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

ABSTRACT: 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.

SUBMITTER: Ren LH 

PROVIDER: S-EPMC12749806 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature