Integrating DCE-MRI-Based Dural Drainage Function Indicators into Machine Learning Models for Improved Intracranial...

Meningeal lymphatic vessels (mLVs) are crucial in intracranial tumor progression. This study investigated whether incorporating mLVs functional indicators into machine learning models enhances prognostic prediction for intracranial malignant tumors. We prospectively enrolled 246 patients, assessing baseline mLVs function via dynamic contrast-enhanced MRI. After 2.5 years' follow-up, 100 patients (51 survivors, 49 deceased) were finally included. The mean area under the receiver operating characteristic curve (AUROC) of the XGBoost model excluding DCE-MRI-based dural drainage function indicators (DDFIs) was 0.746 (95% CI: 0.621-0.871), which increased to 0.808 (95% CI: 0.733-0.883) with the inclusion of DDFIs. Kaplan-Meier survival curves demonstrated significantly better discrimination when DDFIs were included (p = 5.66 × 10^-8 vs. p = 1.22 × 10^-4). The c-index of the Cox regression model excluding DDFIs was 0.919 (95% CI: 0.916-0.940), rising to 0.948 (95% CI: 0.946-0.955) with their inclusion. In the glioma subgroup (n = 43), AUROC rose from 0.804 (95% CI: 0.629-0.980) to 0.904 (95% CI: 0.717-1.000). These findings indicate that integrating mLVs function significantly refines long-term prognostic accuracy in intracranial malignant tumors, supporting its potential clinical utility.