European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
October 14, 2026 · via Europe PMC
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.
Xiong R, Sun X, Jiang Q, +8 more, Gao X
AI summary — for doctors, generated from the abstract only
An interpretable XGBoost model predicted lymph node metastasis in early gastric cancer with good discrimination across external and prospective cohorts.
What was studied
A multicenter model development study with training (3412 patients), external, prospective, and preoperative-pathology validation cohorts, comparing six machine-learning algorithms with SHAP interpretation and a web calculator.
Key findings
- 467 of 3412 training patients (13.7%) had lymph node metastasis
- XGBoost AUC was 0.8583 in external and 0.8766 in prospective validation cohorts
- AUC was 0.8653 in the preoperative pathology cohort
- Key predictors included lymphovascular/neural invasion, invasion depth, tumor location, differentiation, and tumor size
Clinical relevance
May help decide between endoscopic resection and surgery in early gastric cancer.
Who should care
Limitations
Retrospective development, use of SMOTE, and performance (PR-AUC about 0.51-0.61) leaves room for false positives; not tested in routine practice.
Promising, validated risk tool, but broader implementation studies are needed.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
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.
DOI
10.1016/j.ejso.2026.112310
PMID
42892022
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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