Table 2 Results for different involved architectures and data pipelines using the testing datasets in Config1. Significant values are in bold underline.
From: Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion
Model | Architecture | Mean AUC (95% CI) | Mean specificity (95% CI) | Mean sensitivity (95% CI) |
|---|---|---|---|---|
ResNet | A | 0.73 (0.70–0.75) | 0.71 (0.70–0.74) | 0.72 (0.70–0.75) |
B | 0.75 (0.72–0.77) | 0.70 (0.70–0.73) | 0.71 (0.70–0.74) | |
C | 0.79 (0.77–0.81) | 0.75 (0.72–0.77) | 0.77 (0.74–0.79) | |
D | 0.70 (0.69–0.74) | 0.73 (0.71–0.77) | 0.73 (0.71–0.76) | |
E | 0.75 (0.72–0.78) | 0.74 (0.71–0.78) | 0.74 (0.72–0.77) | |
F | 0.74 (0.71–0.76) | 0.74 (0.71–0.76) | 0.74 (0.71–0.76) | |
DenseNet | A | 0.71 (0.70–0.74) | 0.70 (0.70–0.72) | 0.71 (0.70–0.74) |
B | 0.78 (0.76–0.81) | 0.71 (0.71–0.73) | 0.72 (0.70–0.73) | |
C | 0.80 (0.78–0.82) | 0.76 (0.74–0.78) | 0.76 (0.74–0.79) | |
D | 0.78 (0.77–0.82) | 0.73 (0.71–0.75) | 0.74 (0.72–0.76) | |
E | 0.77 (0.76–0.80) | 0.75 (0.73–0.77) | 0.73 (0.71–0.75) | |
F | 0.75 (0.72–0.76) | 0.74 (0.72–0.76) | 0.74 (0.72–0.77) | |
EfficientNet | A | 0.73 (0.73–0.77) | 0.72 (0.70–0.74) | 0.71 (0.70–0.75) |
B | 0.75 (0.73–0.78) | 0.71 (0.70–0.74) | 0.71 (0.70–0.73) | |
C | 0.73 (0.70–0.75) | 0.74 (0.72–0.76) | 0.73 (0.71–0.75) | |
D | 0.77 (0.74–0.79) | 0.74 (0.72–0.77) | 0.73 (0.70–0.74) | |
E | 0.76 (0.75–0.79) | 0.73 (0.71–0.74) | 0.74 (0.71–0.76) | |
F | 0.73 (0.70–0.75) | 0.75 (0.72–0.77) | 0.73 (0.71–0.74) |
