GD-MIL: Grade-Disentangled Multiple Instance Learning for Multimodal Biochemical Recurrence Prediction in Prostate Cancer
Researchers have developed a new method called Grade-Disentangled Multiple Instance Learning (GD-MIL) to improve the prediction of biochemical recurrence in prostate cancer. This approach uses whole slide images (WSIs) to extract prognostic information beyond traditional Gleason grade, which is a significant limitation in current risk stratification. GD-MIL achieved a C-index of 0.704, outperforming both clinical baselines and existing imaging-only models, suggesting that H&E morphology holds valuable complementary prognostic data. AI
IMPACT This research could lead to more accurate prostate cancer recurrence prediction, improving patient stratification and treatment decisions.