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International audience; INTRODUCTION: Previous studies identified different dimensions of attitudes towards a decrease in meat consumption, but they did not assess which one had the greater ability to change this consumption.OBJECTIVES: This cross-sectional study aimed to describe sociodemographic profiles associated with attitudes and the ability to decrease meat consumption in a large sample of French adults.METHODS: This study included 30,318 non-vegetarian participants from the NutriNet-Santé cohort. Twelve dimensions of attitudes towards meat consumption evolution such as taste, health, ethics, environment, animal welfare or social pressure, have been assessed using a web-based questionnaire. For each dimension, attitude and its corresponding ability to change meat consumption were evaluated with two 5-point Likert scales, respectively. Participants were classified into three different groups: no concern, concern but no ability to change, concern with ability to change. Associations between sociodemographic profiles and involvement levels in changing were evaluated using multivariable polytomic logistic regressions.RESULTS: Higher concerns towards varying protein sources, health and environment appeared to be factors associated with the reduction of meat consumption. Sex, age and educational level were more likely to be associated with attitudes having the ability to reduce meat consumption. For example, a dislike for meat taste was more likely to have the ability to reduce meat consumption in women compared to men (concern with ability to change compared to no concern: OR =3.19, 95% CI =[2.65 ; 3.84]).CONCLUSION: In a context of food sustainability, our results improved the understanding of the determinants associated with a change in meat consumption.

The arrival of immunotherapy has profoundly changed the management of multiple cancers, obtaining unexpected tumour responses. However, until now, the majority of patients do not respond to these new treatments. The identification of biomarkers to determine precociously responding patients is a major challenge. Computational medical imaging (also known as radiomics) is a promising and rapidly growing discipline. This new approach consists in the analysis of high-dimensional data extracted from medical imaging, to further describe tumour phenotypes. This approach has the advantages of being non-invasive, capable of evaluating the tumour and its microenvironment in their entirety, thus characterising spatial heterogeneity, and being easily repeatable over time. The end goal of radiomics is to determine imaging biomarkers as decision support tools for clinical practice and to facilitate better understanding of cancer biology, allowing the assessment of the changes throughout the evolution of the disease and the therapeutic sequence. This review will develop the process of computational imaging analysis and present its potential in immuno-oncology.

We investigated whether a score combining baseline neutrophilia and a PET biomarker could predict outcome in patients with locally advanced cervical cancer (LACC). Patients homogeneously treated with definitive chemoradiation plus image-guided adaptive brachytherapy (IGABT) between 2006 and 2013 were analyzed retrospectively. We divided patients into two groups depending on the PET device used: a training set (TS) and a validation set (VS). Primary tumors were semi-automatically delineated on PET images, and 11 radiomics features were calculated (LIFEx software). A PET radiomic index was selected using the time-dependent area under the curve (td-AUC) for 3-year local control (LC). We defined the neutrophil SUV grade (NSG = 0, 1 or 2) score as the number of risk factors among (i) neutrophilia (neutrophil count >7 G/L) and (ii) high risk defined from the PET radiomic index. The NSG prognostic value was evaluated for LC and overall survival (OS). Data from 108 patients were analyzed. Estimated 3-year LC was 72% in the TS (n = 69) and 65% in the VS (n = 39). In the TS, SUVpeak was selected as the most LC-predictive biomarker (td-AUC = 0.75), and was independent from neutrophilia (p = 0.119). Neutrophilia (HR = 2.6), high-risk SUVpeak (SUVpeak > 10, HR = 4.4) and NSG = 2 (HR = 9.2) were associated with low probability of LC in TS. In multivariate analysis, NSG = 2 was independently associated with low probability of LC (HR = 7.5, p

