Clinical characteristics, survival and prognostic nomogram for patients with esophageal mucinous adenocarcinoma: a SEER population-based analysis.

Abstract

Background: Esophageal mucinous adenocarcinoma (MAC) is a rare adenocarcinoma (AC) subtype. Limited research exists on its incidence, survival rates, and treatment responses. This study utilized the Surveillance, Epidemiology, and End Results (SEER) database to compare the clinical characteristics and prognoses of patients with esophageal MAC, AC, and signet-ring cell carcinoma (SRC), and developed nomograms to predict outcomes.

Methods: Patient information was retrieved from the SEER database from 2004 to 2015. The baseline characteristics were balanced using propensity score matching (PSM). Prognostic factors for esophageal MAC patients were identified by univariate and multivariate Cox analyses.

Results: A total of 497 esophageal MAC, 21,109 esophageal AC and 1,144 esophageal SRC patients were selected. MAC patients were more likely to have a higher pathological grade (P<0.001), and later T stage (P<0.001) and American Joint Committee on Cancer (AJCC) stage (P=0.003) than AC patients. The proportion of grade I-II MAC patients was higher than that of SRC patients. The overall survival (OS) and cancer-specific survival (CSS) of MAC patients were similar to those of AC patients. However, MAC patients had significantly better OS and CSS than SRC patients. After PSM analysis, the OS and CSS of MAC patients were similar to those of AC and SRC patients (all P>0.05). In MAC patients, N stage, M stage, and surgery were independent predictive factors for both OS and CSS. The area under the curve (AUC) and calibration curves demonstrated high precision and discrimination. Decision curve analysis (DCA) demonstrated that the CSS and OS nomograms have high potential clinical value.

Conclusions: Esophageal MAC patients had similar survival compared with esophageal AC and esophageal SRC patients. The nomograms provide OS and CSS predictions for MAC patients, to aid clinicians in predicting patients' prognoses.