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 (DL_com) 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. DL_com 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. DL_com effectively predicts 1-year progression risk in HGG, offering a valuable tool for risk stratification and clinical decision support.