Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis

• Published in: Magnetic resonance imaging Vol. 32; no. 3; pp. 197 - 205
• Main Authors: Wang, Teh-Chen, Huang, Yan-Hao, Huang, Chiun-Sheng, Chen, Jeon-Hor, Huang, Guei-Yu, Chang, Yeun-Chung, Chang, Ruey-Feng
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.04.2014
• Subjects: Adult; Aged; Aged, 80 and over; Breast; Breast Neoplasms - diagnosis; Breast Neoplasms - metabolism; Contrast Media - pharmacokinetics; DCE-MRI; Ellipsoid; Female; Gadolinium DTPA - pharmacokinetics; GLCM; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging - methods; Middle Aged; Models, Biological; Models, Statistical; Morphology; Pattern Recognition, Automated - methods; Pharmacokinetic; Radiology; Reproducibility of Results; Sensitivity and Specificity; Breast; DCE-MRI; Ellipsoid; Morphology; Pharmacokinetic; GLCM
• ISSN: 0730-725X; 1873-5894; 1873-5894
• DOI: 10.1016/j.mri.2013.12.002

Three-dimensional (3-D) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) consists of a large number of images in different enhancement phases which are used to identify and characterize breast lesions. The purpose of this study was to develop a computer-assisted algorithm for tumor segmentation and characterization using both kinetic information and morphological features of 3-D breast DCE-MRI. An integrated color map created by intersecting kinetic and area under the curve (AUC) color maps was used to detect potential breast lesions, followed by the application of a region growing algorithm to segment the tumor. Modified fuzzy c-means clustering was used to identify the most representative kinetic curve of the whole segmented tumor, which was then characterized by using conventional curve analysis or pharmacokinetic model. The 3-D morphological features including shape features (compactness, margin, and ellipsoid fitting) and texture features (based on the grey level co-occurrence matrix) of the segmented tumor were obtained to characterize the lesion. One hundred and thirty-two biopsy-proven lesions (63 benign and 69 malignant) were used to evaluate the performance of the proposed computer-aided system for breast MRI. Five combined features including rate constant (kep), volume of plasma (vp), energy (G1), entropy (G2), and compactness (C1), had the best performance with an accuracy of 91.67% (121/132), sensitivity of 91.30% (63/69), specificity of 92.06% (58/63), and Az value of 0.9427. Combining the kinetic and morphological features of 3-D breast MRI is a potentially useful and robust algorithm when attempting to differentiate benign and malignant lesions.