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Prédiction par Deep Learning de la réponse complète après radiochimiothérapie pré-opératoire du cancer du rectum localement avancé

Abstract : The use of Electronic Health Records is generating vast amount of data. They include demographic, socio-economic, clinical, biological, imaging and genomic features. Medicine, which relied on semiotics and physiopathology, will be permanently disrupted by this phenomenon. The complexity and volume of data that need to be analyzed to guide treatment decision will soon overcome the human cognitive abilities. Artificial Intelligence methods could be used to assist the physicians and guide decision-making. The first part of this work presents the different types of data routinely generated in oncology, which should be considered for modelling a prediction. We also explore which specific data is created in radiation oncology and explain how it can be integrated in a clinical data warehouse through the use of an ontology we created. The second part reports on several types of machine learning methods: k-NN, SVM, ANN and Deep Learning. Their respective advantages and pitfalls are evaluated. The studies using these methods in the field of radiation oncology are also referenced. The third part details the creation of a model predicting pathologic complete response after neoadjuvant chemoradiation for locally-advanced rectal cancer. This proof-of-concept study uses heterogeneous sources of data and a Deep Neural Network in order to find out which patient could potentially avoid radical surgical treatment, in order to significantly reduce the overall adverse effects of the treatment. This example, which could easily be integrated within the existing treatment planning systems, uses routine health data and illustrates the potential of this kind of approach for treatment personalization.
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Submitted on : Tuesday, February 4, 2020 - 2:43:06 PM
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  • HAL Id : tel-02466476, version 1


Jean-Emmanuel Bibault. Prédiction par Deep Learning de la réponse complète après radiochimiothérapie pré-opératoire du cancer du rectum localement avancé. Bio-informatique [q-bio.QM]. Université Sorbonne Paris Cité, 2018. Français. ⟨NNT : 2018USPCB216⟩. ⟨tel-02466476⟩



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