Nomogram for predicting 5-year survival after primary surgery for epithelial ovarian cancer

Abstract

5523 Background: Nomograms have been shown to be superior to traditional staging systems for predicting an individual's probability of long-term survival. Our objective was to develop a nomogram based on established prognostic factors to predict the probability of 5-year disease-specific survival (DSS) after primary surgery for patients with epithelial ovarian cancer (EOC) and to compare its predictive accuracy with the currently used FIGO staging system. Methods: We identified all pts with EOC who had their primary staging/cytoreductive surgery at our institution from January 1996-December 2004. DSS was estimated using the Kaplan-Meier method. We analyzed 28 clinical and pathologic factors for prognostic significance. Significant factors on univariate analysis were then included in the Cox proportional hazards regression model, which identified the factors to be used to construct the nomogram. The concordance index (CI) was used as an accuracy measure, with bootstrapping to correct for optimistic bias. Calibration plots were constructed. Results: There were 478 evaluable pts on the study. The median age was 58 years (range 25–96). The primary surgeon in all cases was an attending gynecologic oncologist. All patients received platinum-based systemic chemotherapy postop. DSS at 5 years was 52%. The most predictive nomogram was constructed using the following 7 predictor variables: age, ASA status, family history suggestive of hereditary breast/ovarian cancer syndrome, preoperative serum albumin level, FIGO stage, tumor histology, and residual disease status after primary surgery. This nomogram was internally validated using bootstrapping and shown to have excellent calibration with a bootstrap-corrected CI of 0.721. The CI for FIGO staging alone was significantly less at 0.62 (p = 0.002). Conclusions: We developed a nomogram to predict 5-year DSS after primary surgery for EOC. The nomogram uses 7 variables that are readily accessible, assigns a point value to each variable, and then predicts the probability of 5-year survival based on the total point value for an individual patient. This tool is more accurate than FIGO staging and should be useful for patient counseling, clinical trial eligibility determination, postop management, and follow-up. No significant financial relationships to disclose.