Radiomics model integrating MRI and ECV enhances prediction accuracy for progression in high-grade glioma

Glioma is the most common primary brain tumor, with high-grade glioma (HGG) posing significant clinical challenges due to its poor survival outcomes. One-year tumor recurrence indicates a poor prognosis, making accurate progression risk prediction models critical for clinical decision-making. This study aimed to develop a novel combined model (DLcom) based on the MobileNet-based Hybrid Network (MobHy-Net), integrating clinical variables and deep learning features from both T2-FLAIR and extracellular volume images to predict 1-year progression risk. Preoperative multi-sequence MRI (T1WI, T1C, and T2-FLAIR) from 193 HGG patients across two centers was analyzed. DLcom demonstrated superior predictive performance, with area under the curve values of 0. 954 (training), 0. 911 (validation), and 0. 919 (test), significantly outperforming other models (P < 0. 05). Furthermore, decision curve analysis confirmed its clinical utility, and Shapley Additive Explanations analysis enhanced its visualization and interpretability. DLcom effectively predicts 1-year progression risk in HGG, offering a valuable tool for risk stratification and clinical decision support.