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Table 6 Predictive performance of combined model, radiomics model, and clinicoradiological model

From: Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Model

Combined model

Radiomics model

Clinicoradiological model

Training cohort

Validation cohort

Testing cohort

Training cohort

Validation cohort

Testing cohort

Training cohort

Validation cohort

Testing cohort

AUC

0.887 [0.798–0.952]

0.859 [0.748–0.935]

0.828 [0.731–0.929]

0.860 [0.759–0.963]

0.824 [0.736–0.915]

0.787 [0.710–0.892]

0.752 [0.649–0.870]

0.703 [0.592–0.844]

0.637 [0.511–0.769]

Accuracy (%)

85.24 [76.75–90.14]

82.76 [75.99–91.36]

81.62 [74.18–92.45]

83.61 [75.85–92.65]

77.14 [69.38–86.05]

73.42 [61.63–82.66]

69.33 [56.54–83.76]

62.87 [51.74–75.36]

56.35 [42.48–69.49]

Sensitivity (%)

89.77 [80.63–96.98]

84.94 [76.72–92.49]

85.56 [77.82–91.58]

84.78 [74.36–93.74]

78.72 [64.35–91.48]

82.15 [73.74–91.92]

71.86 [59.43–83.82]

73.89 [65.95–83.47]

65.78 [46.89–80.87]

Specificity (%)

84.47 [72.60–91.65]

83.42 [74.32–92.18]

78.01 [69.45–85.74]

82.67 [74.35–93.14]

80.33 [69.74–92.26]

76.48 [63.36–89.17]

70.42 [61.36–83.47]

66.86 [56.63–78.10]

64.23 [52.36–80.62]

PPV (%)

82.34 [73.87–91.59]

81.52 [73.67–87.59]

78.36 [70.16–86.47]

77.79 [69.16–85.97]

75.98 [68.30–83.57]

74.82 [65.87–83.64]

68.24 [56.25–81.39]

65.71 [52.63–78.78]

60.99 [48.34–73.76]

NPV (%)

87.65 [79.48–97.86]

86.77 [78.43–91.17]

85.60 [79.98–90.35]

83.90 [72.74–95.46]

80.43 [71.32–89.91]

82.62 [71.64–92.35]

74.22 [65.61–86.34]

70.98 [61.05–82.77]

73.17 [60.05–87.47]

  1. Data in parentheses are 95% CIs