Nomogram for predicting the early death of patients with stage IV ovarian cancer: a retrospective analysis of the SEER database.

Abstract

Background: Ovarian cancer is a major health problem for women all over the world and tends to progress to advanced stages. Therefore, it is important to predict the early survival of patients with advanced ovarian cancer. The purpose of this study is to assist clinicians in predicting the short-term prognosis of patients with stage IV ovarian cancer in order to make optimal medical decisions.

Methods: A retrospective analysis was conducted on data from the Surveillance, Epidemiology, and End Results database, involving 3,077 patients with stage IV ovarian cancer. Univariate and multivariate logistic regression analyses were performed to identify risk factors. Using R software, relevant predictive models were constructed. The calibration, discrimination, and clinical utility of these models were assessed in a validation cohort.

Results: A nomogram model was developed utilizing four independent risk factors to predict the probability of early death in patients with stage IV ovarian cancer. The model exhibited satisfactory discrimination in both the training cohort (area under the receiver operating characteristic curve =0.816) and the validation cohort (area under the receiver operating characteristic curve =0.827). The calibration curve demonstrated a high level of predictive accuracy for the model. Furthermore, the decision curve analysis indicated that the nomogram holds clinical utility and offers a net benefit to patients within certain limitations. The predictive effectiveness of the nomogram was verified by the Kaplan-Meier survival curve.

Conclusions: We have successfully developed a nomogram and risk classification system to accurately predict the probability of early death in patients with stage IV ovarian cancer.