Development And Validation Of A Simple-To-Use Nomogram To Predict 3-Year Recurrence Rate Of Ovarian Cancer

Objective: To establish a reliable nomogram model to predict the recurrence rate of ovarian cancer after surgery. Methods: We retrospectively reviewed 216 patients diagnosed with ovarian cancer in our hospital, of which 164 cases were considered valid. Chi-square test and binary logistic regression model were used to analyze the possible predictors. After that, a nomogram model based on those significantly related predictors was established. We used the bootstrap to internally validate the predictive ability of the nomogram model and used the decision curve analysis (DCA) to compare the performance of the FIGO stage with this model. Results: The nomogram included four significant recurrence predictors: FIGO stage (advanced ovarian cancer), omentum involvement, lymphovascular space invasion (LVSI), CA125. The accuracy of predicting the recurrence was 81.7%. The Maximum Deviation (Emax) and the Average Deviation (Eavg) of bootstrap were 0.035 and 0.007, and the area under the curve (AUC) was 0.863, which demonstrated this model had a good predictive ability. Compared with the FIGO stage, this hybrid model is more superior in predicting recurrence risk in ovarian cancer patients Conclusions: We developed and validated a non-invasion and user-friendly nomogram model to predict the recurrence risk of patients with ovarian cancer after surgery. Keywords : Nomogram; recurrence; ovarian cancer.