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Table 2 Baseline characteristics of the included studies

From: A meta-analysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients

Author (year of publication)

Algorithm

Sequences

Segmentation

Dataset

Sensitivity

Specificity

Hongna Tan (2020)

SVM

T2-FS

3D

Training set

84.38%

72.25%

    

Validation set

65.22%

81.08%

Thomas Ren (2020)

CNN

T1CE

2D

Validation set

92.10 ± 2.90%

79.30 ± 5.10%

Xiao Zhang (2019)

RF

DWI

3D

Validation set

83.30%

74.20%

  

T2-FS

  

77.80%

87.10%

Jia Liu (2019)

SVM

T1CE

3D

Training set

75.00%

76.00%

    

Validation set

71.00%

100.00%

 

Xgboost

  

Training set

89.00%

76.00%

    

Validation set

86.00%

83.00%

 

LR

  

Training set

71.00%

71.00%

    

Validation set

71.00%

83.00%

Karl D Spuhler (2019)

CNN

T1CE

3D

Testing set

72.20%

88.90%

Lu Han (2019)

SVM

T1CE

2D

Training set

89.00%

57.00%

    

Validation set

78.00%

72.00%

Jiaxiu Luo (2018)

SVM

DWI

3D

Testing set

86.70%

83.30%

Chunling Liu (2019)

LR

T1CE

3D

Training set

76.10%

66.70%

    

Validation set

81.90%

77.80%

Yuhao Dong (2018)

LR

T2-FS

3D

Training set

66.30 ± 0.30%

81.60 ± 0.20%

    

Validation set

60.00 ± 0.60%

74.70 ± 0.40%

  

DWI

 

Training set

74.00 ± 0.20%

80.80 ± 0.20%

    

Validation set

69.50 ± 0.50%

75.70 ± 0.40%

  

T2-FS and DWI

 

Training set

66.30 ± 0.30%

81.60 ± 0.20%

    

Validation set

70.00 ± 0.80%

74.70 ± 0.50%

Meijie Liu (2020)

LR

T1CE

2D

Validation set

64.00%

79.00%

Xiaoyu Cui (2019)

SVM

T1CE

3D

Validation set

94.90%

77.96%

 

KNN

   

89.39%

87.18%

 

LDA

   

80.31%

67.78%

Demircioglu (2020)

LR

T1CE and T2-FS

3D

Validation set

71.00%

74.00%

Arefan (2020)

LDA

T1CE

2D

Testing set

60.00%

87.00%

 

RF

   

60.00%

86.00%

 

NB

   

81.00%

67.00%

 

KNN

   

74.00%

54.00%

 

SVM

   

70.00%

71.00%

 

LDA

 

3D

 

63.00%

92.00%

 

RF

   

64.00%

90.00%

 

NB

   

85.00%

62.00%

 

KNN

   

67.00%

58.00%

 

SVM

   

81.00%

50.00%

 

LDA

 

2D

Validation set

82.00%

76.00%

 

RF

   

89.00%

68.00%

 

NB

   

73.00%

82.00%

 

KNN

   

79.00%

75.00%

 

SVM

   

65.00%

88.00%

 

LDA

 

3D

 

82.00%

78.00%

 

RF

   

89.00%

70.00%

 

NB

   

69.00%

76.00%

 

KNN

   

75.00%

75.00%

 

SVM

   

73.00%

79.00%

Fusco (2018)

LDA

T1CE

3D

Validation set

88.50%

77.80%

  1. CNN, convolutional neural networks; SVM, support vector machine; LR, linear regression; KNN, k-nearest neighbor; LDA, linear discriminant analysis; NB, naive Bayes; RF, random forest; T2-FS, fat-suppressed T2; T1CE, contrast-enhanced T1. The algorithm pooled was chosen with the highest performance