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Performance of machine learning models and the SPAN-100 index in 3 cohorts
Cohorts/Models Sensitivity (%) Specificity (%) Accuracy (%) AUC
Full cohort 
 Clinical features only (16 features) 78.1 65.5 73.5 0.77
 Imaging features only (11 features) 53.5 79.9 63.2 0.69
 Both clinical and imaging features (27 features) 74.4 69.8 72.8 0.79
 Best-performing clinical and imaging features (6 features) 72.2 74.0 72.8 0.80^{^a}
 SPAN-100 80.6 64.3 73.5 0.78
Recanalized 
 Clinical features only (16 features) 73.1 70.4 71.9 0.76
 Imaging features only (11 features) 53.7 69.4 60.9 0.61
 Both clinical and imaging features (27 features) 74.5 68.9 72.0 0.77
 Best-performing clinical and imaging features (6 features) 76.9 69.9 73.8 0.79^{^a}
 SPAN-100 78.8 63.8 71.9 0.76
Nonrecanalized 
 Clinical features only (16 features) 80.0 65.8 74.1 0.78
 Imaging features only (11 features) 63.3 67.8 64.3 0.70
 Both clinical and imaging features (27 features) 71.3 80.5 73.3 0.81
 Best-performing clinical and imaging features (6 features) 81.9 75.4 80.5 0.82^{^a}
 SPAN-100 65.5 77.1 68.1 0.78
↵a Model with the highest AUC value.