Artificial Intelligence Meets Medulloblastoma: Breaking Barriers in Subgroup Prediction

Medulloblastoma is the most common malignant brain tumour in children, and early, accurate diagnosis is crucial for effective treatment. Current methods for identifying molecular subgroups of medulloblastoma rely on invasive biopsies and genetic testing, which are costly and not always accessible. This project aims to develop an artificial intelligence (AI) model that can predict medulloblastoma molecular subgroups using multiparametric MRI scans. By applying advanced machine learning techniques, this approach seeks to provide a non-invasive, accurate, and accessible diagnostic tool that could help guide treatment decisions and improve patient outcomes. Access to CBTN’s comprehensive MRI imaging and clinical datasets will be essential for training and validating the model.