Abstract

ObjectivesWe aimed to develop a risk model to predict a risk of suboptimal cytoreduction in primary surgery of ovarian cancer. MethodsThe clinical records and computed tomography (CT) data of 358 patients with stages II–IV epithelial ovarian cancer were reviewed. Tumor spread patterns identified by principal component analysis, CA-125, and a newly developed surgical skill index were integrated into a logistic model along with other variables. Internal validation was performed using bootstrapped re-sampling and calibration was assessed by goodness-of-fit test. ResultsAmong the 358 patients, optimal cytoreduction, which was defined as no residual tumor, was achieved in 145 patients (40.5%). The surgical capacity of an individual institution was estimated by a surgical skill index, which was the frequency of complex surgeries in patients with advanced disease. In a multivariate model, two distinctive CT patterns of tumor spread (diffuse spread pattern and upper abdominal extension pattern), a surgical skill index, and serum CA-125 independently predicted a risk of suboptimal cytoreduction (P=0.006, P=0.013, P=0.031, and P=0.001, respectively). The model showed a C-statistic of .73 (95% confidence interval .67 to .79), which was significantly higher than tumor stage or ascites. Rigorous internal validation by bootstrapped re-sampling successfully confirmed the model. ConclusionsWe identified two distinct tumor spread patterns of ovarian cancer, which can be integrated to improve a prediction model. Our model may be useful in patient referral or clinical trials for patient stratification.

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