Deep learning-enabled multiphoton microscopy predicts colorectal cancer recurrence from routine FFPE specimens.
retrospective_cohort · Level III
Where this comes from
- Record sourced from PubMed, PMID 41254110.
- Also identified by DOI 10.1038/s41746-025-02058-3 and PMC identifier 12627545.
- Licence recorded as CC BY-NC-ND.
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Abstract
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.