Classification of signal-time curves obtained by dynamic magnetic resonance mammography: statistical comparison of quantitative methods.
OBJECTIVE
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.


METHODS
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.


RESULTS
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%).


CONCLUSIONS
Quantitative classifiers can support the radiologist in the diagnosis of breast lesions.
Federal Office for Radiation Protection, Department of Radiation and Health, Division of Medical Radiation Hygiene and Dosimety, Neuherberg, Germany.
Robert E A
RE
Lucht
Stefan
S
Delorme
Jürgen
J
Hei
Michael V
MV
Knopp
Marc-André
MA
Weber
Jürgen
J
Griebel
Gunnar
G
Brix
D001940:Breast / Q000473:pathology; D001941:Breast Diseases / Q000473:pathology; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology; D003287:Contrast Media; D005260:Female; D006801:Humans; D007089:Image Enhancement; D008279:Magnetic Resonance Imaging; D008327:Mammography / Q000379:methods*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors
D003287:Contrast Media