ArticleJournal of gastrointestinal oncology2026
Development and external validation of a prognostic nomogram for cancer-specific survival in elderly patients with early-stage gastric cancer.
Article in Journal of gastrointestinal oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Elderly patients with early-stage gastric cancer (EGC) often present with heterogeneous prognoses, yet, reliable tools for individualized risk stratification remain limited. This study aimed to develop and externally validate an oncology-focused prognostic model to stratify the risk of cancer-specific survival (CSS) in elderly patients with EGC. Methods: Data on individuals diagnosed with EGC between 2000 and 2020 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Patients from Zhongshan Hospital were included for external validation. Univariate and multivariate Cox regression analyses were performed to develop the nomogram. The concordance index (C-index), calibration curves and decision curve analysis (DCA) were calculated to assess the clinical applicability of the model in predicting 3-, 5-, and 10-year CSS. Results: A total of 1,898 individuals were extracted from the SEER database, and 174 patients were enrolled for external validation. Age, sex, histologic type, tumor stage, radiotherapy, tumor size and the log odds of positive lymph nodes (LODDS) were identified as the most important independent prognostic factors for CSS and were utilized to develop a prognostic nomogram model. The C-index values were 0.697, 0.668 and 0.697 in the three cohorts, suggesting that the nomogram model demonstrated effective performance. The calibration curves and DCA provided great visualization of the clinical utility. Additionally, the differences in the Kaplan-Meier curves suggested that the model exhibited good discriminatory ability. Conclusions: The nomogram complements tumor-node-metastasis staging by enabling cancer-specific risk stratification and may provide additional prognostic information when interpreted alongside clinical assessment.
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