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Utilisation de données cliniques pour la construction de modèles en oncologie

Abstract : This thesis deals with the use of clinical data in the construction of models applied to oncology. Existing models which take into account many biological mechanisms of tumor growth have too many parameters and cannot be calibrated on clinical cases. On the contrary, too simple models are not able to precisely predict tumor evolution for each patient. The diversity of data acquired by clinicians is a source of information that can make model estimations more precise. Through two different projets, we integrated data in the modeling process in order to extract more information from it. In the first part, clinical imaging and biopsy data are combined with machine learning methods. Our aim is to distinguish fast recurrent patients from slow ones. Results show that the obtained stratification is more efficient than the stratification used by cliniciens. It could help physicians to adapt treatment in a patient-specific way. In the second part, data is used to correct a simple tumor growth model. Even though this model is efficient to predict the volume of a tumor, its simplicity prevents it from accounting for shape evolution. Yet, an estimation of the tumor shape enables clinician to better plan surgery. Data assimilation methods aim at adapting the model and rebuilding the tumor environment which is responsible for these shape changes. The prediction of the growth of brain metastases is then more accurate.
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Submitted on : Tuesday, December 11, 2018 - 4:23:08 PM
Last modification on : Friday, August 21, 2020 - 4:50:50 AM
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  • HAL Id : tel-01951801, version 1



Thibaut Kritter. Utilisation de données cliniques pour la construction de modèles en oncologie. Mathématiques générales [math.GM]. Université de Bordeaux, 2018. Français. ⟨NNT : 2018BORD0166⟩. ⟨tel-01951801⟩



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