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<p>Accurate prediction of lymph node metastasis (LNM) in T1 esophageal squamous cell cancer is critical for guiding treatment decisions after endoscopic submucosal dissection (ESD). We developed a deep learning-based artificial intelligence model using whole slide images (WSIs) to predict LNM and reduce overtreatment. The model was trained, validated, and internally tested on 160 surgically resected cases (72 LNM+, 88 LNM–) from 374 patients without prior ESD, achieving an AUC of 0.949 (95% CI: 0.912–0.986) on internal test. Further validation was performed on an external ESD cohort comprising clinically high-risk cases with invasion depths from MM to SM2. The model attained an accuracy of 90.1%, sensitivity of 81.8%, specificity of 91.4%, and an F1-score of 69.2%. It correctly classified 90.1% of samples, with a negative predictive value (NPV) of 96.9%. The high NPV and specificity underscore the model’s utility in minimizing overtreatment while preserving diagnostic accuracy in high-risk T1 esophageal cancer.</p>

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Artificial intelligence-driven prediction of lymph node metastasis in T1 esophageal squamous cell carcinoma using whole slide images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月23日

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  • Li-Hua Ren,
  • Yuan Ding,
  • Yue-Xin Zhang,
  • Ke-Han Teng,
  • Lu Wang,
  • Wan-Yue Zhang,
  • Ye Zhu,
  • Jia-Jia Xu,
  • Xiao-Ying Wei,
  • Bin Wang,
  • Kai Hu,
  • Rui-Hua Shi

摘要

Accurate prediction of lymph node metastasis (LNM) in T1 esophageal squamous cell cancer is critical for guiding treatment decisions after endoscopic submucosal dissection (ESD). We developed a deep learning-based artificial intelligence model using whole slide images (WSIs) to predict LNM and reduce overtreatment. The model was trained, validated, and internally tested on 160 surgically resected cases (72 LNM+, 88 LNM–) from 374 patients without prior ESD, achieving an AUC of 0.949 (95% CI: 0.912–0.986) on internal test. Further validation was performed on an external ESD cohort comprising clinically high-risk cases with invasion depths from MM to SM2. The model attained an accuracy of 90.1%, sensitivity of 81.8%, specificity of 91.4%, and an F1-score of 69.2%. It correctly classified 90.1% of samples, with a negative predictive value (NPV) of 96.9%. The high NPV and specificity underscore the model’s utility in minimizing overtreatment while preserving diagnostic accuracy in high-risk T1 esophageal cancer.