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 framework 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 an 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 Segmentation (BraTS) dataset, and demonstrated superior accuracy and effectiveness compared to well-known existing approaches.