ORIGINAL RESEARCH article
Front. Oncol.
Sec. Gastrointestinal Cancers: Gastric and Esophageal Cancers
This article is part of the Research TopicReal-World Clinical and Translational Research in Gastrointestinal CancersView all 26 articles
Machine Learning Models Based on Log Odds of Positive Lymph Nodes for Predicting Survival in T1N+ Gastric Cancer
Provisionally accepted
- Nankai University, Tianjin, China
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Background Although early gastric cancer (EGC) is generally limited to the mucosal and submucosal layers, lymph node metastasis can still occur, which may worsen the prognosis, particularly when the number of examined lymph nodes (ELNs) is inadequate. This study introduces log odds of positive lymph nodes (LODDS) as a prognostic factor and integrates it with machine learning to improve survival predictions in T1N+ gastric cancer (GC). Methods This retrospective study used data from the Surveillance, Epidemiology, and End Results (SEER)Program and an independent validation cohort from the Chinese People's Liberation Army General Hospital. Predictive factors were selected using LASSO regression and multivariate Cox regression. Cox proportional-hazards (CoxPH), random survival forest (RSF), and XGBoost models were developed to predict overall survival (OS). Model interpretability and feature importance were evaluated using the SHapley Additive exPlanations (SHAP) method. Results A total of 419 T1N+ GC patients from the SEER database and 193 from our institution were included in the study. LODDS staging was identified as an independent prognostic factor, demonstrating superior discriminatory power compared to N staging (C-index 0.65 vs. 0.57). Based on the Brier score, area under the ROC curve (AUC), and C-index, the RSF model outperformed both the Cox model and XGBoost model. The RSF model achieved a C-index of 0.79 in the training cohort and 0.80 in the validation cohort, indicating favorable discrimination and calibration, with Brier scores below 0.25. Conclusions Integrating LODDS staging into the RSF model, alongside other clinical features, provides a highly accurate tool for survival prediction in T1N+ GC patients.
Keywords: Early Gastric Cancer, lymph node metastasis, LODDS staging, machine learning, prognosis
Received: 06 Jun 2025; Accepted: 17 Dec 2025.
Copyright: © 2025 Liu, Cui, Yuan, Wang, An, Li, Cui and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jianxin Cui
Bo Wei
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