Novel prognostic nomograms in cervical cancer based on analysis of 1075 patients

Abstract

Objective: To explore the factors affecting the prognosis of cervical cancer (CC), and to construct and evaluate predictive nomograms to guide individualized clinical treatment. Methods: The clinicopathological and follow-up data of CC patients from June 2013 to December 2019 in Sun Yat-sen Memorial Hospital of Sun Yat-sen University were retrospectively analyzed. Log-rank test was used for univariate survival analysis, and Cox multivariate regression was used to identify independent prognostic factors, based on which nomogram models were established and evaluated in multiple aspects. Results: Patients were randomly assigned into the training (n = 746) and validation sets (n = 329). Survival analysis of the training set identified cervical myometrial invasion, parametrial involvement, and malignant tumor history as prognosticators of postoperative DFS and pathological type, cervical myometrial invasion, and history of STD for OS. C-index was 0.799 and 0.839 for the nomograms for DFS and OS, respectively. Calibration curves and Brier scores also indicated high performance. Importantly, decision curve analysis suggested great clinical applicability of these nomograms. Conclusions: In this study, we analyzed a cohort of 1075 CC patients and identified DFS- or OS-associated clinicohistologic characteristics. Two nomograms were subsequently constructed for DFS and OS prognostication, respectively, and showed high performance in terms of discrimination, calibration, and clinical applicability. These models may facilitate individualized treatment and patient selection for clinical trials. Future investigations with larger cohorts and prospective designs are warranted for validating these prognostic models.