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Use of complex surgical procedures, patterns of tumor spread, and CA-125 predicts a risk of incomplete cytoreduction: A Korean Gynecologic Oncology Group study (KGOG-3022)

 Dae Chul Jung  ;  Sokbom Kang  ;  Seung-Cheol Kim  ;  Jae Weon Kim  ;  Joo-Hyun Nam  ;  Sang-Young Ryu  ;  Seok Ju Seong  ;  Byoung-Gie Kim 
 Gynecologic Oncology, Vol.131(2) : 336-340, 2013 
Journal Title
 Gynecologic Oncology 
Issue Date
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.
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1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
정대철(Jung, Dae Chul) ORCID logo https://orcid.org/0000-0001-5769-5083
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