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<p>Data-driven approaches have advanced colorectal lesion diagnosis in digestive endoscopy, yet their application in endocytoscopy (EC)—a high-magnification imaging technique—remains limited, with most studies relying on conventional machine learning methods like support vector machines. Inspired by the success of large-scale language models that leverage progressive pre-training, we develop a computer-aided diagnosis (CAD) model using narrow-band imaging endocytoscopy (EC-NBI) to classify colorectal lesions (non-neoplastic lesions, adenomas, and invasive cancers). Here, we show that our model, trained through a multi-stage pre-training strategy combined with supervised deep clustering, outperforms state-of-the-art supervised methods in a multi-center retrospective cohort. Notably, it surpasses endoscopists’ diagnostic accuracy in human-machine competitions and enhances their performance when used as an assistive tool. This EC-NBI CAD model significantly improves the accuracy and consistency of diagnosing colorectal lesions, laying a foundation for future early cancer screening, particularly for distinguishing superficial and deep submucosal invasive cancers, pending further expansive multi-center data.</p>

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Development of deep learning-based narrow-band imaging endocytoscopic classification for predicting colorectal lesions from a retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月24日

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  • Jie Wang,
  • Mingqing Liu,
  • Haiming Liao,
  • Jiawei Fan,
  • He Zhu,
  • Tantan Ma,
  • Dong Yang,
  • Fengming Ni,
  • Fan Zhang,
  • Guohua Jin,
  • Juan Yu,
  • Jiahui He,
  • Xiaokun Liang,
  • Nan Zhang,
  • Hong Xu,
  • Zhicheng Zhang

摘要

Data-driven approaches have advanced colorectal lesion diagnosis in digestive endoscopy, yet their application in endocytoscopy (EC)—a high-magnification imaging technique—remains limited, with most studies relying on conventional machine learning methods like support vector machines. Inspired by the success of large-scale language models that leverage progressive pre-training, we develop a computer-aided diagnosis (CAD) model using narrow-band imaging endocytoscopy (EC-NBI) to classify colorectal lesions (non-neoplastic lesions, adenomas, and invasive cancers). Here, we show that our model, trained through a multi-stage pre-training strategy combined with supervised deep clustering, outperforms state-of-the-art supervised methods in a multi-center retrospective cohort. Notably, it surpasses endoscopists’ diagnostic accuracy in human-machine competitions and enhances their performance when used as an assistive tool. This EC-NBI CAD model significantly improves the accuracy and consistency of diagnosing colorectal lesions, laying a foundation for future early cancer screening, particularly for distinguishing superficial and deep submucosal invasive cancers, pending further expansive multi-center data.