Development And Validation Of A Novel Nomogram To Predict Cancer-Specific Survival In Patients With Uterine Cervical Adenocarcinoma

Background: The treatment strategies and prognostic factors for uterine cervical adenocarcinoma (UAC) primarily refer to that for squamous cell carcinoma (SCC). However, the biological behavior, treatment outcomes of UAC differ from that of SCC. This study aimed to develop and validate a prognostic nomogram for predicting the probability of 3and 5-year cancer-specific survival (CSS) in patients with UAC. Methods: A total of 8,991 UAC patients from the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. Patients diagnosed between 1988 and 2010 (n=5,655) were enrolled for model development and internal validation, and those diagnosed between 2011 and 2016 (n=3,336) were used for temporal validation. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to select predictors of CSS. Cox hazard regression analysis was used to construct the model, which was presented as a static nomogram and web-based dynamic nomogram. The nomogram was internally validated using the bootstrap resampling method and underwent temporal validation. Results: Tumor grade, stage T, stage N, stage M, tumor size, and surgery of the primary site were identified as independent prognostic factors for CSS and subsequently incorporated into construction of the nomogram. The nomogram could accurately predict 3and 5-year CSS with an optimism adjusted c-statistic of 0.90 [95% confidence intervals (CI): 0.89-0.91] and 0.89 (95% CI: 0.88-0.91) after internal validation, respectively; while, after temporal validation, the statistics were 0.89 (95% CI: 0.87-0.91) and 0.88 (95% CI: 0.83-0.94), respectively. The internal and temporal calibration plots demonstrated good consistency between the predicted and observed values of CSS. Based on the model, the cases were stratified into highand low risk groups. The Kaplan-Meier plot showed that high-risk patients exhibited significantly poorer survival than those at low risk (P<0.0001). The prediction model exhibited a good discriminative ability and an optimal accuracy. Conclusions: In the form of a static nomogram or an online calculator, an effective and convenient nomogram was developed and validated to help clinicians quantify the risk of mortality, make personalized survival assessments, and create optimal treatment plans for UAC patients.