Related Experiment Video
Updated: Oct 19, 2026
Establishment and Evaluation of a Risk Prediction Model for Pathological Escalation of Gastric Low-Grade Intraepithelial Neoplasia
Published on: February 16, 2024
Development and validation of an explainable model for predicting lymph node metastasis in early gastric cancer: a
Ran Xiong1, Xiangfei Sun1, Qi Jiang2
1Department of General Surgery, Zhongshan Hospital, Fudan University School of Medicine, Shanghai, China.
Background:
Accurate preoperative prediction of lymph node metastasis (LNM) is essential for treatment selection in early gastric cancer (EGC), particularly when considering endoscopic submucosal dissection. Existing approaches show limited sensitivity for metastatic disease and frequently lack external or prospective validation. We aimed to develop and validate an interpretable machine-learning model for individualized preoperative prediction of LNM in EGC.
Methods:
In this multicenter study, four cohorts from three tertiary centers were included. Patients with pathologically confirmed EGC who underwent gastrectomy with lymph node dissection were retrospectively enrolled for training and external validation. A temporally independent cohort (September 2023-December 2024) was prospectively enrolled for validation. A preoperative pathology validation cohort (January 2025-January 2026) was also analyzed. Clinicopathological variables were selected using LASSO regression. SMOTE was applied to address class imbalance. Six machine-learning algorithms were compared. Model performance was assessed using AUC and PR-AUC, and interpretability was evaluated using SHapley Additive exPlanations. A web-based calculator was developed.
Results:
Among 3412 patients in the training cohort, 467 (13.7%) had LNM. XGBoost achieved the best discrimination (training AUC = 0.9289) and maintained robust performance in the external (AUC = 0.8583; PR-AUC = 0.6079) and prospective validation cohorts (AUC = 0.8766; PR-AUC = 0.5137). In the preoperative pathology validation cohort, the model achieved an AUC of 0.8653 and a PR-AUC of 0.6050. Key predictors included lymphovascular/neural invasion, invasion depth, tumor location, differentiation, and tumor size.
Conclusions:
This externally and prospectively validated, interpretable machine-learning model provides transparent individualized risk estimates and may support personalized decision-making between endoscopic and surgical management in EGC.
