Development of a postoperative nomogram to predict disease-specific mortality in gastric cancer: A competing risk analysis using variables the AJCC recommends be collected and registered

e16099 Background: The American Joint Committee on Cancer (AJCC) has increasingly recognized the need for individual risk prediction model for the era of tailored therapy. In several reports, gastric cancer postoperative nomograms for predicting overall or disease-specific survival have been described. The AJCC Precision Medicine Core introduced the attractiveness of disease-specific mortality (DSM) as an endpoint of risk model. This study aimed to develop the first pretreatment gastric cancer postoperative nomogram for predicting DSM that considers competing risks (CRs). Methods: The nomogram was developed using data of 2,501 patients with primary gastric cancer who are potential candidate for adjuvant chemotherapy based on NCCN guideline, and it was created with a multivariable Fine and Gray subdistribution proportional hazard regression model. Fifteen pathological or non-tumor-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. Results: In the development procedure, multivariable analysis for DSM selected 12 variables (age, tumor location, macroscopic type, histology, pT, pN, number of negative nodes, location of positive nodes, lymphovascular invasion, CY, tumor margin and serum CEA) for constructing the nomogram. The developed nomogram showed good discrimination, with a C-index of 0.802(0.786-0.817) that of the American Joint Committee on Cancer (AJCC) pathological stage was 0.714 (0.697-0.734). The calibration appeared to be accurate for the 5-year prediction. Compared with scenarios in which no prediction model was used for postoperative treatment decision making (i.e., assume all or assume none), the nomograms had a favorable net benefit across a wide range of decision threshold probabilities between 5-year DSM probabilities of approximately 5 and 95%. Conclusions: This new postoperative risk model accurately predicts DSM in gastric cancer and can be used for patient counseling in clinical practice.