Nomograms for predicting the survival rate of stage T1−T2 uterine cervical adenocarcinoma patients after hysterectomy

Abstract

To explore factors associated with the overall survival (OS) and cancer-specific survival (CSS) of patients with stage T1–T2 uterine cervical adenocarcinoma (UAC) after hysterectomy and develop nomogram models to predict their prognosis. The data of stage T1–T2 UAC patients after hysterectomy, diagnosed from 2004 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database, were retrieved and divided into a training cohort (n = 2103) and internal validation cohort (n = 1052). Another dataset of eligible patients (n = 107) diagnosed from 2013 to 2019 at the Fujian Maternal and Child Health Hospital was retrieved as the external validation set. Nomograms were developed by the results of univariate and multivariate Cox regression models of OS and CSS. C-index, calibration curve, receiver operating characteristic (ROC) curve and area under the curve (AUC) value were used to assess the prediction model. Age, race, marital status, tumor grade, T stage, tumor size and number of positive lymph nodes were identified as independent prognostic factors for OS and CSS. The number of primary tumors was a specific influencing factor for OS, and postoperative radiotherapy was a beneficial factor for CSS. The C-indexes of OS and CSS nomograms constructed in this study in the training cohort were 0.825 (0800–0.850) and 0.820 (0.789–0.851), higher than the 0.701 (0.671–0.731) and 0.735 (0.702–0.768) of the International Federation of Gynecology and Obstetrics (FIGO) stage, p < 0.001. The prediction ability of the nomogram models was successfully validated in both the internal and external validation cohorts. The established nomogram models had high prediction accuracy for predicting the OS and CSS of stage T1–T2 UAC patients after hysterectomy and were superior to the FIGO stage. They could help clinicians accurately predict patients’ prognoses and select the best treatment plan.