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Table 2 Diagnostic performance of RMI and ROMA algorithms in the subset of studies cited in this article

From: Biomarkers and algorithms for diagnosis of ovarian cancer: CA125, HE4, RMI and ROMA, a review

  Systematic review or meta-analysis RMI ROMA
Se (%) (95% IC) Sp (%) (95% IC) PPV (%) NPV (%) AUC (95% IC) Se (%) (95% IC) Sp (%) (95% IC) PPV (%) NPV (%) AUC (95% IC)
Ferraro et al. [14] X           
Dikmen et al. [15]        88     0.96
Chen et al. [19]        97 80    0.97 (0.95–1)
Yanaranop et al. [31]   78 80 60 90 0.88 (0.83–0.93) 84 69 52 91 0.86 (0.81–0.91)
Wilailak et al. [32]       0.84 (0.77–0.91)      0.86 (0.81–0.91)
Wang et al. [36] X       85 (81–89) 82 (77–87)    0.91 (0.88–0.93)
Zhen et al. [37] X           
Abdel-Azeez et al. [45]            
Holcomb et al. [46]            
Moore et al. [48]            
Goff et al. [52]            
Meys et al. [55] X 75 (72–79) 92 (88–94)         
Van Gorp et al. [56]            
Al Musalhi et al. [57]   77 82 56 93 0.85 75 88 65 92 0.84
Moore et al. [62]            
Li et al. [64] X       89 (84–93) 83 (77–88)    0.93 (0.90–0.95)
Wei et al. [66]        94 93 90 86  
Sandri et al. [67]        89 81    0.93 (0.90–0.96)
  1. RMI risk of malignancy index, ROMA risk of ovarian malignancy algorithm, Se sensitivity, Sp specificity, PPV positive predictive value, NPV negative predictive value, AUC area under the curve