TABLE 6.

Performance of classifiers with pathology-informed CT biomarkers and complementary clinical variables (tumor diameter, gender, age) as inputs.

Model name	ROC-AUC	95% CI	F1-score	Sensitivity	Specificity
Tumor-Diameter as feature
Random forest	0.679 ± 0.023	(0.568, 0.815)	0.615 ± 0.032	0.605 ± 0.047	0.638 ± 0.013
Pathology-informed CT biomarkers + clinical variables
CorrFABR	0.855 ± 0.005	(0.775, 0.947)	0.793 ± 0.029	0.741 ± 0.058	0.876 ± 0.032
BiomedCLIP-CorrFeat-Avg-Clinical	0.841 ± 0.011	(0.761, 0.939)	0.788 ± 0.022	0.768 ± 0.044	0.822 ± 0.037
VGGCorrFeat-HED-90-Clinical	0.802 ± 0.025	(0.737, 0.923)	0.735 ± 0.042	0.730 ± 0.064	0.746 ± 0.056

Bold values indicate best performance.

Note: Our proposed CorrFABR method that uses pathology-informed CT biomarkers and clinical variables in a MLP classifier outperforms other methods. Adding clinical variables improves performance of all models, irrespective of the type of imaging feature extractor (DinoV2, BiomedCLIP, VGG16) and the type of classifier. Abbreviations: CorrFABR, correlated feature aggregation by region; CT, computed tomography; HED, holistically nested edge detection; MLP, multi-layer perceptron-based; ROC-AUC, area under the receiver operating characteristics curve.