Knowledge extraction with machine Learning techniques from multi-modal MRI Data : Application to Gliomas classification

Abstract

The present thesis proposes a non-invasive machine learning (ML) framework for predicting MGMT methylation status using features derived from magnetic resonance imaging (MRI) scans, with the ultimate goal of supporting personalized therapeutic strategies. The frame- work is structured as a three-step pipeline: (i) extraction of imaging features from multimodal MRI; (ii) selection of the most relevant features using Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) algorithms; and (iii) training a ensemble composed of multiple machine learning models on the selected features to classify MGMT methylation status. The model was developed and validated using the Brain Tumor Segmen-tation (BraTS) dataset, and demonstrated superior accuracy and effectiveness compared to well-known existing approaches.