Deep learning and pathomics analyses predict prognosis of high-grade gliomas.

Objective: Utilizing pathomics to analyze high-grade gliomas and provide prognostic insights.

Methods: Regions of Interest (ROIs) in tumor areas were identified in whole-slide images (WSI). Tumor patches underwent cropping, white space removal, and normalization. A deep learning model trained on these patches aggregated predictions for WSIs. Pathological features were extracted using Pearson correlation, univariate Cox regression, and LASSO-Cox regression. Three models were developed: a Pathomics-based model, a clinical model, and a combined model integrating both.

Results: Pathological and Clinical Features were used to build two models, leading to a predictive model with a C-index of 0.847 (train) and 0.739 (test). High-risk patients had a median progression-free survival (PFS) of 10 months (p<0.001), while low-risk patients had not reached median PFS. Stratification by IDH status revealed significant PFS differences.

Conclusion: The combined model effectively predicts high-grade glioma prognosis.