Value of Radiotherapy in Primary Gastric Cancer and Establishment of a Prognostic Nomogram Model

Abstract Background: This study aimed to compare the use of radiotherapy (RT) in gastric cancer (GC) patients from the SEER database and established a nomogram to assess cancer-specific survival (CSS).Methods: Patients from the SEER database between 2004 and 2013 were analyzed. Survival was analyzed by Kaplan-Meier curves and log-rank test. Prognostic factors in multivariate Cox analysis were screened to construct a nomogram. The performance the nomogram was validated via concordance index (C-index), calibration plots, and decision curve analyses (DCAs). Results: 9653 GC patients were analyzed totally. In the entire cohort, patients who received pre/postoperative RT had better survival than those who did not receive RT (P = 0.043 and < 0.001, respectively). Similar results were observed in lymph node-positive patients. However, no significant survival benefit was seen in lymph node-negative patients between postoperative RT group and no RT group (P = 0.057), but patients who received postoperative RT and those who did not receive RT experienced better survival than those who received preoperative RT (P < 0.001 and 0.001, respectively). Prognostic factors of GC analyzed by Cox regression model included age, race, tumor grade, tumor histology type, primary tumor site, T stage, lymph node metastasis ratio, RT status, and chemotherapy information independently (P < 0.001). The nomogram was established and showed excellent prediction performance, and its C-index of 0.725 were significantly higher than those of nomograms based AJCC system with C-index at 0.643. In addition, the calibration plots performed good consistency between the predicted and actual survival probabilities, and the DCAs indicated better clinical net benefits than the traditional AJCC system.Conclusions: RT can improve CSS in GC patients, especially those with positive lymph nodes. The construction and verification of a nomogram based on SEER database can effectively predict the survival outcomes of GC patients.