Table 3.
Performance statistics of the five MSI classification models and two feature extractors for comparative analysis and WiseMSI on TSMCC cohort
| Model | Specificity (95% CI) | Sensitivity (95% CI) | AUC (95% CI) |
|---|---|---|---|
| EfficientNet | 0.973 (0.920, 1.026) | 0.3603 (0.071, 0,650) | 0.9050 (0.865, 0.946) |
| ViT | 0.978 (0.966, 0.990) | 0.7431 (0.681, 0.806) | 0.9385 (0.928, 0.949) |
| MIL | 0.9698 (0.950, 0.990) | 0.6680 (0.635, 0.701) | 0.9073 (0.884, 0.931) |
| AttMIL | 0.9950 (0.989, 1.001) | 0.6318 (0.579, 0.685) | 0.8993 (0.872, 0.927) |
| VarMIL | 0.992 (0.983, 1.000) | 0.624 (0.577, 0.671) | 0.903 (0.890, 0.915) |
| WiseMSI (Moco V2 version) | 0.926 (0.871, 0.980) | 0.793 (0.723, 0.863) | 0.937 (0.920, 0.95) |
| WiseMSI (ImageNet version) | 0.947 (0.937, 0.957) | 0.847 (0.826, 0.869) | 0.954 (0.948, 0.968) |
Performance is reported on WSI level and 95% confidence level (CI) is calculated based on 5-fold cross-validation for EfficientNet, Vit, MIL, and AttMIL, and 10-fold cross-validation of WiseMSI.