Carte d'identité tumorale pour la caractérisation multi-échelle de l'hétérogénéité tumorale

par David Wallis

Projet de thèse en Imagerie et physique médicale

Sous la direction de Irène Buvat.

Thèses en préparation à Paris Saclay , dans le cadre de Electrical,Optical,Bio: PHYSICS_AND_ENGINEERING , en partenariat avec Imagerie moleculaire in vivo (laboratoire) et de Université Paris-Sud (établissement de préparation de la thèse) depuis le 31-03-2018 .


  • Résumé

    Projet ESR du contrat européen H2020 HYBRID Innovative Training Network towards raising and supporting the next generation of creative and entrepreneurial cross-speciality imaging experts Actuellement, la prise en charge du cancer est le plus souvent orientée par les résultats de biopsies, à partir desquelles les caractéristiques anatomopathologiques de la tumeur sont déterminées et utilisées pour choisir le traitement le plus adapté. Pourtant, du fait de l'hétérogénéité tumorale, plus de 60% des anomalies présentes dans les cellules tumorales ne sont pas retrouvées partout dans la tumeur et donc risquent de ne pas être détectées par une biopsie unique. Contrairement aux biopsies, l'imagerie médicale fournit des informations anatomiques, fonctionnelles, et moléculaires concernant l'intégralité de la tumeur et des sites tumoraux, et aussi concernant l'environnement tumoral. Le calcul de caractéristiques à partir des images et leur analyse à des fins diagnostiques ou pronostiques constituent une discipline émergente, la radiomique. La radiomique consiste à extraire de nombreuses caractéristiques à partir des images et à les combiner, entre eux ou à d'autres caractéristiques biologiques, génétiques, ou cliniques, pour améliorer la prise en charge des patients. La thèse consistera à étudier le potentiel de différentes caractéristiques calculées à partir des images (TEP, IRM, TDM) pour décrire au mieux la complexité des tumeurs. A partir de ces caractéristiques, il s'agira d'établir un phénotype tumoral, reflétant l'hétérogénéité, pronostique et prédictif. Méthodes : IMIV est largement impliqué dans le domaine de la radiomique depuis 4 ans. Le laboratoire a une connaissance approfondie de la signification biologique et de la redondance d'un certain nombre de caractéristiques en TEP, ainsi que de leur robustesse et variabilité. Des méthodes pour améliorer la robustesse de certaines caractéristiques ont été proposées. La thèse consistera à 1) construire, à partir de ces bases, un phénotype de caractéristiques radiomiques robustes, reproductible et explicable, Methods to overcome the variability of biomarkers across medical centres have been proposed ; 2) concevoir et valider des modèles utilisant ce phénotype radiomique, combiné ou pas à d'autres informations (génétiques, cliniques, protéomiques, etc) pour prédire la réponse à la thérapie ou le devenir du patient. Durant la thèse, des mobilités sont prévues chez GE Healthcare (Buc, France), King's College London (UK) et la Eberhard Karls University in Tuebingen (Allemagne). Principales références bibliographiques du laboratoire en rapport avec le projet : - Orlhac et al. Tumor texture analysis in 18F-FDG-PET: relationships between texture parameters, histogram indices, SUVs, metabolic volumes and total lesion glycolysis. J Nucl Med 55: 414-422, 2014. - Buvat et al. Tumor texture analysis in PET: where do we stand? J Nucl Med 56: 1642-1644, 2015. - Orlhac et al. 18F-FDG PET-derived textural indices reflect tissue-specific uptake pattern in non-small cell lung cancer. Plos One 10(12):e0145063, 2015. - Orlhac et al. Multi-scale texture analysis: from 18F-FDG PET images to pathological slides. J Nucl Med 57: 1823-1828, 2016. - Orlhac et al. Understanding changes in tumor textural indices in PET: a comparison between visual assessment and index values in simulated and patient data. J Nucl Med 58: 387-392, 2017. - Reuze et al. Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners. Oncotarget. 8: 43169-43179, 2017. - Schernberg et al. A score combining baseline neutrophilia and the SUVpeak of the primary tumor on FDG-PET predicts outcome in locally advanced cervical cancer. Eur J Nucl Med Mol Imaging in press doi: 10.1007/s00259-017-3824-z, 2017. - Orlhac et al. A post-reconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med 59: in press, 2018.

