Predicting lymph node metastasis in colorectal cancer using case-level multiple instance learning

Table 2 Area under the receiver operating characteristic curve for lymph node metastasis prediction across models using 5-fold cross-validation

Category	Fold 1	Fold 2	Fold 3	Fold 4	Fold 5	mean ± SD
CONCH	0.732 (0.503-0.921)	0.767 (0.550-0.945)	0.865 (0.700-0.976)	0.934 (0.825-1.000)	0.770 (0.551-0.925)	0.814 ± 0.083
CONCH c	0.903 (0.758-0.994)	0.870 (0.703-0.994)	0.893 (0.743-0.988)	0.954 (0.855-1.000)	0.877 (0.731-0.982)	0.899 ± 0.033
CONCH ROI	0.709 (0.482-0.915)	0.798 (0.598-0.944)	0.843 (0.639-0.979)	0.924 (0.787-1.000)	0.755 (0.536-0.927)	0.806 ± 0.083
CONCH ROI-c	0.759 (0.544-0.929)	0.826 (0.642-0.958)	0.884 (0.725-0.993)	0.910 (0.765-1.000)	0.809 (0.621-0.963)	0.838 ± 0.060
UNI2	0.682 (0.450-0.893)	0.775 (0.577-0.941)	0.839 (0.648-0.970)	0.830 (0.650-0.970)	0.693 (0.476-0.879)	0.764 ± 0.074
UNI2 c	0.737 (0.519-0.911)	0.607 (0.364-0.824)	0.954 (0.851-1.000)	0.775 (0.556-0.944)	0.695 (0.458-0.875)	0.754 ± 0.128
UNI2 ROI	0.699 (0.475-0.905)	0.756 (0.539-0.929)	0.844 (0.654-0.976)	0.846 (0.673-0.976)	0.726 (0.516-0.911)	0.774 ± 0.068
UNI2 ROI-c	0.750 (0.536-0.929)	0.737 (0.521-0.911)	0.892 (0.737-1.000)	0.867 (0.703-0.987)	0.819 (0.643-0.952)	0.813 ± 0.069

Values represent area under the curve scores with 95%CI in brackets for each fold, alongside the mean ± SD across folds. Models evaluated include: CONCH (CONCH v1.5 trained on slide-level labels), CONCH-c (CONCH v1.5 trained on case-level labels), CONCH-ROI (CONCH v1.5 trained on slide-level labels with tumor area annotations), CONCH-ROI-c (CONCH v1.5 trained on case-level labels with tumor area annotations), UNI2 (UNI2-h trained on slide-level labels), UNI2-c (UNI2-h trained on case-level labels), UNI2-ROI (UNI2-h trained on slide-level labels with tumor area annotations), and UNI2-ROI-c (UNI2-h trained on case-level labels with tumor area annotations). ROI: Regions of interest.