18 F-FDG PET is often used in clinical routine for diagnosis, staging, and response to therapy assessment or prediction. The standardized uptake value (SUV) in the primary or regional area is the most common quantitative measurement derived from PET images used for those purposes. The aim of this study was to propose and evaluate new parameters obtained by textural analysis of baseline PET scans for the prediction of therapy response in esophageal cancer. Methods: Forty-one patients with newly diagnosed esophageal cancer treated with combined radiochemotherapy were included in this study. All patients underwent pretreatment whole-body 18 F-FDG PET. Patients were treated with radiotherapy and alkylatinlike agents (5-fluorouracil-cisplatin or 5-fluorouracil-carboplatin). Patients were classified as nonresponders (progressive or stable disease), partial responders, or complete responders according to the Response Evaluation Criteria in Solid Tumors. Different imagederived indices obtained from the pretreatment PET tumor images were considered. These included usual indices such as maximum SUV, peak SUV, and mean SUV and a total of 38 features (such as entropy, size, and magnitude of local and global heterogeneous and homogeneous tumor regions) extracted from the 5 different textures considered. The capacity of each parameter to classify patients with respect to response to therapy was assessed using the Kruskal-Wallis test (P , 0.05). Specificity and sensitivity (including 95% confidence intervals) for each of the studied parameters were derived using receiver-operatingcharacteristic curves. Results: Relationships between pairs of voxels, characterizing local tumor metabolic nonuniformities, were able to significantly differentiate all 3 patient groups (P , 0.0006). Regional measures of tumor characteristics, such as size of nonuniform metabolic regions and corresponding intensity nonuniformities within these regions, were also significant factors for prediction of response to therapy (P 5 0.0002). Receiver-operating-characteristic curve analysis showed that tumor textural analysis can provide nonresponder, partialresponder, and complete-responder patient identification with higher sensitivity (76%-92%) than any SUV measurement. Conclusion: Textural features of tumor metabolic distribution extracted from baseline 18 F-FDG PET images allow for the best stratification of esophageal carcinoma patients in the context of therapy-response prediction.

GATE (Geant4 Application for Emission Tomography) is a Monte Carlo simulation platform developed by the OpenGATE collaboration since 2001 and first publicly released in 2004. Dedicated to the modelling of planar scintigraphy, single photon emission computed tomography (SPECT) and positron emission tomography (PET) acquisitions, this platform is widely used to assist PET and SPECT research. A recent extension of this platform, released by the OpenGATE collaboration as GATE V6, now also enables modelling of x-ray computed tomography and radiation therapy experiments. This paper presents an overview of the main additions and improvements implemented in GATE since the publication of the initial GATE paper (Jan et al 2004 Phys. Med. Biol. 49 4543-61). This includes new models available in GATE to simulate optical and hadronic processes, novelties in modelling tracer, organ or detector motion, new options for speeding up GATE simulations, examples illustrating the use of GATE V6 in radiotherapy applications and CT simulations, and preliminary results regarding the validation of GATE V6 for radiation therapy applications. Upon completion of extensive validation studies, GATE is expected to become a valuable tool for simulations involving both radiotherapy and imaging.

Intratumoral uptake heterogeneity in 18 F-FDG PET has been associated with patient treatment outcomes in several cancer types. Textural feature analysis is a promising method for its quantification. An open issue associated with textural features for the quantification of intratumoral heterogeneity concerns its added contribution and dependence on the metabolically active tumor volume (MATV), which has already been shown to be a significant predictive and prognostic parameter. Our objective was to address this question using a larger cohort of patients covering different cancer types. Methods: A single database of 555 pretreatment 18 F-FDG PET images (breast, cervix, esophageal, head and neck, and lung cancer tumors) was assembled. Four robust and reproducible textural feature-derived parameters were considered. The issues associated with the calculation of textural features using co-occurrence matrices (such as the quantization and spatial directionality relationships) were also investigated. The relationship between these features and MATV, as well as among the features themselves, was investigated using Spearman rank coefficients for different volume ranges. The complementary prognostic value of MATV and textural features was assessed through multivariate Cox analysis in the esophageal and non-small cell lung cancer (NSCLC) cohorts. Results: A large range of MATVs was included in the population considered (3-415 cm 3 ; mean, 35; median,19; SD, 50). The correlation between MATV and textural features varied greatly depending on the MATVs, with reduced correlation for increasing volumes. These findings were reproducible across the different cancer types. The quantization and calculation methods both had an impact on the correlation. Volume and heterogeneity were independent prognostic factors (P 5 0.0053 and 0.0093, respectively) along with stage (P 5 0.002) in non-small cell lung cancer, but in the esophageal tumors, volume and heterogeneity had less complementary value because of smaller overall volumes. Conclusion: Our results suggest that heterogeneity quantification and volume may provide valuable complementary information for volumes above 10 cm 3 , although the complementary information increases substantially with larger volumes. Fordi agnosis and staging in oncology, 18 F-FDG PET/CT is a powerful tool (1). Its use in therapy assessment (2,3) is increasing. Within this context, more emphasis is being given to image-derived indices (4). On the one hand, features extracted from PET images, including metabolically active tumor volume (MATV), mean standardized uptake value (SUV), and total lesion glycolysis, have provided potentially higher prognostic value than standard maximum SUV in various cancer types (5). On the other hand, more recently the heterogeneity of 18 F-FDG uptake within tumors has been associated with treatment failure (4,6-8). Proposed approaches to assessing the heterogeneity of intratumoral activity distribution include visual evaluation (9), SUV coefficient of variation (10), area under...

After seminal papers over the period 2009 – 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.