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Analyse quantitative des données de routine clinique pour le pronostic précoce en oncologie

Cynthia Perier 1, 2
2 MONC - Modélisation Mathématique pour l'Oncologie
IMB - Institut de Mathématiques de Bordeaux, Institut Bergonié [Bordeaux], Inria Bordeaux - Sud-Ouest
Abstract : Tumor shape and texture evolution may highlight internal modifications resulting from the progression of cancer. In this work, we want to study the contribution of delta-radiomics features to cancer-evolution prediction. Our goal is to provide a complete pipeline from the 3D reconstruction of the volume of interest to the prediction of its evolution, using routinely acquired data only.To this end, we first analyse a subset of MRI(-extracted) radiomics biomarquers in order to determine conditions that ensure their robustness. Then, we determine the prerequisites of features reliability and explore the impact of both reconstruction and image processing (rescaling, grey-level normalization). A first clinical study emphasizes some statistically-relevant MRI radiomics features associated with event-free survival in anal carcinoma.We then develop machine-learning models to improve our results.Radiomics and machine learning approaches were then combined in a study on high grade soft tissu sarcoma (STS). Combining Radiomics and machine-learning approaches in a study on high-grade soft tissue sarcoma, we find out that a T2-MRI delta-radiomic signature with only three features is enough to construct a classifier able to predict the STS histological response to neoadjuvant chemotherapy. Our ML pipeline is then trained and tested on a middle-size clinical dataset in order to predict early metastatic relapse of patients with breast cancer. This classification model is then compared to the relapsing time predicted by the mechanistic model.Finally we discuss the contribution of deep-learning techniques to extend our pipeline with tumor automatic segmentation or edema detection.
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Cynthia Perier. Analyse quantitative des données de routine clinique pour le pronostic précoce en oncologie. Traitement des images [eess.IV]. Université de Bordeaux, 2019. Français. ⟨NNT : 2019BORD0219⟩. ⟨tel-02457768⟩

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