Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images
Table 4
Mean ± standard deviations of the evaluation metrics with different features in the training and validation sets. The results of deep features from each column are shown in bold. The values of paired t-tests among different features in the validation set are listed in the lower half of the table. Calculations of the sensitivity and specificity of handcrafted and deep feature sets are provided in the Supplementary Information.
Training set
Validation set
Type
AUC
Se
Sp
Acc
AUC
Se
Sp
Acc
FLAIR
0.9429 ± 0.0037
0.7936 ± 0.0129
0.8738 ± 0.0044
0.8598 ± 0.0036
0.9271 ± 0.0047
0.7826 ± 0.0157
0.8421 ± 0.0062
0.8304 ± 0.0052
T1C
0.8980 ± 0.0053
0.6912 ± 0.0117
0.8455 ± 0.0053
0.8094 ± 0.0043
0.8771 ± 0.0065
0.7153 ± 0.0157
0.8032 ± 0.0072
0.7854 ± 0.006
T1
0.9783 ± 0.0017
0.8687 ± 0.0103
0.9284 ± 0.0034
0.9179 ± 0.0029
0.9696 ± 0.0024
0.8529 ± 0.0108
0.9077 ± 0.005
0.8960 ± 0.0043
T2
0.9182 ± 0.0038
0.8109 ± 0.0115
0.8290 ± 0.0044
0.8264 ± 0.0037
0.8994 ± 0.0049
0.8019 ± 0.0144
0.7905 ± 0.0061
0.7909 ± 0.0051
Multimodality
0.9722 ± 0.0029
0.8849 ± 0.0109
0.9190 ± 0.0035
0.9172 ± 0.0033
0.9624 ± 0.0038
0.8497 ± 0.0133
0.9083 ± 0.0052
.8960 ± 0.0047
AlexNet
0.9995 ± 0.0002
0.9996 ± 0.0004
0.9870 ± 0.0015
0.9892 ± 0.0012
0.9993 ± 0.0003
0.9994 ± 0.0006
0.9801 ± 0.0022
0.9833 ± 0.0018
Inception v3
0.9941 ± 0.0012
0.9913 ± 0.0034
0.9615 ± 0.0039
0.9669 ± 0.0033
0.9914 ± 0.0017
0.9884 ± 0.0042
0.9436 ± 0.0054
0.9509 ± 0.0047
Fusion AlexNet
0.9988 ± 0.0005
0.9957 ± 0.0021
0.9838 ± 0.002
0.9860 ± 0.0017
0.9982 ± 0.0007
0.9941 ± 0.0028
0.9755 ± 0.0029
0.9786 ± 0.0025
Fusion Inception v3
0.9992 ± 0.0004
0.9933 ± 0.0025
0.9863 ± 0.0019
0.9874 ± 0.0017
0.9988 ± 0.0006
0.9907 ± 0.0034
0.9793 ± 0.0028
0.9809 ± 0.0025
Single-modality handcrafted features compared to multimodality handcrafted features ( values)
T1
—
—
—
—
5.35 × 10−39
1.71 × 10−27
1.56 × 10−28
6.93 × 10−59
T2
—
—
—
—
3.57 × 10−16
0.1832
6.0 × 10−26
1.22 × 10−22
T1C
—
—
—
—
3.10 × 10−23
0.02
9.71 × 10−17
1.68 × 10−22
FLAIR
—
—
—
—
0.03
0.4863
0.02
0.0099
Deep features compared to multimodality handcrafted features ( values)
AlexNet
—
—
—
—
3.88 × 10−138
6.40 × 10−117
8.37 × 10−208
7.99 × 10−301
Inception v3
—
—
—
—
3.60 × 10−99
1.72 × 10−103
3.97 × 10−98
1.32 × 10−162
Fusion AlexNet
—
—
—
—
1.56 × 10−134
5.76 × 10−109
1.47 × 10−193
5.44 × 10−277
Fusion Inception v3
—
—
—
—
2.81 × 10−135
1.02 × 10−102
2.94 × 10−190
1.49 × 10−269
AlexNet features compared to Inception v3 and fusion AlexNet features, respectively ( values)
Inception v3
—
—
—
—
1.35 × 10−18
8.17 × 10−8
8.21 × 10−31
2.41 × 10−33
Fusion AlexNet
—
—
—
—
0.01
1.11 × 10−4
0.08
0.02
Fusion Inception v3 features compared to Inception v3 and fusion AlexNet features ( values)
Inception v3
—
—
—
—
2.18 × 10−14
0.88
1.81 × 10−25
2.09 × 10−23
Fusion AlexNet
—
—
—
—
0.8913
0.03
0.42
0.9663
Se: sensitivity; Sp: specificity; Acc: accuracy. “—” in the table indicates the item was not calculated to correspond to paired t-test value in the training set.
