Impact of chemotherapy and radiotherapy on the survival of elderly esophageal cancer patients undergoing surgery: a SEER database analysis

Abstract

Abstract Background Esophageal cancer (EC) is a common and lethal carcinoma; however, the effectiveness and feasibility of the chemo- and radio-therapy (CRT) for the elderly patients (≥ 70 years) with surgery have not been fully discussed. The purpose of this study was to investigate the potential effect of CRT on the prognosis. Methods A total of 1085 patients (534 CRT patients vs. 551 non-CRT patients) from 1998 to 2016 were collected from the Surveillance, Epidemiology, and End Results database according to the inclusion and exclusion criteria. Using the competing risk regression and survival analysis, an overall estimation of the effectiveness of CRT was performed on a well-balanced cohort via performing propensity score matching. Then, the specific impact of CRT on high- (n = 557) and low-risk (n = 528) cohorts derived from the nomogram’s risk quantification for every patient were further evaluated respectively. Additionally, the advantages of the nomogram model and the conventional tumor, node, metastasis (TNM, 6th revision) staging system were compared. Results A better survival outcome was observed among patients receiving both surgery and CRT than those who underwent surgery alone (HR: 0.55, 95% CI 0.45–0.68, P < 0.001), especially for those with tumors characterized by poor differentiation, large tumor size, advanced T staging, lymphatic metastasis, and distant metastasis (HR: 0.48, 95% CI 0.39–0.59, P < 0.001), while no benefit was observed among the low-risk patients. Furthermore, the newly established nomogram model might be better than the TNM (6th revision) staging system but more data needed. Conclusion Aggressive treatments, such as surgery, chemotherapy, and radiotherapy, were considered effective for selected elderly patients with EC according to the newly established nomogram model.

Keywords

• Elderly patient
• Esophageal cancer
• Nomogram
• Predictive model
• Cancer-specific death