Development of a postoperative nomogram to predict overall survival in gastric cancer using variables the AJCC recommends for collection and registration.

Abstract

468 Background: The American Joint Committee on Cancer (AJCC) has increasingly recognized the need for individual risk prediction model for the era of tailored therapy. This study aimed to develop a postoperative gastric cancer nomogram for prediction of overall survival (OS). Methods: The nomogram was developed using data of 4,990 patients with primary gastric cancer who underwent macroscopically complete resection (residual tumor: R0 or R1) at Shizuoka Cancer Center (Shizuoka, Japan), and it was created with a multivariable Cox proportional hazard regression model. Fifteen pathological or host-related variables (age, sex tumor location, tumor size, macroscopic type, histology, depth (pT), number of positive nodes (pN), number of negative nodes, location of positive nodes, lymphovascular invasion, lavage cytology (CY), tumor margin, serum CEA and serum CA19-9) were collected. They are recommended to collect and register by the AJCC. The model was validated internally using measures of discrimination (Harrell’s C-index), calibration and decision curve analysis. A stage-specific subset survival analysis of the three risk groups (low, intermediate, high) calculated using the nomogram was performed. Results: In the development procedure, multivariable analysis for OS selected 11 variables for constructing the nomogram. The developed nomogram showed good discrimination, with a C-index of 0.812; that of the American Joint Committee on Cancer (AJCC) pathological stage was 0.756. The nomogram performed well in the calibration and decision curve analyses. 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 ordinal staging. Conclusions: This new postoperative risk model accurately predicts OS in gastric cancer and can be used for patient counseling in clinical practice and clinical trial eligibility determination.