Table 3.
Performance of the deep neural network (DNN) models on individual magnetic resonance imaging (MRI) sequences: T1-weighted MRI (T1), T2-weighted MRI (T2), and gadolinium-contrast-enhanced T1-weighted MRI (T1c).
| DNN model and magnetic resonance sequence | Sensitivity (95% CI) | Specificity (95% CI) | Accuracy (95% CI) | Area under the curve (95% CI) | |
| VGGa16 |
|
|
|
|
|
|
|
T1 | 0.725 (0.696-0.753) | 0.606 (0.562-0.648) | 0.684 (0.660-0.708) | 0.718 (0.689-0.747) |
|
|
T2 | 0.690 (0.660-0.719) | 0.686 (0.644-0.727) | 0.689 (0.665-0.713) | 0.767 (0.740-0.794) |
|
|
T1c | 0.874 (0.851-0.894) | 0.540 (0.496-0.585) | 0.759 (0.736-0.781) | 0.770 (0.743-0.797) |
| VGG19 |
|
|
|
|
|
|
|
T1 | 0.804 (0.778-0.829) | 0.448 (0.404-0.492) | 0.681 (0.657-0.705) | 0.692 (0.663-0.721) |
|
|
T2 | 0.743 (0.714-0.770) | 0.554 (0.510-0.598) | 0.678 (0.653-0.702) | 0.741 (0.713-0.769) |
|
|
T1c | 0.800 (0.773-0.825) | 0.653 (0.610-0.694) | 0.749 (0.726-0.771) | 0.795 (0.769-0.821) |
| ResNetb-50 |
|
|
|
|
|
|
|
T1 | 0.782 (0.755-0.808) | 0.584 (0.540-0.627) | 0.714 (0.690-0.737) | 0.732 (0.704-0.760) |
|
|
T2 | 0.833 (0.808-0.852) | 0.525 (0.480-0.569) | 0.727 (0.703-0.750) | 0.762 (0.735-0.789) |
|
|
T1c | 0.825 (0.799-0.848) | 0.653 (0.610-0.694) | 0.766 (0.743-0.787) | 0.824 (0.800-0.848) |
| Inception-v3 |
|
|
|
|
|
|
|
T1 | 0.724 (0.695-0.752) | 0.596 (0.552-0.639) | 0.680 (0.656-0.704) | 0.706 (0.677-0.735) |
|
|
T2 | 0.634 (0.603-0.665) | 0.734 (0.693-0.772) | 0.668 (0.644-0.693) | 0.734 (0.706-0.762) |
|
|
T1c | 0.769 (0.741-0.795) | 0.732 (0.691-0.770) | 0.756 (0.733-0.778) | 0.831 (0.807-0.855) |
| Inception-ResNet-v2 |
|
|
|
|
|
|
|
T1 | 0.774 (0.746-0.800) | 0.590 (0.546-0.633) | 0.711 (0.687-0.734) | 0.748 (0.720-0.776) |
|
|
T2 | 0.829 (0.804-0.852) | 0.529 (0.484-0.573) | 0.726 (0.702-0.748) | 0.804 (0.779-0.829) |
|
|
T1c | 0.812 (0.786-0.837) | 0.722 (0.681-0.761) | 0.781 (0.759-0.802) | 0.841 (0.818-0.864) |
| ERN-Netc |
|
|
|
|
|
|
|
T1 | 0.704 (0.674-0.732) | 0.519 (0.474-0.563) | 0.640 (0.615-0.665) | 0.646 (0.615-0.676) |
|
|
T2 | 0.634 (0.603-0.665) | 0.606 (0.562-0.648) | 0.624 (0.599-0.649) | 0.675 (0.645-0.705) |
|
|
T1c | 0.803 (0.777-0.828) | 0.643 (0.600-0.685) | 0.748 (0.725-0.770) | 0.807 (0.782-0.832) |
aVGG: Visual Geometry Group.
bResNet: residual neural network.
cERN-Net: efficient radionecrosis neural network.