|
Model
|
Accuracy
|
Sensitivity
|
Specificity
|
PPV
|
NPV
|
|---|
|
Training set
| | | | | |
|
CT 3D-CNN
|
0.84 ± 0.03
|
0.90 ± 0.07
|
0.62 ± 0.16
|
0.90 ± 0.04
|
0.63 ± 0.15
|
|
PET 3D-CNN
|
0.92 ± 0.02
|
0.97 ± 0.03
|
0.76 ± 0.15
|
0.94 ± 0.04
|
0.88 ± 0.09
|
|
PET/CT 3D-CNN
|
0.93 ± 0.01
|
0.98 ± 0.01
|
0.76 ± 0.06
|
0.94 ± 0.02
|
0.90 ± 0.09
|
|
Testing set
| | | | | |
|
CT 3D-CNN
|
0.67
|
0.70
|
0.50
|
0.86
|
0.27
|
|
PET 3D-CNN
|
0.76
|
0.85
|
0.33
|
0.85
|
0.33
|
|
PET/CT 3D-CNN
|
0.85
|
0.96
|
0.33
|
0.87
|
0.67
|
- The results in the table are expressed as mean ± SD
- 3D-CNN, three-dimensional convolutional neural network; PPV, positive predictive value; NPV, negative predictive value
