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Table 5 Classification results for differentiation of malignant and benign lesions for manually annotated lesions and automatic segmented lesions using automatic feature selection

From: Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features

Feature selection method Manual annotation Automatic segmentation
  AUC (mean ± SD) Sensitivity/specificity Number of features AUC (mean ± SD) Sensitivity/specificity Number of features
GI LOOCV 0.949 ± 0.019 0.920/0.868 4 0.771 ± 0.040 0.961/0.482 100
GI LOOCV without PET 0.946 ± 0.002 0.924/0.859 4 0.771 ± 0.040 0.972/0.486 75
GI LOOCV without DWI 0.949 ± 0.015 0.915/0.873 4 0.754 ± 0.035 0.983/0.427 75
GI LOOCV without DWI, without PET 0.944 ± 0.018 0.922/0.868 4 0.755 ± 0.033 0.976/0.409 75
mRMR LOOCV 0.978 ± 0.008 0.946/0.936 2 0.858 ± 0.013 0.941/0.773 3
mRMR LOOCV w/o PET 0.975 ± 0.010 0.957/0.918 2 0.856 ± 0.018 0.948/0.736 3
mRMR LOOCV w/o DWI 0.977 ± 0.006 0.954/0.950 2 0.857 ± 0.017 0.943/0.745 3
mRMR LOOCV w/o DWI, PET 0.973 ± 0.010 0.950/0.927 2 0.861 ± 0.009 0.941/0.755 3
  1. AUC area under the curve at receiver operating characteristic analysis, DCE-MRI dynamic contrast-enhanced magnetic resonance imaging, DWI diffusion-weighted imaging, GI Gini importance, LOOCV leave-one-out cross-validation, mRMR minimum-redundancy-maximum-relevance, PET positron emission tomography, SD standard deviation. Values presented in bold are the highest values