TABLE 5.
Comparing performance of CT imaging features and pathology-informed CT biomarkers in classifying ccRCC tumors.
| Model name | ROC-AUC | 95% CI | F1-score | Sensitivity | Specificity |
|---|---|---|---|---|---|
|
| |||||
| Classification Models | |||||
| Transfer learning with CT slices or training MLP with foundation-model derived features | |||||
| VGG16-fine-tune | 0.778 ± 0.024 | (0.707, 0.897) | 0.654 ± 0.073 | 0.616 ± 0.143 | 0.757 ± 0.117 |
| DinoV2-PCA | 0.325 ± 0.130 | (0.122, 0.336) | 0.408 ± 0.087 | 0.438 ± 0.075 | 0.27 ± 0.204 |
| DinoV2-KPCA | 0.782 ± 0.026 | (0.708, 0.905) | 0.709 ± 0.022 | 0.719 ± 0.044 | 0.692 ± 0.070 |
| BiomedCLIP-PCA | 0.391 ± 0.114 | (0.170, 0.397) | 0.499 ± 0.074 | 0.573 ± 0.106 | 0.281 ± 0.122 |
| BiomedCLIP-KPCA | 0.625 ± 0.117 | (0.585, 0.823) | 0.629 ± 0.048 | 0.692 ± 0.037 | 0.486 ± 0.112 |
| Training MLP with pathology informed CT biomarkers | |||||
| Dinov2-Corrfeat-Avg | 0.815 ± 0.007 | (0.725, 0.910) | 0.663 ± 0.047 | 0.573 ± 0.083 | 0.854 ± 0.056 |
| Dinov2-Corrfeat-AvgStd | 0.768 ± 0.086 | (0.722, 0.911) | 0.654 ± 0.086 | 0.578 ± 0.079 | 0.811 ± 0.070 |
| BiomedCLIP-Corrfeat-Avg | 0.794 ± 0.008 | (0.703, 0.914) | 0.657 ± 0.028 | 0.519 ± 0.032 | 0.941 ± 0.011 |
| BiomedCLIP-Corrfeat-AvgStd | 0.765 ± 0.021 | (0.693, 0.900) | 0.642 ± 0.056 | 0.541 ± 0.082 | 0.865 ± 0.057 |
| Segmentation models with classification head | |||||
| Training segmentation + classification models with CT images | |||||
| HED-75 | 0.723 ± 0.036 | (0.610, 0.849) | 0.641 ± 0.027 | 0.557 ± 0.050 | 0.822 ± 0.047 |
| HED-90 | 0.729 ± 0.035 | (0.622, 0.857) | 0.651 ± 0.024 | 0.578 ± 0.047 | 0.805 ± 0.036 |
| Training segmentation+classification models with pathology-informed CT biomarkers | |||||
| VGGCorrFeat-HED-75 | 0.758 ± 0.029 | (0.662, 0.881) | 0.7 ± 0.053 | 0.632 ± 0.085 | 0.832 ± 0.040 |
| VGGCorrFeat-HED-90 | 0.767 ± 0.030 | (0.663, 0.884) | 0.694 ± 0.053 | 0.649 ± 0.089 | 0.789 ± 0.032 |
Bold values indicate best performance.
Note: Pathology-informed CT biomarkers improve ROC-AUC irrespective of the kind of features and the models used. DinoV2-CorrFeat-Avg achieves the best ROC-AUC performance, with the lowest standard deviation value across the five-folds.
Abbreviations:ccRCC,clear cell Renal Cell Carcinoma;CT,computed tomography;HED, holistically nested edge detection;KPCA,kernel principal component analysis; MLP, multi-layer perceptron-based; PCA, principal component analysis; ROC-AUC, area under the receiver operating characteristics curve.