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Background <p>Gastric cancer (GC) is a major public health challenge in China, highlighting the need for effective risk stratification to improve early detection. This study aimed to validate existing GC risk assessment models and develop a locally adapted screening approach for GC and precancerous lesions.</p> Methods <p>From 2022 to 2024, a total of 144,329 participants were recruited through a large multicenter endoscopic screening cohort in Zhejiang Province, China, with baseline data collected via interviews and endoscopic examinations. Participants screened in 2022 were used for model development, whereas those screened during 2023–2024 were used for validation and comparative analyses. Least absolute shrinkage and selection operator regression and logistic regression were used to construct a new model and a weighted point-based risk score. Six previously published models were externally validated for comparison across the validation set. Model performance and clinical utility were assessed and compared using area under the curve (AUC), detection rates, and decision curve analysis for both GC and the composite outcome of GC/high-grade intraepithelial neoplasia (HGIN).</p> Results <p>A total of 1,419 GC cases were identified in the screening cohort. The newly developed model demonstrated favorable discriminative performance, with an AUC of 0.759 (95% CI: 0.744–0.775) for GC and 0.754 (95% CI: 0.740–0.768) for the composite outcome of GC/HGIN in the validation set. External evaluation of six previously published models showed AUCs ranging from 0.594 (95% CI: 0.558–0.629) to 0.742 (95% CI: 0.725–0.758) for GC, and from 0.602 (95% CI: 0.570–0.635) to 0.744 (95% CI: 0.730–0.759) for GC/HGIN. Risk stratification analyses demonstrated progressively increasing odds of both GC and GC/HGIN across higher-risk quartiles in most evaluated models. In the newly developed model, the highest-risk quartile accounted for 62.25% of GC cases and 58.56% of HGIN cases, indicating substantial enrichment of clinically significant lesions. Decision curve analysis indicated a favorable net benefit of the newly developed model across the evaluated range of threshold probabilities.</p> Conclusions <p>The newly developed model demonstrated favorable discrimination, stable geographic performance, and effective risk stratification in a large multicenter screening cohort. These findings support its potential utility for risk-adapted GC screening in Chinese populations.</p> Graphical abstract <p></p>

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Comparative validation and local adaptation of gastric cancer risk assessment models in a large multicenter endoscopic screening cohort in China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月24日

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  • Xue Li,
  • Ying Chen,
  • Juan Zhu,
  • Wei-Yan Yu,
  • Huan-Qing Tao,
  • Gang Chen,
  • Hui-Zhang Li,
  • Ling-Bin Du

摘要

Background

Gastric cancer (GC) is a major public health challenge in China, highlighting the need for effective risk stratification to improve early detection. This study aimed to validate existing GC risk assessment models and develop a locally adapted screening approach for GC and precancerous lesions.

Methods

From 2022 to 2024, a total of 144,329 participants were recruited through a large multicenter endoscopic screening cohort in Zhejiang Province, China, with baseline data collected via interviews and endoscopic examinations. Participants screened in 2022 were used for model development, whereas those screened during 2023–2024 were used for validation and comparative analyses. Least absolute shrinkage and selection operator regression and logistic regression were used to construct a new model and a weighted point-based risk score. Six previously published models were externally validated for comparison across the validation set. Model performance and clinical utility were assessed and compared using area under the curve (AUC), detection rates, and decision curve analysis for both GC and the composite outcome of GC/high-grade intraepithelial neoplasia (HGIN).

Results

A total of 1,419 GC cases were identified in the screening cohort. The newly developed model demonstrated favorable discriminative performance, with an AUC of 0.759 (95% CI: 0.744–0.775) for GC and 0.754 (95% CI: 0.740–0.768) for the composite outcome of GC/HGIN in the validation set. External evaluation of six previously published models showed AUCs ranging from 0.594 (95% CI: 0.558–0.629) to 0.742 (95% CI: 0.725–0.758) for GC, and from 0.602 (95% CI: 0.570–0.635) to 0.744 (95% CI: 0.730–0.759) for GC/HGIN. Risk stratification analyses demonstrated progressively increasing odds of both GC and GC/HGIN across higher-risk quartiles in most evaluated models. In the newly developed model, the highest-risk quartile accounted for 62.25% of GC cases and 58.56% of HGIN cases, indicating substantial enrichment of clinically significant lesions. Decision curve analysis indicated a favorable net benefit of the newly developed model across the evaluated range of threshold probabilities.

Conclusions

The newly developed model demonstrated favorable discrimination, stable geographic performance, and effective risk stratification in a large multicenter screening cohort. These findings support its potential utility for risk-adapted GC screening in Chinese populations.

Graphical abstract