Deep learning-enabled multiphoton microscopy predicts colorectal cancer recurrence from routine FFPE specimens.

Colorectal cancer recurrence remains a major challenge after curative resection, and accurate tools for early risk assessment are essential to stratify patients and guide personalized therapeutic planning. We developed MPMRecNet, a dual-stream deep learning model for predicting recurrence using multiphoton microscopy imaging of formalin-fixed paraffin-embedded tissue sections from 1071 patients across two hospitals. MPMRecNet employs MaxViT-based encoders, cross-modal attention fusion, and classification under focal loss with mixed-precision optimization. It achieved strong external validation performance (ROC-AUC = 0.849, PR-AUC = 0.664), outperforming traditional clinical predictors. Multivariable analysis confirmed MPMRecNet as the most powerful independent predictor of recurrence (OR = 5.66, p < 0.001), and a combined nomogram incorporating clinical variables further improved stratification (ROC-AUC = 0.872). MPMRecNet offers a non-destructive tool for recurrence prediction from routine pathology slides, supporting precise risk assessment and postoperative surveillance.