Random survival forest model in patients with epithelial ovarian cancer: a study based on SEER database and single center data.

Abstract

Clinical data of 1,780 patients with epithelial ovarian carcinoma (EOC) in the Surveillance, Epidemiology and End Results (SEER) database were retrospectively analyzed. A random survival forest model and a nomogram model were built based on the prognostic factors. The clinical data of 140 patients with EOC treated in Liuzhou Worker's Hospital were collected for the validation of the prognostic model. Age (≥75 years), histology grade (poor differentiation or undifferentiation), histologic types (clear cell carcinoma or carcinosarcoma), T stage (T2 or T3), M stage (M1), surgical conditions, and chemotherapy situation (without chemotherapy) were identified as independent risk factors. Based on these factors, a random forest survival prediction model was established. In the training set, the area under the curve (AUC) for the random forest survival prediction model in predicting 1-, 3- and 5-year survival were 0.848, 0.859 and 0.890, respectively. In the test set, the AUCs for 1-, 3- and 5-year survival were 0.992, 0.795 and 0.883, respectively. A nomogram prediction model was also established. In the training set, the AUCs for the nomogram prediction model for 1-, 3- and 5-year survival were 0.789, 0.803 and 0.838, respectively. In the test set, the AUCs for 1-, 3- and 5-year survival were 0.926, 0.748 and 0.836, respectively. The results indicated that the random forest survival model established in this study holds significant clinical value. Physicians can develop personalized follow-up strategies or treatment regimens for patients based on the predicted survival risk, potentially improving long-term outcomes.