Bayesian statistics and modeling for the prediction of radiotherapy outcomes : an application to glioblastoma treatment

Abstract : A Bayesian statistics framework was created in this thesis work for developing clinical based models in a continuous learning approach in which new data can be added. The objective of the models is to forecast radiation therapy effects based on clinical evidence. Machine learning concepts were used for solving the Bayesian framework. The models developed concern an aggressive brain cancer called glioblastoma. The medical data comprises a database of about 90 patients suffering glioblastoma; the database contains medical images and data entries such as age, gender, etc. Neurologic grade predictions models were constructed for illustrating the type of models that can be build with the methodology. Glioblastoma recurrence models, in the form of Generalized Linear Models (GLM) and decision tree models, were developed to explore the possibility of predicting the recurrence location using pre-radiation treatment imaging. Following, due to the lack of a sufficiently strong prediction obtained by the tree models, we decided to develop visual representation tools to directly observe the medical image intensity values concerning the recurrence and non-recurrence locations. Overall, the framework developed for modeling of radiation therapy clinical data provides a solid foundation for more complex models to be developed.
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Oscar Daniel Zambrano Ramirez. Bayesian statistics and modeling for the prediction of radiotherapy outcomes : an application to glioblastoma treatment. Physics [physics]. Normandie Université, 2018. English. ⟨NNT : 2018NORMC277⟩. ⟨tel-02163513⟩

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