallest spheres volumes, that the 42% threshold may grossly over-estimate (>100% errors for the most challenging imaging conditions). On the other hand, T50 leads to larger CE for the two larger spheres, as it tends to under-estimate their volumes by only including the central high intensity voxels of the sphere. Considering the FCM algorithm, the results demonstrate that it is unable to accurately segment spheres smaller than 2cm in diameter, leading to large overall mean errors when considering the performance over all of the sphere sizes, although it exhibits lower variability than the fixed threshold approaches for the majority of the spheres with a size >2cm. As Figure 4(b) demonstrates, whereas FLAB exhibits small variability with respect to contrast changes, all other methodologies, especially T42 and FCM exhibit higher sensitivity to such changes.

Results

Classification

T50 on the other hand, is less sensitive to contrast changes with respect to the mean error but exhibits larger variability for lower contrast. Figure 4(c) illustrates the resilience to shorter acquisition (hence higher noise levels) for each methodology.

FLAB demonstrates very low variability with shorter acquisitions, whereas all other methodologies show higher variability with significantly larger mean errors and standard deviations. Finally, only small improvements were found for each methodology (except for T50) when using smaller voxels (see figure 4(d)).

The the simulated and the clinical datasets. FCM also lead to satisfactory repeatability results (0.8±0.6% on simulated tumors and 1.7±1.9% on clinical cases). In comparison, the use of the TSBR led to more than twice as high variability (3.4±2.8% and 3.8±3.1% for the simulated tumors and clinical cases respectively) which is most certainly due to the background ROI manual definition. By contrast manual segmentation performed by the two experts showed high intra-observer variability on simulated tumors (13.4±17.3% and 11.7±18.4% for expert 1 and 2 respectively), and even larger variability on clinical images (19.6±15.2% and 22.1±13.6% for expert 1 and 2 respectively). Inter-observer variability (variability of observer 2 with respect to mean volume of observer 1) was 16.4±21.8% and 24.7±17.6% for the simulated tumors and clinical cases respectively. Figure 4 illustrates one example of some of the delineations obtained by the manual segmentation and automatic approaches.

Discussion

Functional tumor uptake delineation represents today an area of interest for multiple clinical (routine and research) applications of PET imaging, such as response to therapy studies and radiotherapy treatment planning. In all of these applications, the robustness and repeatability with which functional uptake volumes can be determined under different imaging conditions a predominant role, allowing a level of confidence to be established in the use of such tumor volume in clinical practice [START_REF] Hatt | Reproducibility of 18F-FDG and 18F-FLT PET tumor volume measurements[END_REF]. Although several promising advanced algorithms have been recently proposed [START_REF] El Naqa | Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning[END_REF][START_REF] Montgomery | Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model[END_REF][13][START_REF] Hatt | A fuzzy Bayesian locally adaptive segmentation approach for volume determination in PET[END_REF][15][START_REF] Belhassen | A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET[END_REF], methodologies currently used in clinical practice are based on the use of manual delineation or fixed and adaptive thresholding [START_REF] Erdi | Segmentation of Lung Lesion Volume by Positron Emission Tomography Image Thresholding[END_REF][START_REF] Daisne | Tri-dimensional automatic segmentation of PET volumes based on measured source-to-background ratios: influence of reconstruction algorithms[END_REF][START_REF] Nestle | Comparison of Different Methods for Delineation of 18F-FDG PET-Positive Tissue for Target Volume Definition in Radiotherapy of Patients with Non-Small Cell Lung Cancer[END_REF]. The major drawback of manual delineation is its high inter-and intra-observer variability in addition to being time consuming. On the other hand, the currently considered state of the art adaptive threshold based algorithms have been shown to accurately define functional volumes under certain imaging conditions of spherical and homogeneous activity distribution lesions. However, they require specific parameters optimization and are thus system-dependent. In addition, the adaptive thresholding approaches usually involve some user interaction to select background regions of interest, which can potentially lead to user introduced variability. In the present study we have focused on the evaluation under different imaging conditions of the level of robustness and repeatability of different functional volume segmentation algorithms, including current state-of-the-art in clinical practice.

In terms of robustness, the use of images from different commercial PET/CT systems acquired under typical clinical acquisition conditions resulted in large variability in the performance of the different segmentation algorithms evaluated.

Across all of the images and spheres considered, a fixed threshold of 42% of the maximum resulted in the largest variability of the segmented functional volumes (±15-60%) across the different images considered for spheres <3cm in diameter. On the other hand, a fixed threshold of 50% was closer in terms of variability (±20%) with that of one of the advanced segmentation algorithms included in this work (FCM).

