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. 2026 Aug 18;41(9):2953–2954. doi: 10.1111/jgh.70519

Comment on: Five‐Year Unified Risk Prediction Model for Gastric and Esophageal Cancer Based on Nationwide Health Check‐Up Data in Japan: J‐GESS Score

Yichao Mo 1, Xiaowen Su 1, Qinshou Chen 1, Siyao Che 1,✉
PMCID: PMC13534263  PMID: 42609056

Dear Editor,

1.

We carefully read the article published by Watanabe and colleagues [1] regarding the newly developed J‐GESS scoring system for predicting 5‐year risk of gastric cancer and esophageal squamous cell carcinoma. Combining nationwide physical examination and endoscopic data from multiple Japanese centers, this unified risk model solves the clinical demand for risk stratification before gastroscopy follow‐up, and the rigorous cohort design and statistical processing are worthy of recognition. Still, we have several comments to share.

The J‐GESS score includes age, gender, smoking history, and endoscopic gastric atrophy as independent predictors, while drinking status did not reach statistical significance in multivariate analysis. As the authors themselves discussed, key ESCC risk factors including heavy alcohol intake, ALDH2 genotype, and alcohol flushing were not collected in routine health check‐up data and excluded from model building. In East Asia, alcohol‐related genetic susceptibility is closely linked to esophageal squamous cancer; therefore, the current scoring system may underestimate cancer risk among heavy‐drinking susceptible populations. We suggest adding simple questionnaire‐based drinking‐related indicators to improve the predictive performance for ESCC in future model updates.

This research focused only on GC and ESCC and excluded esophageal adenocarcinoma. However, EAC incidence has been rising year by year in Japan and surrounding East Asian countries. Barrett's esophagus, the precancerous lesion of EAC, can be easily identified during initial screening endoscopy. It would be helpful to add Barrett's esophagus classification as an auxiliary index, so that high‐risk individuals for EAC can be screened separately outside the existing J‐GESS risk grouping.

All research subjects came from Japanese health screening crowds without external verification in other high‐incidence areas such as China and South Korea. Differences in Helicobacter pylori infection rate, dietary environment, and gastric atrophy epidemiology exist across East Asian populations, so the critical cutoff values of 4 points and 6 points cannot be directly applied in other regions. Multinational external validation is needed to adjust scoring standards for wider clinical use.

Gastric mucosal atrophy graded by Kimura–Takemoto is the most weighted predictor in this score, but subjective differences always exist when different endoscopists evaluate atrophy grade in routine practice. Standardized assessment aided by endoscopic artificial intelligence can lower interobserver error and make J‐GESS more stable in primary screening, which needs further prospective research confirmation.

Overall, J‐GESS provides a concise tool to rationally allocate limited endoscopic screening resources in Japan. Supplementing alcohol‐related risk markers, Barrett's esophagus indicators, and cross‐country validation will further optimize this scoring system for upper gastrointestinal cancer prevention in Asian populations.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Reference

  • 1. Watanabe K., Fukuda S., Kubota D., et al., “Five‐Year Unified Risk Prediction Model for Gastric and Esophageal Cancer Based on Nationwide Health Check‐Up Data in Japan: J‐GESS Score,” Journal of Gastroenterology and Hepatology 41, no. 7 (2026): 2073–2081, 10.1111/jgh.70409. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.


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