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Peer-reviewedCC BY 4.0Cancers

Treatment Effects of Different Therapeutic Strategies and Development of a Prognostic Prediction Model for Early Gastric Cancer: A Population-Based and Multicenter Study

  • Ruiying Jing1
  • Shimei Sun2
  • Tian Tian1
  • Zhiyu Xiang3
  • Wei Su3
  • Hunter Wang4
  • Michael J. Bourke4
  • Shengli Lin3
  • Yaoyao Gong1,5
5 affiliations
  1. 1Department of Gastroenterology, Jiangsu Province Hospital (the First Affiliated Hospital of Nanjing Medical University), Nanjing 210029, China
  2. 2Department of Gastroenterology, Hospital of Sihong, No. 31, Jiankang Road, Sihong 223900, China
  3. 3Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University & Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China
  4. 4Department of Gastroenterology and Hepatology, Westmead Hospital, Sydney 2145, Australia
  5. 5Department of Gastroenterology, Chongqing Hospital of Jiangsu Province Hospital, Chongqing 401420, China
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Abstract

Background: Early gastric cancer (EGC) lacks a predictive tool integrating endoscopic therapy (ET), gastrectomy (G), and chemoradiotherapy (CRT) to support individualized risk stratification. This study compared cancer-specific survival (CSS) and overall survival (OS) across the four treatment strategies and developed and externally validated a CSS prediction model incorporating oncologic characteristics and treatment strategy (presented as a nomogram and online tool). Methods: In a U.S. cohort (2017–2021), 1766 patients with EGC were classified into four treatments groups (ET, ET + CRT, G, G + CRT); an independent multicenter cohort (n = 1333) provided external validation. Kaplan–Meier curves, log-rank tests, and Cox regression assessed unadjusted differences, with confounding balanced by stabilized inverse probability of treatment weighting (sIPTW). Predictors were selected by univariable Cox, LASSO, Boruta, and best-subset regression; performance was evaluated by C-index, calibration curves, and decision curve analysis. Results: After sIPTW adjustment, CSS did not differ significantly between ET and G (p = 0.856), whereas G was associated with better CSS than G + CRT (p < 0.05). In exploratory analyses of node-positive patients, G + CRT was associated with lower cancer-specific mortality than G alone (p < 0.05). The CSS prediction model was validated internally and externally (AUCs for 1-, 3-, and 5-year CSS: 0.769, 0.737, 0.717 internally; 0.943, 0.890, 0.872 externally). Conclusions: This is the first externally validated EGC CSS model integrating all four treatments. For eligible patients, ET was associated with survival comparable to G and superior to G + CRT; among node-positive patients, G + CRT was associated with lower mortality than G alone. The model supports individualized prediction and treatment decisions.

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Cite

BibTeXRIS
BibTeX
@article{jing2026treatment,
  title = {{Treatment Effects of Different Therapeutic Strategies and Development of a Prognostic Prediction Model for Early Gastric Cancer: A Population-Based and Multicenter Study}},
  author = {Ruiying Jing and Shimei Sun and Tian Tian and Zhiyu Xiang and Wei Su and Hunter Wang and Michael J. Bourke and Shengli Lin and Yaoyao Gong},
  journal = {Cancers},
  year = {2026},
  month = oct,
  doi = {10.3390/cancers18213488},
  url = {https://www.mdpi.com/2072-6694/18/21/3488},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Treatment Effects of Different Therapeutic Strategies and Development of a Prognostic Prediction Model for Early Gastric Cancer: A Population-Based and Multicenter Study
AU  - Ruiying Jing
AU  - Shimei Sun
AU  - Tian Tian
AU  - Zhiyu Xiang
AU  - Wei Su
AU  - Hunter Wang
AU  - Michael J. Bourke
AU  - Shengli Lin
AU  - Yaoyao Gong
PY  - 2026
DA  - 2026/10/29
JO  - Cancers
DO  - 10.3390/cancers18213488
UR  - https://www.mdpi.com/2072-6694/18/21/3488
AB  - Background: Early gastric cancer (EGC) lacks a predictive tool integrating endoscopic therapy (ET), gastrectomy (G), and chemoradiotherapy (CRT) to support individualized risk stratification. This study compared cancer-specific survival (CSS) and overall survival (OS) across the four treatment strategies and developed and externally validated a CSS prediction model incorporating oncologic characteristics and treatment strategy (presented as a nomogram and online tool). Methods: In a U.S. cohort (2017–2021), 1766 patients with EGC were classified into four treatments groups (ET, ET + CRT, G, G + CRT); an independent multicenter cohort (n = 1333) provided external validation. Kaplan–Meier curves, log-rank tests, and Cox regression assessed unadjusted differences, with confounding balanced by stabilized inverse probability of treatment weighting (sIPTW). Predictors were selected by univariable Cox, LASSO, Boruta, and best-subset regression; performance was evaluated by C-index, calibration curves, and decision curve analysis. Results: After sIPTW adjustment, CSS did not differ significantly between ET and G (p = 0.856), whereas G was associated with better CSS than G + CRT (p < 0.05). In exploratory analyses of node-positive patients, G + CRT was associated with lower cancer-specific mortality than G alone (p < 0.05). The CSS prediction model was validated internally and externally (AUCs for 1-, 3-, and 5-year CSS: 0.769, 0.737, 0.717 internally; 0.943, 0.890, 0.872 externally). Conclusions: This is the first externally validated EGC CSS model integrating all four treatments. For eligible patients, ET was associated with survival comparable to G and superior to G + CRT; among node-positive patients, G + CRT was associated with lower mortality than G alone. The model supports individualized prediction and treatment decisions.
SN  - 2072-6694
ER  -