  • Titre traduit

    Tumour ID cards for multi-scale characterization of tumour heterogeneity


  • Résumé

    This is a 3-y full time position as an early stage researcher (ESR) under the auspice of the ITN HYBRID programme. In the current healthcare environment, cancer treatment is most often selected based on one or several biopsies within the tumour, from which the anatomopathological characteristics of the tumour are identified and used to determine the best therapeutic strategy. However, due to tumor heterogeneity, more than 60% of the abnormalities present in the cells of the tumour tissue are not found everywhere throughout the tumour and are therefore unlikely to be detected using a single biopsy. Unlike biopsies, medical images provide anatomical, functional and molecular information pertaining to the whole tumour, to all tumour foci, and also to the tumour environment. The computation of biomarkers from different medical images and their analysis to predict the nature of the tumour and its therapeutic response are the subject of an emerging discipline, radiomics, which consists in extracting a large number of features such as intensity, shape, texture, from medical images and to determine if radiomic features, possibly combined with other patient features (omics data, blood biomarkers, etc), can assist patient management. The successful PhD candidate will identify and investigate robust biomarkers extracted from various kinds of anatomical (MR, CT), functional (PET, MR) and molecular (PET) images in order to characterize at best the complexity of tumours. Based on these biomarkers, he/she will determine an image-based tumour phenotype that bears useful prognostic information and will help predict the patient response to therapy, the progression-free survival and the overall survival in different patient cohorts (eg, brain tumours, non-small cell lung cancer). Methodology. In the past four years, IMIV has been highly involved in the field of radiomics. We have established the complementary or redundant nature of some measurable biomarkers from positron emission tomography (PET) images. The sources of biomarker variability have been identified and robust computational methods have been proposed. Links have been found between some PET radiomic biomarkers and the histological characteristics of the tumours measured ex vivo. The evolution of radiomic biomarkers as a function of the macroscopic characteristics of the tumors has also been analyzed. Methods to overcome the variability of biomarkers across medical centres have been proposed. The PhD thesis project will therefore build on this experience and will now focus on: 1) the determination of robust image-based phenotype, or tumour ID card, that is reproducible and explainable; 2) the design and validation of models based on this ID card, possibly combined with other biological, omics, or clinical information, that could predict the patient response to therapy and outcome. The validity of the ID card, that is likely to depend on the cancer type, and the usability of the models for a large variety of imaging devices and imaging protocols will be carefully determined, so that models established in a given centre can be used for data acquired in a different centre. Within the course of this project, secondments to GE Healthcare (Buc, France), King's College London (UK) and the Eberhard Karls University in Tuebingen (Germany) are planned. Main bibliographic references of the hosting lab on the PhD thesis topic: - Orlhac et al. Tumor texture analysis in 18F-FDG-PET: relationships between texture parameters, histogram indices, SUVs, metabolic volumes and total lesion glycolysis. J Nucl Med 55: 414-422, 2014. - Buvat et al. Tumor texture analysis in PET: where do we stand? J Nucl Med 56: 1642-1644, 2015. - Orlhac et al. 18F-FDG PET-derived textural indices reflect tissue-specific uptake pattern in non-small cell lung cancer. Plos One 10(12):e0145063, 2015. - Orlhac et al. Multi-scale texture analysis: from 18F-FDG PET images to pathological slides. J Nucl Med 57: 1823-1828, 2016. - Orlhac et al. Understanding changes in tumor textural indices in PET: a comparison between visual assessment and index values in simulated and patient data. J Nucl Med 58: 387-392, 2017. - Reuze et al. Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners. Oncotarget. 8: 43169-43179, 2017. - Schernberg et al. A score combining baseline neutrophilia and the SUVpeak of the primary tumor on FDG-PET predicts outcome in locally advanced cervical cancer. Eur J Nucl Med Mol Imaging in press doi: 10.1007/s00259-017-3824-z, 2017. - Orlhac et al. A post-reconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med 59: in press, 2018.