A model for predicting surgical outcome in patients with advanced ovarian carcinoma using computed tomography

A reliable model for predicting the outcome of primary cytoreductive surgery may be a useful tool in the clinical management of patients with advanced epithelial ovarian carcinoma.

Forty-one women with a preoperative computed tomographic (CT) scan of the abdomen and pelvis and a histologic diagnosis of Stage III or IV epithelial ovarian carcinoma following primary surgery performed by one of nine gynecologic oncologists were identified from tumor registry databases. All CT scans were analyzed retrospectively using a panel of 25 radiographic features without knowledge of the operative findings. Patient demographics, surgical findings and outcome, Gynecologic Oncology Group performance status, and pre-operative serum CA125 values were collected from patient medical records. Residual disease measuring < or = 1 cm in maximal diameter was considered an optimal surgical result. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each radiographic feature and clinical characteristic. Based on statistical probability of each factor predicting cytoreductive outcome, 13 radiographic features, in addition to performance status, were selected for inclusion in the final model. Each parameter was assigned a numeric value based on the strength of statistical association, and a total Predictive Index score was tabulated for each patient. Receiver operating characteristic (ROC) curve analysis was used to assess the ability of the model to predict surgical outcome. Statistical significance was evaluated using the Fisher exact test.

Twenty of 41 patients (48.8%) underwent optimal cytoreduction to /= 2 cm), bowel mesentery involvement (>/= 2 cm), suprarenal paraaortic lymph nodes (>/= 1 cm), omental extension (spleen, stomach, or lesser sac), and pelvic sidewall involvement and/or hydroureter were most strongly associated with surgical outcome. Using the Predictive Index scores, a receiver operating characteristic curve was generated with an area under the curve = 0. 969 +/- 0.023. In the final model, a Predictive Index score >/= 4 had the highest overall accuracy at 92.7% and identified patients undergoing suboptimal surgery with a sensitivity of 100% (21/21). The specificity, or ability to identify patients undergoing optimal surgery, was 85.0% (17/20). The PPV of a Predictive Index score >/= 4 was 87.5% (21/24), and the NPV was 100%. The ability of this model to correctly predict surgical outcome was statistically significant (P < 0.001).

In this model, a Predictive Index score >/= 4 demonstrated high sensitivity, specificity, PPV, and NPV, and was highly accurate in identifying patients with advanced epithelial ovarian carcinoma unlikely to undergo optimal primary cytoreductive surgery. The Predictive Index model may have clinical utility in guiding the management of patients with ovarian carcinoma.