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<p>Microsatellite instability (MSI) is crucial for immunotherapy selection and Lynch syndrome diagnosis in colorectal cancer. Despite recent advances in deep learning algorithms using whole-slide images, achieving clinically acceptable specificity remains challenging. In this large-scale multicenter study, we developed Deepath-MSI, a feature-based multiple instances learning model specifically designed for sensitive and specific MSI prediction, using 5070 whole-slide images from seven diverse cohorts. Deepath-MSI achieved an AUROC of 0.98 in the test set. At a predetermined sensitivity threshold of 95%, the model demonstrated 92% specificity and 92% overall accuracy. In a real-world validation cohort, performance remained consistent with 95% sensitivity and 91% specificity. Deepath-MSI could transform clinical practice by serving as an effective pre-screening tool, substantially reducing the need for costly and labor-intensive molecular testing while maintaining high sensitivity for detecting MSI-positive cases. Implementation could streamline diagnostic workflows, reduce healthcare costs, and improve treatment decision timelines.</p>

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Deepath-MSI: a clinic-ready deep learning model for microsatellite instability detection in colorectal cancer using whole-slide imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月28日

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  • Xu Feng,
  • Wenjuan Yin,
  • Qing Ye,
  • Yayun Chi,
  • Huer Wen,
  • Yifeng Sun,
  • Jin Zheng,
  • Qifeng Wang,
  • Qian Wang,
  • Ming Zhao,
  • Lin Yuan,
  • Qinghua Xu,
  • Dan Su,
  • Xiaoyan Zhou

摘要

Microsatellite instability (MSI) is crucial for immunotherapy selection and Lynch syndrome diagnosis in colorectal cancer. Despite recent advances in deep learning algorithms using whole-slide images, achieving clinically acceptable specificity remains challenging. In this large-scale multicenter study, we developed Deepath-MSI, a feature-based multiple instances learning model specifically designed for sensitive and specific MSI prediction, using 5070 whole-slide images from seven diverse cohorts. Deepath-MSI achieved an AUROC of 0.98 in the test set. At a predetermined sensitivity threshold of 95%, the model demonstrated 92% specificity and 92% overall accuracy. In a real-world validation cohort, performance remained consistent with 95% sensitivity and 91% specificity. Deepath-MSI could transform clinical practice by serving as an effective pre-screening tool, substantially reducing the need for costly and labor-intensive molecular testing while maintaining high sensitivity for detecting MSI-positive cases. Implementation could streamline diagnostic workflows, reduce healthcare costs, and improve treatment decision timelines.