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Table 1 Detailed performance of univariate analysis for imaging feature with each modality and fusion method

From: PET/MR fusion texture analysis for the clinical outcome prediction in soft-tissue sarcoma

Class

Feature number

AUC value

No fusion-based features

 T1-weighted MR images

52

0.7196 ± 0.0340

 T2-weighted MR images

71

0.6985 ± 0.0228

 PET images

79

0.7254 ± 0.0366

Image-level fusion based features

 T1/PET Image Fusion (0.1)

85

0.7459 ± 0.0414

 T1/PET Image Fusion (0.2)

87

0.7462 ± 0.0504

 T1/PET Image Fusion (0.3)

78

0.7533 ± 0.0463

 T1/PET Image Fusion (0.4)

83

0.7523 ± 0.0420

 T1/PET Image Fusion (0.5)

77

0.7567 ± 0.0386

 T1/PET Image Fusion (0.6)

76

0.7619 ± 0.0448

 T1/PET Image Fusion (0.7)

75

0.7631 ± 0.0432

 T1/PET Image Fusion (0.8)

70

0.7407 ± 0.0338

 T1/PET Image Fusion (0.9)

73

0.7306 ± 0.0365

 T2/PET Image Fusion (0.1)

90

0.7411 ± 0.0442

 T2/PET Image Fusion (0.2)

83

0.7519 ± 0.0438

 T2/PET Image Fusion (0.3)

83

0.7503 ± 0.0420

 T2/PET Image Fusion (0.4)

75

0.7392 ± 0.0328

 T2/PET Image Fusion (0.5)

70

0.7215 ± 0.0283

 T2/PET Image Fusion (0.6)

81

0.7084 ± 0.0244

 T2/PET Image Fusion (0.7)

72

0.7041 ± 0.0226

 T2/PET Image Fusion (0.8)

66

0.6957 ± 0.0195

 T2/PET Image Fusion (0.9)

69

0.6885 ± 0.0167

Matrix-level fusion based features

 T1/PET Matrix Fusion

90

0.7216 ± 0.0355

 T2/PET Matrix Fusion

95

0.7441 ± 0.0464

Feature-level fusion based features

 T1/PET Feature Concatenation

131

0.7231 ± 0.0359

 T2/PET Feature Concatenation

150

0.7126 ± 0.0335

 T1/PET Feature Average

85

0.7249 ± 0.0318

 T2/PET Feature Average

95

0.7366 ± 0.0416

  1. The number in the parentheses indicated the MR weight