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KMID : 1137020150260010046
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Journal of Gynecologic Oncology
2015 Volume.26 No. 1 p.46 ~ p.53
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Distinguishing benign from malignant pelvic mass utilizing an algorithm with HE4, menopausal status, and ultrasound findings
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Wilailak Sarikapan
Chan Karen KL
Chen Chi-An
Nam Joo-Hyun
Ochiai Kazunori
Aw Tar-Choon
Sabaratnam Subathra
Hebbar Sudarshan
Sickan Jaganathan
Schodin Beth A
Charakorn Chuenkamon
Sumpaico Walfrido W
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Abstract
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Objective: The purpose of this study was to develop a risk prediction score for distinguishing benign ovarian mass from malignant tumors using CA-125, human epididymis protein 4 (HE4), ultrasound findings, and menopausal status. The risk prediction score was compared to the risk of malignancy index and risk of ovarian malignancy algorithm (ROMA).
Methods: This was a prospective, multicenter (n=6) study with patients from six Asian countries. Patients had a pelvic mass upon imaging and were scheduled to undergo surgery. Serum CA-125 and HE4 were measured on preoperative samples, and ultrasound findings were recorded. Regression analysis was performed and a risk prediction model was developed based on the significant factors. A bootstrap technique was applied to assess the validity of the HE4 model.
Results: A total of 414 women with a pelvic mass were enrolled in the study, of which 328 had documented ultrasound findings. The risk prediction model that contained HE4, menopausal status, and ultrasound findings exhibited the best performance compared to models with CA-125 alone, or a combination of CA-125 and HE4. This model classified 77.2% of women with ovarian cancer as medium or high risk, and 86% of women with benign disease as very-low, low, or medium-low risk. This model exhibited better sensitivity than ROMA, but ROMA exhibited better specificity. Both models performed better than CA-125 alone.
Conclusion: Combining ultrasound with HE4 can improve the sensitivity for detecting ovarian cancer compared to other algorithms.
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KEYWORD
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Algorithms, CA-125 Antigen, Ovarian Neoplasms, Prospective Studies, Regression Analysis, Sensitivity and Specificity
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