Computer-aided Diagnosis of Breast DCE-MRI Using Pharmacokinetic Model
Date Issued
2009
Date
2009
Author(s)
CHANG, PEI-KANG
Abstract
The breast cancer is the second most common cancer and the major cause of death for women. However, it is also a type of cancer that could be early detected and has an excellent curability in early stage. Recently, the computer-aided diagnosis (CAD) system develops persistently. For breast cancer, the CAD system provides the information about the tumor for radiologists and further detects the lesion and differentiates the benignancy from malignancy of the tumor. Thus, the invasive examination, like the biopsy, might be reduced. In this study, the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to record the change of the signal intensity of the tumor over time after injecting the contrast agent by vien. In this paper, a novel segmentation method using the kinetic color map and the area-under-the-curve color map is proposed to find the tumor. The fuzzy c-means clustering and the kinetic curve average are used to identify a kinetic curve of the tumor for analysis. The pharmacokinetic model is used to fit the kinetic curve of the tumor. The parameters of the fitted pharmacokinetic model are used as the diagnosis features for statistic analysis and they will be compared with the features of the exponential model and conventional kinetic curve characteristics. A total 124 lesions with 78 malignant and 46 benignant are included in our study. The result of our experiment shows that the feature set of pharmacokinetic model has better and stable performance. Its accuracy, sensitivity, specificity, and Az value are 79.84%, 80.77%, 78.26%, and 0.8604, respectively.
Subjects
CAD
DCE-MRI
Breast
Color Map
AUC
Kinetic
Pharmacokinetic
SDGs
Type
thesis
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