Development and validation of an explainable model for predicting lymph node metastasis in early gastric cancer: a multicenter retrospective study with external and prospective validation
In brief
A model predicts lymph node spread in early gastric cancer with 0.88 AUC
Trained on 3,412 patients, the explainable model maintained strong discrimination in external and prospective validation, with AUCs of 0.86 and 0.88. It uses clinical and tumor features to estimate an individual's risk and could inform choices between endoscopic and surgical treatment. Whether using its estimates improves treatment decisions or patient outcomes remains unknown.
- Journal
- European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology (Q1)
- Published
- 14 October 2026
- Study design
- Prospective / inception cohort
- Evidence level
- Level 2, Moderate (CEBM 2b)
- Authors
- Ran Xiong, Xiangfei Sun, Qi Jiang, Zipeng Xu, Yinwen Sun, Yuning Zhou, et al.
- PMID
- 42892022
- DOI
- 10.1016/j.ejso.2026.112310
Why clinicians should know about it
- Picked for Health Informatics (paper of the day, 20 October 2026): Explainable model predicting lymph node metastasis in early gastric cancer
Abstract
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.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.