Aurélien Corroyer-Dulmont

Explainability and Clinical Trust of Deep Learning in Glioblastoma Treatment Efficacy Prediction: A Comprehensive Framework.

Glioblastoma (GBM) treatment response is highly heterogeneous. Predicting outcomes from pre-treatment MRI is a critical unmet need, yet the clinical adoption of "black box" deep learning (DL) models is hindered by a lack of transparency. Robust and holistic validation frameworks still need to be developed to allow clin...

Deep learning prediction of overall survival in glioblastoma patients using pre-chemoradiotherapy MRI and clinical data

Glioblastoma (GBM) exhibits variable treatment response and patients shown a median survival of 15 months. A major challenge lies in the difficulty of reliably assessing treatment response at an early stage, as it can usually only be evaluated on anatomical MRI performed up to three months after the start of treatm...

Biomedical Imaging CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking

CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking

Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a median overall survival of 15 months. Longitudinal, multi-modal imaging datasets with comprehensive clinical and treatment data are essential to support the development of reproducible computational methods for treatment response prediction...