Table 1 Classification model performance for GL261 in different tumor regions.
From: Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study
| Â | All | 95% CI | Model Performance Evaluation: GL261 | 95% CI | ||||
|---|---|---|---|---|---|---|---|---|
Center | 95% CI | Middle | 95% CI | Edge | ||||
AUC | 0.923 | — | 0.909 | — | 0.960 | — | 0.732 | — |
Accuracy | 0.908 | 0.904, 0.911 | 0.840 | 0.837, 0.843 | 0.87 | 0.861, 0.875, | 0.72 | 0.72, 0.73 |
Specificity | 0.92 | 0.91, 0.92 | 0.811 | 0.808, 0.815 | 0.919 | 0.910, 0.928 | 0.69 | 0.68, 0.70 |
Sensitivity | 0.893 | 0.889, 0.90 | 0.88 | 0.87, 0.88 | 0.786 | 0.779, 0.793 | 0.77 | 0.76, 0.78 |
F-Score | 0.888 | 0.884, 0.891 | 0.824 | 0.821, 0.827 | 0.821 | 0.812, 0.829 | 0.69 | 0.69, 0.70 |
