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Table 2 Diagnostic performance of ME_ADC0–1000, BE_IVIM_D, ME_ADCall b, DKI-D and DKI-K by using RF, L1R-LR, PCA-LR, and SVM, respectively

From: Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR

Maps

AUC (95% CI)

RF

L1R-LR

PCA-LR

SVM

mAUC

ME-ADC0–1000

0.83 (0.80–0.87)

0.83 (0.79–0.87)

0.76 (0.70–0.81)

0.81 (0.76–0.85)

0.81

BE-IVIM-D

0.85 (0.81–0.89)

0.83 (0.78–0.87)

0.75 (0.70–0.82)

0.80 (0.75–0.85)

0.81

ME-ADCall b

0.84 (0.80–0.87)

0.82 (0.79–0.87)

0.77 (0.70–0.83)

0.79 (0.74–0.85)

0.81

DKI-D

0.83 (0.80–0.86)

0.83 (0.78–0.86)

0.75 (0.74–0.82)

0.80 (0.77–0.84)

0.80

DKI-K

0.84 (0.81–0.89)

0.83 (0.78–0.87)

0.74 (0.70–0.80)

0.79 (0.75–0.85)

0.80

  1. RF: random forest; SVM: support vector machine; PCA: principal component analysis; L1R: L1 regularization; LR: linear regression; mAUC: mean values of AUCs of RF, L1R-LR, PCA-LR and SVM