This study aims to evaluate the impact of key parameters on the pseudo computed tomography (pCT) quality generated from magnetic resonance imaging (MRI) with a 3-dimensional (3D) convolutional neural network.Four hundred two brain tumor cases were retrieved, yielding associations between 182 computed tomography (CT) and T1-weighted MRI (T1) scans, 180 CT and contrast-enhanced T1-weighted MRI (T1-Gd) scans, and 40 CT, T1, and T1-Gd scans. A 3D CNN was used to map T1 or T1-Gd onto CT scans and evaluate the importance of different components. First, the training set size's influence on testing set accuracy was assessed. Moreover, we evaluated the MRI sequence impact, using T1-only and T1-Gd-only cohorts. We then investigated 4 MRI standardization approaches (histogram-based, zero-mean/unit-variance, white stripe, and no standardization) based on training, validation, and testing cohorts composed of 242, 81, and 79 patients cases, respectively, as well as a bias field correction influence. Finally, 2 networks, namely HighResNet and 3D UNet, were compared to evaluate the architecture's impact on the pCT quality. The mean absolute error, gamma indices, and dose-volume histograms were used as evaluation metrics.Generating models using all the available cases for training led to higher pCT quality. The T1 and T1-Gd models had a maximum difference in gamma index means of 0.07 percentage point. The mean absolute error obtained with white stripe was 78 ± 22 Hounsfield units, which slightly outperformed histogram-based, zero-mean/unit-variance, and no standardization (P.0001). Regarding the network architectures, 3%/3 mm gamma indices of 99.83% ± 0.19% and 99.74% ± 0.24% were obtained for HighResNet and 3D UNet, respectively.Our best pCTs were generated using more than 200 samples in the training data set. Training with T1 only and T1-Gd only did not significantly affect performance. Regardless of the preprocessing applied, the dosimetry quality remained equivalent and relevant for potential use in clinical practice.

Radiomics is a recent area of research in precision medicine and is based on the extraction of a large variety of features from medical images. In the field of radiation oncology, comprehensive image analysis is crucial to personalization of treatments. A better characterization of local heterogeneity and the shape of the tumor, depicting individual cancer aggressiveness, could guide dose planning and suggest volumes in which a higher dose is needed for better tumor control. In addition, noninvasive imaging features that could predict treatment outcome from baseline scans could help the radiation oncologist to determine the best treatment strategies and to stratify patients as at low risk or high risk of recurrence. Nuclear medicine molecular imaging reflects information regarding biological processes in the tumor thanks to a wide range of radiotracers. Many studies involving 18F-fluorodeoxyglucose positron emission tomography suggest an added value of radiomics compared with the use of conventional PET metrics such as standardized uptake value for both tumor diagnosis and prediction of recurrence or treatment outcome. However, these promising results should not hide technical difficulties that still currently prevent the approach from being widely studied or clinically used. These difficulties mostly pertain to the variability of the imaging features as a function of the acquisition device and protocol, the robustness of the models with respect to that variability, and the interpretation of the radiomic models. Addressing the impact of the variability in acquisition and reconstruction protocols is needed, as is harmonizing the radiomic feature calculation methods, to ensure the reproducibility of studies in a multicenter context and their implementation in a clinical workflow. In this review, we explain the potential impact of positron emission tomography radiomics for radiation therapy and underline the various aspects that need to be carefully addressed to make the most of this promising approach.

Beyond the conventional techniques of diagnosis and follow-up of cancer, radiomic analysis allows to personalize radiotherapy treatments, by proposing a non-invasive characterization of tumor heterogeneity. Based on the extraction of advanced quantitative parameters (histograms of intensities, texture, shape) from multimodal imaging, this technique has notably proved its interest in determining predictive signatures of treatment response. During this thesis, signatures of cervical cancer recurrence have been developed, based on radiomic analysis alone or in combination with conventional biomarkers, providing major perspectives in the stratification of patients that can lead to dosimetric treatment plan adaptation.However, various methodological barriers were raised, notably related to the great variability of the protocols and technologies of image acquisition, which leads to major biases in multicentric radiomic studies. These biases were assessed using phantom acquisitions and multicenter patient images for PET imaging, and two methods enabling a correction of the stratification effect were proposed. In MRI, a method of standardization of images by harmonization of histograms has been evaluated in brain tumors.To go further in the characterization of intra-tumor heterogeneity and to allow the implementation of a personalized radiotherapy, a method for local texture analysis has been developed. Specifically adapted to brain MRI, its ability to differentiate sub-regions of radionecrosis or tumor recurrence was evaluated. For this purpose, parametric heterogeneity maps have been proposed to experts as additional MRI sequences.In the future, validation of the predictive models in external centers, as well as the establishment of clinical trials integrating these methods to personalize radiotherapy treatments, will be mandatory steps for the integration of radiomic in the clinical routine.; Au-delà des techniques conventionnelles de diagnostic et de suivi du cancer, l’analyse radiomique a pour objectif de permettre une médecine plus personnalisée dans le domaine de la radiothérapie, en proposant une caractérisation non invasive de l’hétérogénéité tumorale. Basée sur l’extraction de paramètres quantitatifs avancés (histogrammes des intensités, texture, forme) issus de l’imagerie multimodale, cette technique a notamment prouvé son intérêt pour définir des signatures prédictives de la réponse aux traitements. Dans le cadre de cette thèse, des signatures de la récidive des cancers du col utérin ont notamment été développées, à partir de l’analyse radiomique seule ou en combinaison avec des biomarqueurs conventionnels, apportant des perspectives majeures dans la stratification des patients pouvant aboutir à une adaptation spécifique de la dosimétrie.En parallèle de ces études cliniques, différentes barrières méthodologiques ont été soulevées, notamment liées à la grande variabilité des protocoles et technologies d’acquisition des images, qui entraîne un biais majeur dans les études radiomiques multicentriques. Ces biais ont été évalués grâce à des images de fantômes et des images multicentriques de patients pour l’imagerie TEP, et deux méthodes de correction de l’effet de stratification ont été proposées. En IRM, une méthode de standardisation des images par harmonisation des histogrammes a été évaluée dans les tumeurs cérébrales.Pour aller plus loin dans la caractérisation de l’hétérogénéité intra-tumorale et permettre la mise en œuvre d’une radiothérapie personnalisée, une méthode d’analyse locale de la texture a été développée. Adaptée particulièrement aux images IRM de tumeurs cérébrales, ses capacités à différencier des sous-régions de radionécrose ou de récidive tumorale ont été évaluées. Dans ce but, les cartes paramétriques d’hétérogénéité ont été proposées à des experts comme des séquences IRM additionnelles.À l’issue de ce travail, une validation dans des centres extérieurs des modèles développés, ainsi que la mise en place d’essais cliniques intégrant ces méthodes pour personnaliser les traitements seront des étapes majeures dans l’intégration de l’analyse radiomique en routine clinique.

Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy. Significance: This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. Cancer Res; 78(16); 4786–9. ©2018 AACR.

// Sylvain Reuze 1, 2, 3, 4 , Fanny Orlhac 1, 5 , Cyrus Chargari 1, 2, 3, 6, 7 , Christophe Nioche 5 , Elaine Limkin 1 , Francois Riet 3 , Alexandre Escande 3 , Christine Haie-Meder 3 , Laurent Dercle 8, 9 , Sebastien Gouy 10 , Irene Buvat 5 , Eric Deutsch 1, 2, 3 and Charlotte Robert 1, 2, 3, 4 1 INSERM, U1030, F-94805, Villejuif, France 2 Universite Paris-Sud, Universite Paris-Saclay, F-94270, Le Kremlin-Bicetre, France 3 Gustave Roussy, Universite Paris-Saclay, Department of Radiotherapy, F-94805, Villejuif, France 4 Gustave Roussy, Universite Paris-Saclay, Department of Medical Physics, F-94805, Villejuif, France 5 IMIV, CEA, INSERM, CNRS, Universite Paris-Sud, Universite Paris-Saclay, CEA-SHFJ, Orsay, France 6 French Military Health Services Academy, Ecole du Val-de-Grâce, Paris, France 7 Institut de Recherche Biomedicale des Armees, Bretigny-sur-Orge, France 8 INSERM, U1015, F-94805, Villejuif, France 9 Gustave Roussy, Universite Paris-Saclay, Department of Nuclear Medicine and Endocrine Oncology, F-94805, Villejuif, France 10 Gustave Roussy, Universite Paris-Saclay, Department of Gynecologic Surgery, F-94805, Villejuif, France Correspondence to: Charlotte Robert, email: ch.robert@gustaveroussy.fr Keywords: radiomics, cervical cancer, texture, PET imaging Received: February 24, 2017 Accepted: April 11, 2017 Published: May 15, 2017 ABSTRACT Objectives: To identify an imaging signature predicting local recurrence for locally advanced cervical cancer (LACC) treated by chemoradiation and brachytherapy from baseline 18 F-FDG PET images, and to evaluate the possibility of gathering images from two different PET scanners in a radiomic study. Methods: 118 patients were included retrospectively. Two groups (G1, G2) were defined according to the PET scanner used for image acquisition. Eleven radiomic features were extracted from delineated cervical tumors to evaluate: (i) the predictive value of features for local recurrence of LACC, (ii) their reproducibility as a function of the scanner within a hepatic reference volume, (iii) the impact of voxel size on feature values. Results: Eight features were statistically significant predictors of local recurrence in G1 (p < 0.05). The multivariate signature trained in G2 was validated in G1 (AUC=0.76, p

