Construction of a prediction model of cancer-specific survival after ovarian clear cell carcinoma surgery

Abstract

Purpose To construct a nomogram model for predicting postoperative cancer-specific survival (CSS) of patients with ovarian clear cell carcinoma (OCCC) and analyze in detail the risk factors associated with OCCC. Methods The clinical pathological data of 596 OCCC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Of these patients, 420 were allocated to the training group and 176 patients to the validation group using bootstrap resampling. The nomogram was developed based on the Cox regression model for predicting the cancer-specific survival probability of patients at 3 and 5 years after the operation. The model was evaluated in both the training and validation groups using consistency index, Receiver Operating Characteristic (ROC), and calibration plots. Results The independent risk factors for CSS in OCCC patients included FIGO stage, race, age, tumor laterality, and the log odds of positive lymph nodes (LODDS). The nomograms were established for predicting the 3- and 5-year CSS of patients after operation. The c-index of the nomogram for CSS was 0.786 in the training group and 0.742 in the verification group. AUCs of the 3-year and 5-year ROC curves were 0.818, 0.824 in the training group; and 0.816, 0.808 in the verification group, respectively. Conclusion Based on the real population data, the construction of the CSS prediction model after OCCC surgery has high prediction efficiency, can identify postoperative high-risk OCCC patients, and can be a valuable aid for the tumor staging system.