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Table 3 Predictive performance of the clinical model, SVM-based radiomics model and fusion model in the training, internal validation and external validation cohorts

From: Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study

Cohort

Model

AUC (95% CI)

SEN (%)

SPE (%)

ACC (%)

Training

Clinical

0.737 (0.669—0.797)

71.74

64.42

67.86

 

SVM-based radiomics

0.884 (0.831—0.925)

86.96

75.00

80.61

 

Fusion

0.916 (0.868—0.951)

91.30

79.81

85.20

Internal validation

Clinical

0.715 (0.606—0.808)

90.00

50.00

69.05

 

SVM-based radiomics

0.860 (0.767—0.926)

85.00

81.82

83.33

 

Fusion

0.903 (0.819—0.957)

80.00

90.91

85.71

External validation

Clinical

0.654 (0.532—0.763)

54.55

76.32

66.20

 

SVM-based radiomics

0.841 (0.734—0.917)

81.82

84.20

83.10

 

Fusion

0.885 (0.787—0.949)

84.85

84.21

84.51

  1. AUC Area under the curve, ACC Accuracy, CI Confidence interval, SEN Sensitivity, SPE Specificity, SVM Support vector machine