The arrival of immunotherapy has profoundly changed the management of multiple cancers, obtaining unexpected tumour responses. However, until now, the majority of patients do not respond to these new treatments. The identification of biomarkers to determine precociously responding patients is a major challenge. Computational medical imaging (also known as radiomics) is a promising and rapidly growing discipline. This new approach consists in the analysis of high-dimensional data extracted from medical imaging, to further describe tumour phenotypes. This approach has the advantages of being non-invasive, capable of evaluating the tumour and its microenvironment in their entirety, thus characterising spatial heterogeneity, and being easily repeatable over time. The end goal of radiomics is to determine imaging biomarkers as decision support tools for clinical practice and to facilitate better understanding of cancer biology, allowing the assessment of the changes throughout the evolution of the disease and the therapeutic sequence. This review will develop the process of computational imaging analysis and present its potential in immuno-oncology.

The aim of this study was to evaluate if bone marrow (BM) SUVmax measured on pre-treatment 18F-FDG PET/CT predicts the clinical outcome of locally advanced cervical cancer (LACC). We recruited retrospectively patients with LACC who underwent staging 18F-FDG PET/CT and had baseline blood tests, then treated by chemoradiation therapy (CRT), followed by image-guided adaptive brachytherapy (IGABT). BM SUVmax was calculated and correlated to inflammatory blood markers. Tumor size and pelvic lymph node involvement were evaluated on baseline MRI. Prognostic value of SUV uptake and blood markers regarding overall survival (OS), pelvic and extra-pelvic recurrence-free survival (PRFS and EPRFS respectively) was assessed using Cox models with adjusted p-values. 116 patients with FIGO stage Ib-IVa cervical cancer, treated between 2005 and 2014, were analyzed. The median follow-up was 75.5 months. BM SUVmax was significantly correlated to tumor SUVmax. In multivariate analysis, PRFS was significantly poorer in patients with high BM SUVmax (>2.8) and neutrophilia (p 5 vs ≤5 cm) could predict PRFS, EPRFS and OS (p 12 vs ≤12) were not prognostic for OS or pelvic and extra-pelvic relapses. Patients with LACC and high BM SUVmax on 18F-FDG PET have worse PFRS following CRT plus IGABT. These results can be potentially explained by the pro-inflammatory role of the tumor microenvironment and G-CSF expressed by tumor cells. These data support the role of PET as a potential indicator of disease aggressiveness beyond tumor staging.

International audience

e14003Background: The incidence of brain metastases increases with frequent use of stereotactic radiotherapy. The main complication is radionecrosis (RN), which is difficult to diagnose with tumor ...

e14014Background: The incidence of brain metastases (BM) increases with frequent use of stereotactic radiotherapy. The main complication is radionecrosis (RN), without any validated treatment. Retr...

Background: The discovery of biomarkers identifying responders to immunotherapy is a major challenge. Tumor and peritumoral immune infiltration has been shown to be associated with response to anti-PD-1/PD-L1. The aim of this study was to develop a radiomics-based imaging tool of tumor immune infiltrate and to assess whether such a tool could predict clinical outcomes of patients treated with anti-PD1/PDL1. Methods: A predictive radiomics-based model of tumor-infiltrating CD8+ T cells was trained using data from the head and neck cohort of The Cancer Imaging Archive (HNSC-TCIA). Two cohorts from our institute were used for validation. Contrast-enhanced CTs of 57 patients from the HNSC-TCIA were manually segmented (tumor and surrounding tissue) and 76 radiomics features extracted. A radiomics-based score was build using radiomics features to predict tumor-infiltrating CD8+ T-cells' abundance, which was estimated using RNA-sequencing data from The Cancer Genome Atlas, and the Microenvironment Cell Populations-counter signature. As a first validation, this signature was applied to an independent cohort of 100 patients for whom the pathologic tumor immune infiltrate was postulated as either favorable (lymphoma, melanoma, lung, bladder, renal, MSI+ cancers, and adenopathy; 70 patients) or unfavorable (adenoid cystic carcinoma, low-grade neuroendocrine tumors, uterine leiomyoma; 30 patients). The signature was then applied on baseline-CTs of a second external cohort of 139 patients prospectively enrolled in anti PD-1/PD-L1 phase 1 trials. The median of the radiomics-based CD8+ score was used to separate patients into two groups (high and low score). Survival was estimated using Cox-proportional hazards model. Results: We developed a radiomics-based CD8+ signature using the six radiomics features that had highest performance on random forest. In the first external cohort, the radiomics-based CD8 T-cells score was associated with the postulated tumor immune infiltrate (Wilcoxon test, P < 0.001). In the second external cohort of patients treated with anti-PD-1/anti-PD-L1, median (±SD) radiomics score was 109.6±61.3. Patients with high-predicted score had significantly better OS (HR= 0.55, 95%CI=0.36-0.86, P= 0.009). The radiomics-based CD8+ predicted score remained significant in a multivariate Cox regression analysis including RMH score (HR= 0.50, 95%CI=0.32-0.78, P= 0.003). Conclusions: The radiomics-based signature of CD8+ T cells appears as a promising tool to estimate tumor immune infiltrate and to infer the outcome of patients treated with anti-PD-1/PD-L1. Citation Format: Roger Sun, Elaine Johanna Limkin, Laurent Dercle, Sylvain Reuzé, Stéphane Champiat, David Brandao, Loic Verlingue, Samy Ammari, Sandrine Aspeslagh, Antoine Hollebecque, Christophe Massard, Aurélien Marabelle, Jean-Yves Scoazec, Charlotte Robert, Jean-Charles Soria, Eric Deutsch, Charles Ferté. Prediction of clinical outcomes of cancer patients treated with anti-PD-1/PD-L1 using a radiomics-based imaging score of immune infiltrate [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A051.

