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Table 5 ROC metrics for predicting PCR based on molecular subtypes, MRI features at pre- and during NAC using Ensemble RUSBoosted Tree classifier based on single view and multiview without SMOTE

From: Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response

Time point Features type Sens. Spec. PPV NPV Accuracy AUC
(View 1) Molecular subtypes 86.48 76.92 91.42 66.66 84 0.82 (0.66, 0.97)
Tp1 (view 2) MRI texture only 86.48 84.62 94.12 68.75 86 0.88 (0.77, 1.0)
Tp2 (view 3) 97.30 38.46 81.82 83.33 82 0.72 (0.53, 0.91)
Tp1 + Tp2 (view 4) 1.00 76.92 92.50 1.00 84 0.96 (0.92, 1.00)
Multiview Molecular subtype and MRI texture 97.0 85.0 94.70 91.70 94.0 0.96 (0.91, 1.0)
  1. The data were tumor contours based on post-contrast-enhanced MRI with morphological dilation. The numbers in parenthesis show the 95% confidence intervals
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BioMedical Engineering OnLine

ISSN: 1475-925X

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