Adenosquamous Carcinoma of the Cervix: A Population-Based Analysis

Abstract

BackgroundDue to the rarity of adenosquamous carcinoma of the cervix (ASCC), studies on the incidence, prognostic factors, and treatment outcomes of ASCC remain scarce. Therefore, we performed a retrospective population-based study to systematically investigate the characteristics of ASCC patients.MethodsPatients with a histopathologically confirmed diagnosis of ASCC were enrolled from the Surveillance, Epidemiology, and End Results database between 1975 and 2016. Univariate and multivariate Cox regression analyses were performed to identify the potential predictors of cancer-specific survival (CSS) in patients with ASCC. Selected variables were integrated to establish a predictive nomogram and the predictive performance of the nomogram was estimated using Harrell’s concordance index (C-index), calibration curve, and decision curve analysis (DCA).ResultsA total of 1142 ASCC patients were identified and included in this study and were further randomized into the training and validation cohorts in a 7:3 ratio. The age-adjusted incidence of ASCC declined from 0.19 to 0.09 cases per 100,000 person-years between 2000 and 2017, with an annual percentage change of -4.05% (P<0.05). We identified age, tumor grade, FIGO stage, tumor size, and surgical procedure as independent predictors for CSS in ASCC patients and constructed a nomogram to predict the 3- and 5-year CSS using these prognostic factors. The calibration curve indicated an outstanding consistency between the nomogram prediction and actual observation in both the training and testing cohorts. The C-index was 0.7916 (95% CI: 0.7990-0.8042) and 0.8148 (95% CI: 0.7954-0.8342) for the training and testing cohorts, respectively, indicating an excellent discrimination ability of the nomogram. The DCA showed that the nomogram exhibited more clinical benefits than the FIGO staging system.ConclusionsWe established and validated an accurate predictive nomogram for ASCC patients based on several clinical characteristics. This model might serve as a useful tool for clinicians to estimate the prognosis of ASCC patients.