Abstract
The aim of the paper is to identify the breast malignant and benign lesions using the features of apparent diffusion coefficient (ADC), perfusion fraction f, pseudodiffusion coefficient D⁎, and true diffusion coefficient D from intravoxel incoherent motion (IVIM). There are 69 malignant cases (including 9 early malignant cases) and 35 benign breast cases who underwent diffusion-weighted MRI at 3.0 T with 8 b-values (0~1000 s/mm2). ADC and IVIM parameters were determined in lesions. The early malignant cases are used as advanced malignant and benign tumors, respectively, so as to assess the effectiveness on the result. A predictive model was constructed using Support Vector Machine Binary Classification (SVMBC, also known Support Vector Machine Discriminant Analysis (SVMDA)) and Partial Least Squares Discriminant Analysis (PLSDA) and compared the difference between them both. The D value and ADC provide accurate identification of malignant lesions with b = 300, if early malignant tumor was considered as advanced malignant (cancer). The classification accuracy is 93.5% for cross-validation using SVMBC with ADC and tissue diffusivity only. The sensitivity and specificity are 100% and 87.0%, respectively, r2cv = 0.8163, and root mean square error of cross-validation (RMSECV) is 0.043. ADC and IVIM provide quantitative measurement of tissue diffusivity for cellularity and are helpful with the method of SVMBC, getting comprehensive and complementary information for differentiation between benign and malignant breast lesions.
Highlights
Breast cancer is the most prevalent cancer among women worldwide
The Region of concern (ROC) analyses to assess diagnostic utility for the detection of malignant lesions reveals that the average apparent diffusion coefficient (ADC) and D values had higher area under the ROC curve (AUC) values (0.942 and 0.921, resp.), Youden index (0.7839 and 0.7834, resp.), and Matthews correlation coefficient (0.7579 and 0.7493, resp.) when bvalue = 300
The results indicate that SVMBC can improve accuracy when classifying early malignant tumor as advanced malignant, compared with another b-value; the ADC and tissue diffusivity with b = 300 had the best results, where rc = 0.8163, the sensitivity is 1 (I think it is happened to get), specificity is 0.870, root mean square error of cross-validation (RMSECV) is 0.0435, and accuracy is 93.5%
Summary
Breast cancer is the most prevalent cancer among women worldwide. current imaging approaches (such as mammography) often do not provide enough information for proper lesion management, which sometimes results in unnecessary invasive treatments. Magnetic resonance imaging (MRI) and measurements of the apparent diffusion coefficient (ADC) have proven useful in the detection and characterization of cancer [1]. DWI (diffusion-weighted imaging) is a functional magnetic resonance imaging (fMRI) of noninvasive examination; it can directly reflect the water molecule’s Brownian motion in body’s tissues. It can obtain physiological characteristics in body’s tissues based on the quantitative analysis of water molecule’s apparent diffusion coefficient (ADC). DWI has been widely used clinically, and it is through monoexponential model to calculate the ADC value, which contains two kinds of information of microcirculation perfusion and the water molecule diffusion of the organization. IVIMs are positively studying tumors in these aspects of blood perfusion, the identification between benign and malignant, scope of infringement, and curative effect evaluation
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