Table 1_An integrated prognostic model incorporating clinical characteristics, multiparametric MRI radiomics, deep learning features, and intratumoral heterogeneity for glioma patients.docx

Feature-level integration was achieved using a two-stage LASSO-Cox regression to leverage the complementary nature of information from different sources fully. Performance was quantified using the Harrell concordance index (C-index) and validated via Kaplan–Meier survival analysis, time-dependent receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).</p>Results<p>The fusion model integrating DL, radiomics, ITH, and clinical features (DenseNet121_DL+Rad+ITH) achieved C-indices of 0.801 (95% CI: 0.750–0.853) and 0.710 (95% CI: 0.638–0.783) on the training and test sets, respectively, outperforming all single models.

The corresponding stacking ensemble yielded a comparable test C-index of 0.701. Kaplan–Meier analysis confirmed significant risk stratification (P < 0.001). Time-dependent ROC analysis showed AUCs of 0.705, 0.721, 0.746, 0.800, and 0.873 at 12, 18, 24, 30, and 36 months.

Calibration curves demonstrated good agreement between predicted and observed survival probabilities. DCA indicated favorable clinical net benefit for medium- to long-term survival prediction (24–36 months).</p>Conclusions<p>An integrated prognostic model combining clinical characteristics with multiparametric MRI-derived features (DL, radiomics, and ITH) demonstrates strong prognostic potential for glioma patients. Fusion strategies generally outperform single-feature models; however, incremental gains from fusion are modest, and prospective validation in larger cohorts remains necessary.</p>