Table 3.
Diagnostic performance of each model for NSCLC bone metastases in external validation cohort.
| Model | Accuracy | AUC (95% CI) | Kappa | Sensitivity % | Specitivity % | Youden index | F1 score |
|---|---|---|---|---|---|---|---|
| DT | 0.80 | 0.79 (0.73–0.86) | 0.61 | 81.93% | 79.00% | 0.61 | 0.81 |
| Logistic | 0.83 | 0.90 (0.85–0.94) | 0.65 | 84.00% | 81.00% | 0.65 | 0.83 |
| MLP | 0.81 | 0.91 (0.86–0.95) | 0.62 | 70.34% | 91.76% | 0.62 | 0.79 |
| RF | 0.89 | 0.97 (0.94–0.99) | 0.77 | 86.11% | 90.65% | 0.77 | 0.89 |
| SVM | 0.69 | 0.92 (0.87–0.96) | 0.38 | 42.36% | 97.06% | 0.39 | 0.58 |
| Xgboost | 0.86 | 0.95 (0.91–0.98) | 0.72 | 88.36% | 84.12% | 0.72 | 0.87 |
NSCLC: Non-Small Cell Lung Cancer; AUC: Area Under Curve; CI: Confidence Interval; DT: Decision tree model; Logistic: Logistic regression model; MLP: Multilayer perceptron model; RF: Random forest model; SVM: Support vector machine model; Xgboost: Extreme gradient boosting model.