Finally, the FLAB algorithm was the most robust of all evaluated algorithms leading to the smallest variability (±5%), with no particular dependence on acquisition (duration, contrast) and processing parameters (reconstructed voxel size). The 42% fixed threshold and the FCM algorithm were the most sensitive to contrast and the acquisition duration, across the different scanners used. In terms of variability across the different images used, the 50% fixed threshold demonstrated the most significant variability dependence on lesion contrast. Finally, regarding the use of adaptive thresholding (TSBR), applying this approach to acquisitions performed on a different scanner than the one used to optimize its parameters led to higher mean errors of <25%.

In terms of repeatability, all algorithms considered exhibited mean differences of <5%, although only FLAB came close to the perfect repeatability that can be achieved by a deterministic approach such as a fixed threshold. Finally, the repeatability of both threshold and automatic segmentation approaches was superior to that of manual delineation (variability >15-20% for both the clinical and simulated tumors). The present study also demonstrated that the use of any of the segmentation algorithms significantly reduces intra-and inter-observer variability associated with manual delineation. However, one should keep in mind that automated segmentation algorithms are not able to differentiate between similar levels of physiological and pathological elevated tracer uptakes. Therefore physician involvement is still imperative and desirable, especially regarding the detection and selection of elevated tracer uptakes corresponding to pathological findings that are to be subsequently accurately delineated.

Conclusion

This study has demonstrated significant differences in the robustness and reproducibility of functional volume measurements depending on the segmentation algorithm used. The advantage of employing advanced segmentation algorithms is an improvement in overall elevated activity delineation across the different range of image quality that can be encountered today in clinical practice, without the need for system-dependent optimization procedures. In addition, their high level of repeatability allows achieving similar performance to that of deterministic threshold based approaches. Therefore such advanced image segmentation algorithms may provide robust and reliable tools to aid physicians as an initial guess in determining functional volumes in PET. Table IV

Table captions

Table I: of all the parameters considered for each scanner model.

  errors representing segmentation accuracy, computed for each sphere are shown in figure 4(a), considering the entire range of systems used for acquisition and the different parameters in terms of contrast, acquisition duration and voxel size. For all the systems considered the relative impact of the different acquisition (contrast, duration) and reconstruction (voxel size) parameters is demonstrated in figures 4(b), 4(c) and 4(d) respectively. Table III contains the mean errors and standard deviations computed across the different spheres taken separately (as shown in figure 4(a)) and all together for the different imaging devices and acquisition configurations considered.

  The overall better accuracy (lower mean errors) and smaller variability (lower standard deviation) associated with the FLAB algorithm across the different images considered demonstrates its ability, without the need of any scanner-specific optimization, to robustly deal with the different image quality resulting from the use of different reconstruction and correction algorithms as well as sensitivities associated with different systems. This of should be considered within the context of the limited absolute accuracy of binary threshold-based approaches shown in this and previous studies. The accuracy of threshold-based approaches is particularly limited for non-homogeneous in form and activity distribution lesions resulting, as previously shown[15], in large under or over-estimation of the tumor spatial extent.

Table II:

  Figure 1

Figure 1 :

 1 Figure 1: 2D phantom slices, through the centre of the spheres, for the different

Figure 2 :

 2 Figure 2: Illustration of the variability considering the 17mm sphere across all four

Figure 3 : 4 :

 34 Figure 3: Mean classification errors and standard deviation (error bars) for each

Table II Robustness results obtained across entire range of scanner models and acquisition parameters for each method

 II

		PET/CT scanner models and acquisition parameters
		PET/CT	Contrast	Voxel size T42	Duration (min) T50	FCM	Recon FLAB
	Philips Gemini Philips Gemini TF All spheres (37-13	4:1 8:1 4:1 8:1 Mean CE (%) 2 x 2 x 2 4 x 4 x 4 4 x 4 x 4 2 x 2 x 2 42.6	1, 2, 5 1, 2, 5 20.3	RAMLA 3D TF ML-EM 27.8 8.7
	Siemens Biograph	4:1 8:1 Standard dev 2 x 2 x 2 5.33 x 5.33 x 2 . 51.6	1, 2, 5 18.5	FORE-OSEM 25.6 4.5
	GE Discovery LS	4:1 8:1 Mean CE (%) 1.95 x 1.95 x 4.25 4.3 x 4.3 x 4.25 10.5	1, 2, 5 16	FORE-OSEM 11.4 8.4
		37 mm
			Table I Standard dev.	5.3	7.9	5.3	2.8
			Mean CE (%)		17	15.9	11.7	8.4
		28 mm
			Standard dev.	13.8	7.5	5.7	3.6
	Figure 4(a)	Adaptive thresholding parameters for each scanner PET/CT system TSBR a param TSBR b classification param Minimum mean associated 22 mm Mean CE (%) 23 15.6 13.4 deviation of error Standard classification 7.9 Standard dev. 20.7 9.8 7.1 3.3
			Mean CE (%)	49.1	error (%) 21.5	31.6	7.2
		Philips Gemini 17 mm			59.7		10.8	3.3
			Standard dev.		35	13.8	12.7	4.9
	Philips Gemini TF			61.4		9.7	2.8
			Mean CE (%)	113.6	32.7	70.9	11.6
		13 mm
	Siemens Biograph	41.7		57.6		13.1	5.2
			Standard dev.	62.1	33.1	20.9	5.9
		GE Discovery LS	42.0		56.8		11.1	3.7

