ArticleInternational journal of women's health2025
Development and Internal-External Validation Models for Cervical Cancer Overall Survival Prognosis: A SEER-Based Study and Chinese Data.
Article in International journal of women's health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: The study aims to develop a model to predict overall survival (OS) in cervical cancer (CC). Methods: A total of 13,592 CC patient records were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2020. These patients were randomized with a 7:3 ratio into a training cohort (TC, n = 9,514) and an internal validation cohort (IVC, n = 4,078). Univariate and multivariate Cox regression were used to construct a prognostic model based on the training set and develop a nomogram to predict the 3-year, 5-year, and 10-year OS of CC patients. Additionally, medical data from 318 CC patients at Yangming Hospital Affiliated to Ningbo University, collected between 2008 and 2020, were analyzed for external validation. Results: Univariate and multivariate Cox regression identified six predictors of prognosis in CC including age, tumor grade, tumor stage, tumor size, lymph node metastasis (LNM), and lymph vascular space invasion (LVSI) to construct the nomogram. The C-index was 0.882 (95% CI: 0.874 to 0.890), and the areas under curves (AUC) for 3-year, 5-year, and 10-year overall survival (OS) were 0.913, 0.912, and 0.906, respectively for the training cohort. The C-index was 0.885 (95% CI: 0.873 to 0.897), and the AUC for the 3-year, 5-year, and 10-year OS were 0.916, 0.910, and 0.910 for the internal validation cohort. For the external validation cohort, the C-index was 0.872 (95% CI: 0.829-0.915), with AUCs of 0.892, 0.896, and 0.903 for 3-year, 5-year, and 10-year OS, respectively. Conclusion: The devised nomogram can be applied in clinical settings to estimate the OS probability of CC patients. This tool provides personalized predictions for the OS of CC patients, thereby assisting healthcare professionals in optimizing their clinical practices.
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