A web-based prediction model for long-term cancer-specific survival of middle-aged patients with early-stage gastric cancer: a multi-institutional retrospective study

Abstract

Background: This study constructed and validated a prognostic model to evaluate long-term cancer-specific survival (CSS) in middle-aged patients with early gastric cancer (EGC).

Methods: We extracted clinicopathological data from relevant patients between 2004 and 2015 from Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided the patients into a training group (N = 688) and a validation group (N = 292). In addition, 102 Chinese patients were enrolled for external validation. Univariate and multivariate Cox regression models were used to screen for independent prognostic factors, and a nomogram was constructed to predict CSS. We used the concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the predictive performance of the model.

Results: Univariate and multivariate COX regression analyses showed that tumor location, differentiation grade, N stage, chemotherapy, and number of regional nodes examined were independent risk factors for prognosis, and these factors were used to construct the nomogram. The C-index of the model in the training cohort, internal validation cohort, and external validation cohort was 0.749 (95% CI 0.699-0.798), 0.744 (95% CI 0.671-0.818), and 0.807 (95% CI 0.721-0.893), respectively. The calibration curve showed that the model had an excellent fit. The DCA curve showed that the model had good predictive performance and practical clinical value.

Conclusion: This study developed and validated a new nomogram to predict CSS in middle-aged patients with EGC. The prediction model has unique and practical value and can help doctors carry out individualized treatment and judge prognosis.

Keywords: Cancer-specific survival; Early gastric cancer; Nomogram; SEER database.