Classification of breast mass lesions on dynamic contrast-enhanced magnetic resonance imaging by a computer-assisted diagnosis system based on quantitative analysis

Abstract

The aim of the current study was to develop a semi-automatic and quantitative method for the analysis of a time-intensity curve (TIC) from breast dynamic contrastenhanced magnetic resonance imaging. The performance of the proposed method, based on the level set segmentation algorithm, was evaluated by comparison with the traditional method. In the traditional method, the lesion area is delineated manually and the corresponding mean TIC is classified subjectively as one of three washout patterns. In addition, only one quantitative parameter, the maximum slope of increase (MSI), is calculated. In the proposed method, the lesion region was determined semi-automatically and the corresponding mean TIC was categorized quantitatively. In addition to MSI, a number of quantitative parameters were derived from the mean TIC and lesion area, including signal intensity slope (SI slope), initial percentage of enhancement (E initial), percentage of peak enhancement (E peak), early signal enhancement ratio (ESER) and second enhancement percentage (SEP). Wilcoxon signed-rank test and receiver operating characteristic analyses were performed for statistical analysis. For TIC categorization the accuracy was 61.54% for the traditional method and 82.05% for the proposed method. Using the proposed method, mean curve accuracies were 84.0% for SI slope , 66.7% for MSI, 66.0% for E initial , 66.0% for E peak , 68.0% for ESER and 44.9% for SEP. In the lesion region, the accuracies for the aforementioned parameters were 80.8, 65.4, 66.7, 62.2, 69.2 and 57.1%, respectively. Accuracy of the MSI value derived from the traditional method was 63.4%. Compared with the traditional method, the proposed semi-automatic method in the current study may provide results with a higher accuracy to differentiate benign and malignant lesions. Therefore, the proposed method should be considered as a supplementary tool for the diagnosis of breast lesions.

Can contrast-enhanced mammography replace dynamic contrast-enhanced MRI in the assessment of sonomammographic indeterminate breast lesions?

“…Dynamic contrast-enhanced MRI breast has been used in the assessment of indeterminate mammographic lesions for a long time [11]. The disadvantages of CE-MRI are mainly its relatively high cost, long examination time, limited availability compared to the availability of Fig.…”

Deep Learning-based Classification of Breast Tumors using Raw Microwave Imaging Data

“…In their study, an accuracy of 92% was achieved using a feature extraction method based on genetic algorithm [20]. Yin et al [23] presented a statistical classifier for the images obtained by the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) method. In the time-intensity curve (TIC) categorization using data with 85 malignant and 71 benign breast lesions, sensitivity, specificity, and accuracy values were 83.5%, 80.3%, and 82.1%, respectively [23].…”

“…Yin et al [23] presented a statistical classifier for the images obtained by the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) method. In the time-intensity curve (TIC) categorization using data with 85 malignant and 71 benign breast lesions, sensitivity, specificity, and accuracy values were 83.5%, 80.3%, and 82.1%, respectively [23]. Abdel-Nasser et al [25] demonstrated a random forest classifier model for classifying USI that contained 31 malignant and 28 benign cases with an AUC of 0.99.…”

Pharmacokinetic Analysis of Enhancement-Constrained Acceleration (ECA) reconstruction-based high temporal resolution breast DCE-MRI

“…This provides dynamic information on the exchange of the GBCA between the vascular and interstitial compartments [1]. DCE-MRI is a valuable tool in oncology including early diagnosis [2], lesion classification [3], treatment planning [4] and treatment response assessment [5]. Analysis of DCE-MRI datasets is typically performed at different levels of complexity: (1) based on visualization of images, (2) based on semi-quantitative parameters derived from the signal enhancement curve as a function of time (e.g.…”