Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast is being used increasingly in the detection and diagnosis of breast cancer as a complementary modality to mammography and sonography. Although the potential diagnostic value of kinetic curves in DCE-MRI is established, the method for generating kinetic curves is not standardized. The inherent reason that curve identification is needed is that the uptake of contrast agent in a breast lesion is often heterogeneous, especially in malignant lesions. It is accepted that manual region of interest selection in 4D breast magnetic resonance (MR) images to generate the kinetic curve is a time-consuming process and suffers from significant inter- and intraobserver variability. We investigated and developed a fuzzy c-means (FCM) clustering-based technique for automatically identifying characteristic kinetic curves from breast lesions in DCE-MRI of the breast. Dynamic contrast-enhanced MR images were obtained using a T1-weighted 3D spoiled gradient echo sequence with Gd-DTPA dose of 0.2 mmol/kg and temporal resolution of 69 s. FCM clustering was applied to automatically partition the signal-time curves in a segmented 3D breast lesion into a number of classes (i.e., prototypic curves). The prototypic curve with the highest initial enhancement was selected as the representative characteristic kinetic curve (CKC) of the lesion. Four features were then extracted from each characteristic kinetic curve to depict the maximum contrast enhancement, time to peak, uptake rate, and washout rate of the lesion kinetics. The performance of the kinetic features in the task of distinguishing between benign and malignant lesions was assessed by receiver operating characteristic analysis. With a database of 121 breast lesions (77 malignant and 44 benign cases), the classification performance of the FCM-identified CKCs was found to be better than that from the curves obtained by averaging over the entire lesion and similar to kinetic curves generated from regions drawn within the lesion by a radiologist experienced in breast MRI.
Chen
Weijie
W
Department of Radiology, Committee on Medical Physics, The University of Chicago, Chicago, Illinois 60637, USA. weijie@uchicago.edu
Giger
Maryellen L
ML
Bick
Ulrich
U
Newstead
Gillian M
GM
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism*; D003198:Computer Simulation; D003287:Contrast Media; D005260:Female; D019786:Gadolinium DTPA / Q000493:pharmacokinetics*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D007700:Kinetics; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008657:Metabolic Clearance Rate; D008875:Middle Aged; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D003287:Contrast Media; D019786:Gadolinium DTPA