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.
Lucht
Robert E A
RE
Federal Office for Radiation Protection, Department of Radiation and Health, Division of Medical Radiation Hygiene and Dosimety, Neuherberg, Germany.
Delorme
Stefan
S
Hei
Jürgen
J
Knopp
Michael V
MV
Weber
Marc-André
MA
Griebel
Jürgen
J
Brix
Gunnar
G
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