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Développement des approches radiomics à visées diagnostique et pronostique pour la prise en charge de patients atteints des sarcomes des tissus mous.

Amandine Crombé 1, 2
2 MONC - Modélisation Mathématique pour l'Oncologie
IMB - Institut de Mathématiques de Bordeaux, Institut Bergonié [Bordeaux], Inria Bordeaux - Sud-Ouest
Abstract : Soft-tissue sarcomas (STS) are malignant ubiquitous mesenchymal tumors that are characterized by their heterogeneity at several levels, i.e. in terms of clinical presentation, radiological presentation, histology, molecular features and prognosis. Magnetic resonance imaging (MRI) with a contrast-agent injection is the imaging of reference for these tumors. MRI enables to perform the local staging, the evaluation of response to treatment, to plan the surgery and to look for local relapse. Furthermore, MRI can access non-invasively to the whole tumor in situ and in vivo which is complementary to histopathological and molecular analyses requiring invasive biopsy samples at risk of sampling bias. However, no imaging biomarker dedicated to STS has been validated so far. Meanwhile, technical innovations have been developed, namely: (i) alternative imaging modalities or MRI sequences that can quantify intratumoral physiopathological phenomenon; (ii) image analysis tools that can quantify radiological phenotypes better than human’s eyes through hundreds of textural and shape quantitative features (named radiomics features); and (iii) mathematical algorithms that can integrate all these information into predictive models (: machine-learning). Radiomics approaches correspond to the development of predictive models based on machine-learning algorithms and radiomics features, eventually combined with other clinical, pathological and molecular features. The aim of this thesis was to put these innovations into practice and to optimize them in order to improve the diagnostic and therapeutic managements of patients with STS.In the first part, we combined radiological and radiomics features extracted from the baseline structural MRIs of patients with a locally-advanced subtype of STS in order to build a radiomics signature that could help to identify patients with higher risk of metastatic relapse and may benefit from neoadjuvant treatments. In the second part, we elaborated a model based on the early changes in intratumoral heterogeneity (: delta-radiomics) on structural MRIs of patients with locally-advanced high-grade STS treated with neoadjuvant chemotherapy, in order to rapidly identify patients who do not respond to treatment and would benefit from early therapeutic adjustments. In the last part, we tried to better identify and control potential bias in radiomics approaches in order to optimize the predictive models based on radiomics features.
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Amandine Crombé. Développement des approches radiomics à visées diagnostique et pronostique pour la prise en charge de patients atteints des sarcomes des tissus mous.. Modélisation et simulation. Université de Bordeaux, 2020. Français. ⟨NNT : 2020BORD0059⟩. ⟨tel-03270587⟩

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