Table 4.
Performance of each model in the training and testing datasets
| Model | Training dataset | Testing dataset | External dataset | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AUC (95% CI) | p | Sen | Spe | AUC (95% CI) | p | Sen | Spe | AUC (95% CI) | p | Sen | Spe | |
| Radiomics model | 0.914 (0.814–0.970) | < 0.001 | 0.93 | 0.74 | 0.756 (0.545–0.904) | 0.014 | 0.58 | 0.92 | 0.806 (0.606–0.933) | < 0.001 | 0.80 | 0.82 |
| Protein model | 0.756 (0.630–0.857) | < 0.001 | 0.90 | 0.48 | 0.631 (0.417–0.814) | 0.238 | 0.50 | 0.77 | 0.503 (0.302–0.703) | 0.979 | 0.87 | 0.27 |
| Combined model | 0.905 (0.803–0.965) | < 0.001 | 1.00 | 0.64 | 0.801 (0.594–0.932) | 0.003 | 0.67 | 1.00 | 0.782 (0.577–0.918) | 0.002 | 0.53 | 1.00 |
AUC area under the curve, CI confidence interval, Sen sensitivity, Spe specificity