|
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%
|
- 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