Table III Repeatability results for each methodology and manual observers

 III

		Simulated cases	Clinical cases
	Method	Mean	Standard	Mean	Standard
		variability (%)	deviation	variability (%)	deviation
	FLAB	0.5	0.3	0.9	0.5
	FCM	0.8	0.6	1.7	1.9
	Fixed thresholding	0	0	0	0
	Adaptive thresholding	3.4	2.8	3.8	3.1
	Manual delineation 1)	13.4	17.3	19.6	15.2
	Manual delineation 2)	11.7	18.4	22.1	13.6
	Manual delineation (expert 2 w/r to 1)	16.4	21.8	24.7	17.5

Acknowledgments

This work was financially supported by the French National Research Agency (ANR) under contract ANR-08-ETEC-005-01. We would like to thank the following clinical centers and associated members for some of the phantom and patient datasets used in this study: Nuclear Medicine departments of CHU Brest, France (Alexandre Turzo), CHU Sud-Amiens, France, (Pascal Bailly, Joel Daouk), and St Bartholomew's Hospital, London, UK (Iain Murray).


Mathieu Hatt

Dimitris Visvikis

Fixed thresholds cannot reliably define functional volumes due to their deterministic and binary nature whereas tumor uptake is variable, spatially heterogeneous and dependent on a large number of acquisition and reconstruction parameters. We agree that additional studies are needed to better characterize the correlation between tracer uptake and underlying metabolism. However, irrespectively of such correlation, differentiation of a PET volume from its background is an image segmentation issue that cannot be rigorously addressed using threshold-based methodologies. Those lead to inconsistent tumor volumes in most of the realistic clinical cases (1-5), especially heterogeneous ones [START_REF] Devic | Defining Radiotherapy Target Volumes Using 18F-Fluoro-Deoxy-Glucose Positron Emission Tomography/Computed Tomography: Still a Pandora's Box?[END_REF][START_REF] Nestle | Comparison of Different Methods for Delineation of 18F-FDG PET-Positive Tissue for Target Volume Definition in Radiotherapy of Patients with Non-Small Cell Lung Cancer[END_REF][START_REF] Hatt | Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications[END_REF]. In these cases and in the absence of appropriate segmentation tools, it may be more accurate (although less reproducible) to rely on manual delineation rather than fixed threshold.

The use of inappropriate segmentation tools will lead to misleading conclusions regarding the potential of FDG PET in guiding radiotherapy treatment planning or as a prognostic and predictive factor for therapy response [START_REF] Lucignani | SUV and segmentation: pressing challenges in tumor assessment and treatment[END_REF]. As new algorithms become available and the clinical research applications demonstrate their potential, the medical equipment and software industry should implement them. Societies should develop minimum standards and guidelines regarding functional volumes segmentation, first in clinical research and eventually in clinical practice. This is a slow process and misleading conclusions as a result of the use of inappropriate approaches will reduce the interest of the technique, slowing even further the process of making available new technology. We therefore suggest a more radical stance with avoiding the use of any fixed threshold based definition of PET metabolic tumor volumes in the future, especially if they are to be used for any PET image guided therapy application.

  STILL A PANDORA'S BOX?: In Regard to DEVIC et al. (Int J Radiation Oncology Biol Phys 2010 doi:10.1016/j.ijrobp.2010.02.015) Mathieu Hatt, PhD* Dimitris Visvikis, PhD *Corresponding author: Mathieu HATT, PhD INSERM U650, National Institute of Health and Medical Research, Laboratoire de Traitement de l'Information Medicale (LaTIM) CHU Morvan,5 avenue Foch, 29609 Brest, France Tel: +33(0)2 98 01 81 11, Fax: +33(0)2 98 01 81 24, e-mail: hatt@univ-brest.fr To the Editor: Devic et al. (1) investigated the use of fixed thresholds to define NSCLC tumors PET volumes exhibiting heterogeneous uptake. They found no correlation between the CT-based and the PET-based volumes, and associated the observed variations with intrinsic properties of PET acquisition rather than their segmentation choice. They also concluded that PET-based volumes should not be used for radiotherapy dose painting/boosting. Several studies recently dealt with similar issues (2,3) by considering fixed threshold to determine tumor metabolic volumes, demonstrating variability in the threshold values. Other recent studies demonstrated the limitations of fixed threshold and proposed more accurate and robust methods, from adaptive thresholding (4,5) to advanced algorithms (6-8) capable in some cases of handling heterogeneous uptake frequently characterizing tumors treated with radiotherapy.