Objectives We aimed to develop a risk model to predict a risk of suboptimal cytoreduction in primary surgery of ovarian cancer. Methods The 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. Results Among 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. Conclusions We 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.
Bibliographical noteFunding Information:
This study was supported by a grant from National Cancer Center, Republic of Korea (1210200) and the Korea Healthcare Technology R&D Project, Ministry for Health & Welfare Affairs, Republic of Korea (A092255).
- Computed tomography
- Cytoreductive surgery
- Optimal cytoreduction
- Ovarian cancer
- Spread pattern
- Surgical skill index