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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)

Title
 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) 
Authors
 Dae Chul Jung ; Sokbom Kang ; Byoung-Gie Kim ; Seok Ju Seong ; Sang-Young Ryu ; Joo-Hyun Nam ; Jae Weon Kim ; Seung-Cheol Kim 
Issue Date
2013
Journal Title
 Gynecologic Oncology 
ISSN
 0090-8258 
Citation
 Gynecologic Oncology, Vol.131(2) : 336~340, 2013 
Abstract
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.
URI
http://ir.ymlib.yonsei.ac.kr/handle/22282913/88300
DOI
10.1016/j.ygyno.2013.07.110
Appears in Collections:
1. 연구논문 > 1. College of Medicine > Dept. of Radiology
Yonsei Authors
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Link
 http://www.sciencedirect.com/science/article/pii/S0090825813010755
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