<p dir="ltr">Human brain tumors present significant challenges due to their heterogeneous nature, known as intra-tumoral heterogeneity (ITH), which evolves over space and time, leading to treatment resistance and poor patient outcomes. Current diagnostic methods rely on pre-surgical imaging and single biopsy samples, providing only a partial understanding of the tumor microenvironment (TME) and often resulting in incomplete targeting of tumor mutations, leaving residual disease vulnerable to recurrence. Our hypothesis proposes a novel approach: utilizing multimodal and multiparametric <i>in vivo</i> imaging to map the cellular and molecular characteristics of the TME. By correlating imaging signatures with underlying somatic and genomic aberrations, we aim to develop a predictive model guiding personalized targeted therapies to effectively address the heterogeneity of brain tumors.</p><p dir="ltr">To achieve this goal, we designed, tested, and validated a predictive model through a pilot study using clinical MRI scans and one stereotactic biopsy sample. Subsequently, we optimized a multimodal and multiparametric imaging protocol including MRI and PET scans, to acquire comprehensive morphological, functional, and molecular data from the TME. Additionally, we established a detailed pipeline for subject recruitment, data collection, and post-processing to ensure the robustness and reliability of our model.</p><p dir="ltr">This innovative approach has the potential to overcome the limitations of current diagnostic methods by providing a comprehensive understanding of the TME using minimally-invasive imaging techniques. By correlating imaging data with ground truth pathology and genomics, this model will enhance brain tumor diagnosis and facilitate the implementation of targeted therapies, ultimately improving treatment response and patient outcomes.</p>

A l’ère de la médecine personnalisée, de la génomique et des thérapies ciblées, les outils quantitatifs en imagerie médicale, et en particulier en imagerie fonctionnelle par Tomographie par Emission de Positons (TEP), apparaissent incontournables. Au-delà de la mesure de l’intensité de la fixation, il est possible de disposer d’index quantitatifs caractérisant l’ensemble du volume tumoral métabolique et d’en évaluer l’hétérogénéité. L’objectif de ce travail a été d’étudier la valeur de nouveaux indices quantitatifs en imagerie TEP, permettant une analyse plus globale de la tumeur. Une première partie du travail est méthodologique et concerne la caractérisation et la compréhension de l’hétérogénéité tumorale à partir de l’image métabolique, étape ayant permis d’identifier les indices de texture les plus pertinents pour les applications cliniques. Deux séries de patients seront ensuite utilisées pour explorer l’apport de ces indices volumétriques et de texture. Pour des cancers pulmonaires, nos résultats suggèrent que la mesure de l’hétérogénéité tumorale reflète des caractéristiques histologiques de la tumeur. Une deuxième série de résultats montrent que les mesures de volumes métaboliques sont des critères plus pertinents que les indices conventionnels pour l’évaluation des chimiothérapies néoadjuvantes au cours des cancers pulmonaires de stade localement avancé. Une corrélation entre les modifications quantitatives sous traitement et les résultats histologiques post-thérapeutiques a permis de valider l’utilisation de ces indices. Dans les cancers mammaires, nos résultats suggèrent que les tumeurs présentant des signes histologiques d’agressivité, notamment les tumeurs de phénotype triple négatif, présentent une texture plus hétérogène que les autres types. Ainsi, nos travaux montrent qu’une approche quantitative plus globale de la tumeur en imagerie TEP permet d’améliorer l’évaluation pronostique pré-thérapeutique et sous traitement des cancers / In the era of personalized medicine, genomics and targeted therapies, the availability of quantitative tools assisting the interpretation of medical images is essential. In Positron Emission Tomography (PET), beyond measurements of uptake intensity, it is possible to derive quantitative index characterizing the metabolic volume or the tumoral heterogeneity. The objective of this work was to investigate the value of new quantitative indices to enhance PET imaging, allowing for a more comprehensive analysis of the whole tumor. The first part of the work deals with methodological issues associated with the measurement of tumor heterogeneity using textural index. In particular, we identified the most robust and informative textural index for clinical applications. Two sets of patients have then been used to explore the contribution of metabolic volume and texture analysis in PET. In lung cancer patients, our results suggest that the measurement of tumor heterogeneity gives some information regarding the histological features of the tumor. A second set of results shows that metabolic volume is more relevant than conventional indices for evaluating the impact of neoadjuvant chemotherapy in locally advanced stages. A correlation between quantitative changes during treatment and post-treatment histology results was used to demonstrate the relevance of these indices. In breast cancer patients, our results suggest that tumors with aggressive immunohistological patterns, particularly triple-negative phenotype, have a more heterogeneous texture than other types. In summary, our results suggest that a more comprehensive quantitative characterization of the metabolic activity distribution in tumor using PET imaging improves the pre-therapeutic and prognostic evaluation of cancer.