A Nomogram for Predicting the Cancer-Specific Survival of Patients with Initially Diagnosed Metastatic Gastric Cancer

Autor: Jun Ren, Yuedi Dai, Fei Chao, Dong Tang, Jiawei Gu, Gengming Niu, Jie Xia, Xin Wang, Tao Song, Zhiqing Hu, Runqi Hong, Chongwei Ke
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Clinical Medicine Insights: Oncology, Vol 16 (2022)
Druh dokumentu: article
ISSN: 1179-5549
11795549
DOI: 10.1177/11795549221142095
Popis: Background: There are few models to predict the survival of patients of different ethnicities initially diagnosed with metastatic gastric cancer (mGC). Therefore, the aim of this study was to construct a nomogram to predict the cancer-specific survival (CSS) of these patients. Methods: Data for 994 patients initially diagnosed with mGC between 2000 and 2013 were extracted from the Surveillance, Epidemiology, and End Results database. Patients were randomly classified into a training (n = 696) or internal validation (n = 298) cohort, and a cohort of 133 patients from Fudan cohort was used for external validation. A nomogram to predict the CSS of mGC patients was derived and validated using a concordance index (C-index), calibration curves, and decision-curve analysis (DCA). Results: Multivariate Cox regression indicated that five factors were independent predictors of CSS: differentiation grade, T stage, N stage, metastatic site at diagnosis, and with or without chemotherapy. Thus, these factors were integrated into the nomogram model. The C-index value of the nomogram model was 0.63 (95% CI: 0.60–0.65), and those of the internal and external validation cohorts were 0.60 (95%: CI 0.55–0.64) and 0.63 (95%: CI 0.57–0.69), respectively. The calibration curves showed good consistency between the actual and predicted survival rates in both the internal and external validation cohorts. The DCA also showed the clinical utility of the nomogram model. Conclusions: We established a practical nomogram to predict the CSS of patients initially diagnosed with mGC. The nomogram can be used for individualized prediction of survival and to guide clinicians in making treatment decisions.
Databáze: Directory of Open Access Journals
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