Machine learning–based personalized prediction of gastric cancer incidence using the endoscopic and histologic findings at the initial endoscopy

Background and Aims Accurate risk stratification for gastric cancer is required for optimal endoscopic surveillance in patients with chronic gastritis. We aimed to develop a machine learning (ML) model that incorporates endoscopic and histologic findings for an individualized prediction of gastric cancer incidence. Methods We retrospectively evaluated 1099 patients with chronic gastritis who underwent EGD and biopsy sampling of the gastric mucosa. Patients were randomly divided into training and test sets (4:1). We constructed a conventional Cox proportional hazard model and 3 ML models. Baseline characteristics, endoscopic atrophy, and Operative Link on Gastritis-Intestinal Metaplasia Assessment (OLGIM)/Operative Link on Gastritis Assessment (OLGA) stage at initial EGD were comprehensively assessed. Model performance was evaluated using Harrel's c-index. Results During a mean follow-up of 5.63 years, 94 patients (8.55%) developed gastric cancer. The gradient-boosting decision tree (GBDT) model achieved the best performance (c-index from the test set, .84) and showed high discriminative ability in stratifying the test set into 3 risk categories (P < .001). Age, OLGIM/OLGA stage, endoscopic atrophy, and history of malignant tumors other than gastric cancer were important predictors of gastric cancer incidence in the GBDT model. Furthermore, the proposed GBDT model enabled the generation of a personalized cumulative incidence prediction curve for each patient. Conclusions We developed a novel ML model that incorporates endoscopic and histologic findings at initial EGD for personalized risk prediction of gastric cancer. This model may lead to the development of effective and personalized follow-up strategies after initial EGD. Accurate risk stratification for gastric cancer is required for optimal endoscopic surveillance in patients with chronic gastritis. We aimed to develop a machine learning (ML) model that incorporates endoscopic and histologic findings for an individualized prediction of gastric cancer incidence. We retrospectively evaluated 1099 patients with chronic gastritis who underwent EGD and biopsy sampling of the gastric mucosa. Patients were randomly divided into training and test sets (4:1). We constructed a conventional Cox proportional hazard model and 3 ML models. Baseline characteristics, endoscopic atrophy, and Operative Link on Gastritis-Intestinal Metaplasia Assessment (OLGIM)/Operative Link on Gastritis Assessment (OLGA) stage at initial EGD were comprehensively assessed. Model performance was evaluated using Harrel's c-index. During a mean follow-up of 5.63 years, 94 patients (8.55%) developed gastric cancer. The gradient-boosting decision tree (GBDT) model achieved the best performance (c-index from the test set, .84) and showed high discriminative ability in stratifying the test set into 3 risk categories (P < .001). Age, OLGIM/OLGA stage, endoscopic atrophy, and history of malignant tumors other than gastric cancer were important predictors of gastric cancer incidence in the GBDT model. Furthermore, the proposed GBDT model enabled the generation of a personalized cumulative incidence prediction curve for each patient. We developed a novel ML model that incorporates endoscopic and histologic findings at initial EGD for personalized risk prediction of gastric cancer. This model may lead to the development of effective and personalized follow-up strategies after initial EGD.