Development and validation of a pretreatment nomogram to predict overall survival in gastric cancer

Background Pretreatment clinical staging is essential to select therapy. However, there have been no published pretreatment gastric cancer nomograms constructed using pretreatment clinical prognostic factors, including in nonresection patients. We aimed to develop a new pretreatment gastric cancer nomogram for individualized prediction of overall survival (OS). Methods The nomogram was developed using data of 5231 Japanese gastric cancer patients, and it was created with a Cox regression model. Fifteen clinical variables, which were obtained at pretreatment, were collected and registered. Data of two independent cohorts of patients from Seoul St. Mary's Hospital (1001 patients), and the University of Verona (389 patients) formed the external validation cohorts. The model was validated internally and externally using measures of discrimination (Harrell's C‐index), calibration, and decision curve analysis. Results The developed nomogram showed good discrimination, with a C‐index of 0.855; that of the American Joint Committee on Cancer (AJCC) clinical stage was 0.819. In the external validation procedure, the C‐indexes were 0.856 (AJCC, 0.795) in the Seoul St. Mary's cohort and 0.714 (AJCC, 0.648) in the University of Verona cohort. The nomogram performed well in the calibration and decision curve analyses when applied to both the internal and external validation cohorts. A stage‐specific subset survival analysis of the three risk groups calculated using the nomogram also showed the superiority of nomogram‐prediction when compared to AJCC. Conclusion This new pretreatment model accurately predicts OS in gastric cancer and can be used for patient counseling in clinical practice and stratification in clinical trials. The newly developed gastric cancer pretreatment nomogram can accurately predict overall survival and thereby improve risk stratification and patient selection for routine clinical decision‐making and future clinical trials. This model will improve on the traditional categorical AJCC system in the future.