This study aimed to develop and validate a nomogram for predicting the overall survival of cervical adenocarcinoma (CAC) patients using a large database comprising patients with different ethnicities. We enrolled primary CAC cases with complete clinicopathological and survival data from the Surveillance, Epidemiology, and End Results program during 2004 to 2015. For training set samples, this work applied the Cox regression model to obtain factors independently associated with patient prognosis, which could be incorporated in constructing the nomogram. Altogether 3096 qualified cases were enrolled, their survival ranged from 0 to 155 (median, 45.5) months. As revealed by multivariate regression, age, marital status, tumor size, grade, International Federation of Gynecology and Obstetrics (FIGO) classification, pelvic lymph node metastasis, surgery, and chemotherapy served as the factors to independently predict CAC (all P < .05). We later incorporated these factors for constructing the nomogram. According to the concordance index determined, this nomogram had superior discrimination over FIGO classification system (all P < .001). Based on calibration plot, the predicted value was consistent with actual measurement. As revealed by time-independent area under the curves, our constructed nomogram had superior 5-year overall survival over FIGO system. Additionally, according to decision curve analysis, our constructed nomogram showed high clinical usefulness as well as favorable discrimination. Our constructed nomogram attains favorable performances, indicating that it may be applied in predicting survival for CAC patients.
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