Development and Validation of an Early Recurrence Prediction Model for High-Grade Glioma Integrating Temporalis Muscle and Tumor Features: Exploring the Prognostic Value of Temporalis Muscle.

This study aimed to develop and validate a predictive model for early recurrence of high-grade glioma (HGG) within 180 days, assess the prognostic value of preoperative and postoperative temporalis muscle metrics (area and thickness), and explore their significance in postoperative follow-up. Seventy-one molecularly confirmed HGG patients were included, with data sourced from local data and TCIA (The Cancer Imaging Archive) RHUH-GBM (Río Hortega University Hospital Glioblastoma) dataset. Tumor segmentation was performed using deep learning, and radiomic features were extracted following comparison with manual segmentation. Feature selection was conducted using mutual information and recursive feature elimination. A comprehensive model integrating 3D tumor radiomics and temporalis muscle metrics was developed and compared with a tumor-only model to identify the optimal predictive framework. SHAP analysis was used to evaluate model interpretability and feature importance. The TM_Tumor_HistGradientBoosting model, incorporating 16 features including temporalis muscle metrics, outperformed the tumor-only model in accuracy (0.89), recall (0.87), and F1 score (0.88). SHAP analysis highlighted that preoperative temporalis muscle cross-sectional area was strongly associated with early recurrence risk, while postoperative temporalis muscle thickness significantly contributed to recurrence prediction. Combining temporalis muscle metrics with preoperative tumor MRI substantially improved the accuracy of early recurrence prediction in HGG. Temporalis muscle metrics serve as objective and sustainable prognostic indicators with significant clinical value in postoperative follow-up.