2019
DOI: 10.3892/ol.2019.9916
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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, o… Show more

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Cited by 4 publications
(4 citation statements)
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“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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].…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%