TABLE 4.

Comparing low-res and high-res approaches of pathology feature extraction and aggregation demonstrates higher performance with the high-res approach that uses CONCH-selected patches with a normalized cancer similarity score >= 0.9

Model name	Path. Feat.	five-fold ROC-AUC	95% Confidence interval
Training MLP with pathology informed CT biomarkers
Dinov2-Corrfeat-Avg-Low-res	Low-res	0.796 ± 0.018	(0.714, 0.907)
Dinov2-Corrfeat-Avg	High-res	0.815 ± 0.007	(0.725, 0.910)
BiomedCLIP-Corrfeat-Avg-Low-res	Low-res	0.771 ± 0.022	(0.672, 0.898)
BiomedCLIP-Corrfeat-Avg	High-res	0.794 ± 0.007	(0.703, 0.914)

Bold values indicate best performance.

Note: The five-fold ROC-AUC values are reported as mean ± standard deviation across the five-folds.

Abbreviations: CONCH, CONtrastive learning from Captions of Histopathology; MLP, multi-layer perceptron; ROC-AUC, area under the receiver operating characteristics curve.