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<p>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, <i>p</i> &lt; 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月18日

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  • Yabing Yang,
  • Chanchan Xiao,
  • Dehua Zou,
  • Lu Wang,
  • Ruijie Yang,
  • Yiran Zhang,
  • Lei Zhang,
  • Zhan Zhao,
  • Shenghui Qiu,
  • Shijin Liu,
  • Yu Bai,
  • Wang-Yang Sun,
  • Rong-Rong He,
  • Guobing Chen,
  • Tianwang Li,
  • Oscar Junhong Luo,
  • Wei Jiang

摘要

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.