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

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.