Directeur.e(s) de recherche
Louis Archambault
Start date
Title of the research project
Geometry-based quality control for external radiation therapy planning using stochastic frontier analysis
Description

This project focuses on the use of machine learning techniques in external radiotherapy for cancer treatment planning.
Stochastic frontier analysis is a parametric approach used in econometrics and appropriated for medical physics. Using a retrospective bank of treated patients it will be possible to predict the optimal dose of radiation for tumor and healthy organs.
This method is applied to multiple cancer treatment sites which emerge new challenge in the context of prediction, and data processing.

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Radiotherapy treatments currently used in the clinical field are rarely modified. They generally consist of a global therapy of 50 grays, fractionated in five treatments of two grays every week for five weeks.
Thus, it could be worthwhile to develop a numeric tool, based on mathematical models found in the literature, in order to compare different types of treatment without having to test them on real tissues. Several parameters are known to alter the tissue response after irradiation including oxygen

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