Objectives: The role of retrospective analysis has evolved greatly in cancer research. We undertook this network meta-analysis to evaluate retrospectively the diagnostic value of ROMA in ovarian cancer. 
Materials and methods: We systematically retrieved 56 relevant articles published about ROMA index from 2009–2018 and about ovarian cancer from China National Knowledge Infrastructure (CNKI), PubMed and EMBASE. Data were comprehensively analyzed by RevMan 5.3 and MetaDisc 12.4 software.
Results: Data of 5,954 cases were retrieved from 23 literatures. Among them, 2,117 cases were in the ovarian cancer group and 3,837 cases in the control group. The pooled estimates for the ROMA index were sensitivity: 0.90 (95% CI: 0.88–0.93), specificity: 0.91 (95% CI: 0.89–0.94), positive predictive: 0.90 (95% CI: 0.88–0.95), negative predictive: 0.93 (95% CI: 0.91–0.95), and area under ROC curve: 0.96, compared to 0.71 (95% CI: 0.56–0.82), 0.87 (95% CI: 0.80–0.92), 0.82 (95% CI: 0.78–0.86), 0.92 (95% CI: 0.90–0.94), and 0.88 of HE4, respectively.
Conclusions: This meta-analysis confirms that the risk of ovarian malignancy algorithm can facilitate the diagnosis of ovarian cancer to some extent.
Keywords: ROMA index, ovarian cancers, meta-analysis