Classification of Signal-Time Curves Obtained by Dynamic Magnetic Resonance Mammography

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Abstract
This study compares the performance of quantitative methods for the characterization of signal-time curves acquired by dynamic contrast-enhanced magnetic resonance mammography from 253 females. Signal-time curves obtained from 105 parenchyma, 162 malignant, and 91 benign tissue regions were examined (243 lesions were histopathologically validated). A neural network, a nearest-neighbor, and a threshold classifier were applied to either the entire signal-time curve or pharmacokinetic and descriptive parameters calculated from the curves to differentiate between 2 (malignant or benign) or 3 tissue classes (malignant, benign, or parenchyma). The classifiers were tuned and evaluated according to their performance on 2 distinct subsets of the curves. The accuracy determined for the neural network and the nearest-neighbor classifiers was nearly identical (approximately 80% in case of 3 tissue classes, and approximately 76% in case of the 2 classes). In contrast, the accuracy of the threshold classifier applied to the discrimination of 3 classes was low (65%). Quantitative classifiers can support the radiologist in the diagnosis of breast lesions.