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Table 3 Diagnostic performance of four models in the evaluation of mild or moderate renal function impairment

From: MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study

 

Training cohort

Validation cohort

Models

AUC

Sensitivity (%)

Specificity (%)

Accuracy (%)

AUC

Sensitivity (%)

Specificity (%)

Accuracy (%)

All-K

0.888 (0.881–0.895)

78.9 (78.2–79.2)

89.0 (88.4–89.6)

85.0 (84.4–85.6)

0.820 (0.817–0.823)

65.0 (63.8–66.2)

74.2 (73.9–74.5)

75.0 (74.5–75.5)

LC-K

0.919 (0.916–0.922)

83.0 (81.8–84.1)

94.5 (93.2–95.8)

88.3 (87.5–89.2)

0.852 (0.846–0.857)

65.0 (64.4–65.6)

84.4 (82.8–86.0)

76.9 (76.3–77.5)

TLCO-K

0.926 (0.919–0.932)

80.8 (80.3–81.4)

91.8 (89.9–93.6)

87.5 (86.9–88.1)

0.705 (0.691–0.720)

75.0 (73.6–76.3)

59.4 (58.7–60.0)

65.4 (64.8–66.0)

PIZZA-K

0.867 (0.860–0.874)

63.8 (63.3–64.3)

93.1 (93.1–93.2)

81.7 (80.9–82.4)

0.705 (0.690–0.721)

25.0 (24.4–25.5)

87.5 (87.0–87.9)

63.5 (62.9–64.0)

  1. Data in parentheses are 95% confidence interval