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.

Radiomic analysis based on machine learning of multi-sequences MR to assess early treatment response in locally advanced nasopharyngeal carcinoma.

ObjectiveThe prediction of early response in locally advanced nasopharyngeal carcinoma (LA-NPC) after concurrent chemoradiotherapy (CCRT) is important for determining the need for timely consolidation therapy. We developed a radiomic analysis of multi-sequences MR based on machine learning (ML) to assess early response in LA-NPC after CCRT.MethodsThis study retrospectively enrolled 104 LA-NPC patients, randomly divided into training (70%) and test (30%) cohorts.