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Table 1 The optimal single-factor predictors with AUC, sensitivity and specificity for three morphological indices using four machine learning methods

From: Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid–structure interaction models and machine learning methods with patient follow-up data: a feasibility study

Index ΔLPI ΔCTI ΔMPVI
Method Factor AUC (Spe, Sen) Factor AUC (Spe, Sen) Factor AUC (Spe, Sen)
RF Critical PWSn 0.856 (0.928, 0.749) MinCT 0.749 (0.858, 0.555) MinCT 0.785 (0.863, 0.656)
DA Plaque area 0.875 (0.872, 0.677) MinCT 0.818 (0.868, 0.613) MinCT 0.752 (0.830, 0.646)
SVM Wall thickness 0.883 (0.947, 0.653) MinCT 0.697 (0.853, 0.334) MinCT 0.727 (0.866, 0.404)
EL Plaque area 0.776 (0.927, 0.767) MinCT 0.719 (0.852, 0.530) MinCT 0.766 (0.864, 0.654)
  1. Bold indicates that AUC value is the largest in this columns
  2. Sen sensitivity, Spe specificity
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BioMedical Engineering OnLine

ISSN: 1475-925X

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