The Risk of Ovarian Malignancy Algorithm (ROMA) as a Predictor of Ovarian Tumor Malignancy

Abstract

Object: To assess the diagnostic value of Risk of Ovarian Malignancy Algorithm (ROMA) in predicting ovarian malignancy.
 Methods: Diagnostic test was performed at dr. Mohammad Hoesin Hospital Palembang during June 2016 to November 2016. Data were analized with SPSS version 21.0 and Med-calc statistic.
 Results: A total of 57 subjects were recruited in this study. Subjects were divided into two groups: the premenopausal and postmenopausal group. Analysis with ROC curve was performed, the ROMA optimal cut-off of ROMA was 23.7% and 48.15% in the premenopausal and the post-menopausal group, respectively. With the optimal cut-off, the sensitivity was 79.41% and specivicity was 75%, positive predictive value wa 73.07% and negative predictive value 83.77% with accuracy 76.92% in diagnosing ovarian malignancy. Compared to RMI-3, the sensitivity was 65.5% and specivicity was 85.7% with accuracy 75.44%.
 Conclusion: ROMA is not a reliable diagnostic tools of ovarian malignancy.
 Keywords: CA125, HE4, ovarian cancer, risk of ovarian malignancyalgorithm/ ROMA, risk of ovarian malignancy index/RMI