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Title: Development and validation of a predictive score for preoperative diagnosis of early stage epithelial ovarian cancer
Authors: Watcharin Chirdchim
Preecha Wanichsetakul
Phichayut Phinyo
Jayanton Patumanond
Komsun Suwannarurk
Jatupol Srisomboon
Keywords: Biochemistry, Genetics and Molecular Biology
Issue Date: 1-Jan-2019
Abstract: © 2019 Asian Pacific Organization for Cancer Prevention. Objective: To develop and validate a simplified multi-parameter risk-based scoring system for preoperative diagnosis of early stage epithelial ovarian cancer. Methods: All women presented with adnexal mass and were scheduled for operation at Phrapokklao hospital during September 2013 - December 2017 were included and categorized according to their histopathologic reports into early stage ovarian cancer groups and benign ovarian tumor groups. Multivariable logistic regression was used to explore for potential predictors. The selected logistic coefficients were transformed into risk-based scoring system. Internal validation was done with bootstrapping procedure. Results: A total of 270 participants were included in analysis and predictive model development, 54 in early stage ovarian cancer group and 216 in benign ovarian tumor group. Menopausal status, two abnormal ultrasound findings (presence of solid component or ascites), tumor size and serum CA-125 level were used for derivation of the scoring system. The score-based model showed area under ROC of 0.88 (95%CI 0.82-0.93). The developed scoring system ranged from 0 to 51 was classified into 3 subcategories for clinical practicability. The positive predictive values for the presence of early stage ovarian cancer were 2.07 (95%CI 0.43-6.05) for low risk patient, 29.13(95%CI 19.65-41.58) for moderate risk patient, and 95.45(95%CI 77.16-99.88) for high risk patient. Conclusion: This simplified risk-based scoring system for preoperative diagnosis of early stage ovarian cancer could aid general physicians or general gynecologists in evaluation of patients presenting with ovarian tumors and help gynecologic oncologists in management planning and prioritization of patients for operation.
ISSN: 2476762X
Appears in Collections:CMUL: Journal Articles

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