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Table 4 Performances for the automatic model

From: MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study

 

Construction set: centre A + centre B

 

AUC

(95% CI)

ACC %

(95% CI)

[rate]

SE %

(95% CI)

[rate]

SP %

(95% CI)

[rate]

NPV %

(95% CI)

[rate]

PPV %

(95% CI)

[rate]

AP+Bayes

0.75

(67–83)

72

(59–82)

[45/63]

74

(54–89)

[20/27]

69

(52–84)

[25/36]

77

(65–87)

[25/32]

65

(51–76)

[20/31]

MRMR+Bayes

0.78

(69–85)

74

(60–83)

[46/63]

81

(62–94)

[22/27]

67

(49–81)

[24/36]

83

(68–92)

[24/29]

65

(44–70)

[22/34]

Ranking+EL (Bag)

0.90

(87–95)

68

(55–79)

96

(81–99)

47

(30–65)

94

(71–99)

58

(50–65)

[43/63]

[26/27]

[17/36]

[17/18]

[26/45]

AP+SVM polynomial

0.70

(64–82)

67

(54–78)

81

(62–94)

56

(38–72)

80

(63–90)

58

(48–67)

 

[42/63]

[22/27]

[20/36]

[20/25]

[22/38]

Ranking+SVM linear

0.83

(78–90)

74

(62–85)

70

(50–86)

78

(66–87)

78

(66–87)

70

(55–82)

 

[47/63]

[19/27]

[28/36]

[28/36]

[19/27]

Ranking+SVM Gaussian

0.86

(78–94)

78

(66–87)

[49/63]

81

(62–94)

[22/27]

75

(58–88)

[27/36]

84

(71–92)

[27/32]

71

(57–82)

[22/31]

 

Validation set: centre C

Ranking+SVM Gaussian

0.81

(60–89)

75

(53–88)

[21/28]

80

(50–95)

[12/15]

69

(37–90)

[9/13]

75

(54–86)

[9/12]

75

(55–83)

[12/16]

  1. AUC Area under the curve, ACC Accuracy, NPV Negative predictive value, PPV Positive predictive value, SE Sensitivity, SP Specificity