Introduction Radiomics is a promising method which has undergone rapid development over the last few years. However, it has been shown that different acquisition/reconstruction parameters could introduce biases in PET-based textural feature calculation [1] , [2] . The aim of this study was (i) to evaluate the impact of voxel size, spatial resolution (SR) and signal-to-noise ratio (SNR) on feature values, (ii) to propose a harmonization method of 18F-FDG PET images based on phantom acquisitions to reduce the impact of SR and SNR on radiomic indices. Methods 115 cervical cancer patients were included retrospectively. Two groups were defined according to the PET scanner used for baseline image acquisition (G1: Siemens Biograph I; G2: GE Discovery-690). Eleven radiomic features were extracted from a spherical non-pathological hepatic volume of interest (VOI). The impact of voxel size was investigated by resampling all images into three different matrix sizes: 5.3 mm × 5.3 mm × 3.4 mm (G1 grid size), 2.7 mm × 2.7 mm × 3.4 mm (G2 grid size), 2.0 mm × 2.0 mm × 2.0 mm. In addition, two FDG-filled phantoms (homogeneous: HP, triple-line: TLP) were acquired on a GE Discovery-690 PET/CT with seven acquisition and reconstruction protocols, by changing the iteration number and post-filtering FWHM and with or without PSF modeling. SR was evaluated for all sets using TLP and all images were convolved by a 3D-Gaussian function (referred to harmonization filter HF hereafter) with a specific standard deviation so that all filtered images had the same SR as when using the clinical protocol. SNR was evaluated using the homogeneous phantom before and after HF. Radiomic features were also calculated in 22 spherical VOI (19.5 mL) before and after HF on the homogeneous phantom. Bland-Altman plots were used to characterize the dispersion of values between original and HF images. P-values from permutation tests were calculated between the two sets. Results At least 4 features (SUVmax, SUVpeak, Homogeneity, SRE) were highly dependent on the PET scanner in the three sets of patient images (p Original images of the phantoms showed large differences in both SR (3.3–7.9 mm) and SNR (9.2–25.7). A large variability of radiomic feature values was observed between different reconstruction protocols, especially for those with point-spread function correction. After filtering all images to the clinical SR (7.9 mm), feature values were less scattered according to Bland-Altman analysis (Figure 1). The difference in feature values between 2 mm and 6.4 mm FWHM post-filtering was highly significant on original images (permutation test, p 0.05). The whole analysis will be reproduced on a Philips Gemini PET device (work in progress). Conclusions A high variability of feature values was observed on clinical data due to the gap in technology between the two imaging devices. Gaussian filtering showed promising results on phantom data, by reducing the differences in textural feature values between protocols. When applied on highly different PET devices, this method might eliminate some biological signal. A combination of Gaussian filtering and voxel resampling will be investigated on patient data to assess the clinical use of such method on more recent devices.

This paper describes a new method for the three-dimensional (3-D) tracking and the quantification of blood vessels from Magnetic Resonance Angiography (MRA). The approach is based on the 3D geometrical moments and consists of the following steps : (1) interactive selection of 3-D seed points ; (2) automatic tracking of the vessels ; (3) local computation of both diameter and orientation ; (4) rendering of the vessels. This detection and estimation scheme has been validated on simulated and real data.