Nomogram for predicting 5-year disease-specific mortality after primary surgery for epithelial ovarian cancer

Objective

To develop a nomogram based on established prognostic factors to predict the probability of 5-year disease-specific mortality after primary surgery for patients with all stages of epithelial ovarian cancer (EOC) and compare the predictive accuracy with the currently used International Federation of Gynecology and Obstetrics (FIGO) staging system.

Methods

Using a prospectively kept database, we identified all patients with EOC who had their primary surgery at our institution between January 1996 and December 2004. Disease-specific mortality was estimated using the Kaplan-Meier method. Twenty-eight clinical and pathologic factors were analyzed. Significant factors on univariate analysis were included in the Cox proportional hazards regression model, which identified factors utilized in the nomogram. The concordance index (CI) was used as an accuracy measure, with bootstrapping to correct for optimistic bias. Calibration plots were constructed.

Results

A total of 478 patients with EOC were included. The most predictive nomogram was constructed using seven variables: age, FIGO stage, residual disease status, preoperative albumin level, histology, family history suggestive of hereditary breast/ovarian cancer (HBOC) syndrome, and American Society of Anesthesiologists (ASA) status. This nomogram was internally validated using bootstrapping and shown to have excellent calibration with a bootstrap-corrected CI of 0.714. The CI for FIGO staging alone was significantly less at 0.62 (P = 0.002).

Conclusion

We have developed an all-stage nomogram to predict 5-year disease-specific mortality after primary surgery for epithelial ovarian cancer. This tool is more accurate than FIGO staging and should be useful for patient counseling, clinical trial eligibility, postoperative management, and follow-up.