Xue Zhang1, Yang Xiao1, Jie Zeng2, Weibao Qiu1, Ming Qian1, Congzhi Wang1, Rongqin Zheng3, Hairong Zheng4. 1. Shenzhen Key Lab for Molecular Imaging, Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. 2. Department of Medical Ultrasonics, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China. 3. Department of Medical Ultrasonics, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China. Electronic address: zhengronggin@hotmail.com. 4. Shenzhen Key Lab for Molecular Imaging, Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. Electronic address: hr.zheng@siat.ac.cn.
Abstract
PURPOSE: To develop and evaluate a computer-assisted method of quantifying five-point elasticity scoring system based on ultrasound real-time elastography (RTE), for classifying benign and malignant breast lesions, with pathologic results as the reference standard. MATERIALS AND METHODS: Conventional ultrasonography (US) and RTE images of 145 breast lesions (67 malignant, 78 benign) were performed in this study. Each lesion was automatically contoured on the B-mode image by the level set method and mapped on the RTE image. The relative elasticity value of each pixel was reconstructed and classified into hard or soft by the fuzzy c-means clustering method. According to the hardness degree inside lesion and its surrounding tissue, the elasticity score of the RTE image was computed in an automatic way. Visual assessments of the radiologists were used for comparing the diagnostic performance. Histopathologic examination was used as the reference standard. The Student's t test and receiver operating characteristic (ROC) curve analysis were performed for statistical analysis. RESULTS: Considering score 4 or higher as test positive for malignancy, the diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 93.8% (136/145), 92.5% (62/67), 94.9% (74/78), 93.9% (62/66), and 93.7% (74/79) for the computer-assisted scheme, and 89.7% (130/145), 85.1% (57/67), 93.6% (73/78), 92.0% (57/62), and 88.0% (73/83) for manual assessment. Area under ROC curve (Az value) for the proposed method was higher than the Az value for visual assessment (0.96 vs. 0.93). CONCLUSION: Computer-assisted quantification of classical five-point scoring system can significantly eliminate the interobserver variability and thereby improve the diagnostic confidence of classifying the breast lesions to avoid unnecessary biopsy.
PURPOSE: To develop and evaluate a computer-assisted method of quantifying five-point elasticity scoring system based on ultrasound real-time elastography (RTE), for classifying benign and malignant breast lesions, with pathologic results as the reference standard. MATERIALS AND METHODS: Conventional ultrasonography (US) and RTE images of 145 breast lesions (67 malignant, 78 benign) were performed in this study. Each lesion was automatically contoured on the B-mode image by the level set method and mapped on the RTE image. The relative elasticity value of each pixel was reconstructed and classified into hard or soft by the fuzzy c-means clustering method. According to the hardness degree inside lesion and its surrounding tissue, the elasticity score of the RTE image was computed in an automatic way. Visual assessments of the radiologists were used for comparing the diagnostic performance. Histopathologic examination was used as the reference standard. The Student's t test and receiver operating characteristic (ROC) curve analysis were performed for statistical analysis. RESULTS: Considering score 4 or higher as test positive for malignancy, the diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 93.8% (136/145), 92.5% (62/67), 94.9% (74/78), 93.9% (62/66), and 93.7% (74/79) for the computer-assisted scheme, and 89.7% (130/145), 85.1% (57/67), 93.6% (73/78), 92.0% (57/62), and 88.0% (73/83) for manual assessment. Area under ROC curve (Az value) for the proposed method was higher than the Az value for visual assessment (0.96 vs. 0.93). CONCLUSION: Computer-assisted quantification of classical five-point scoring system can significantly eliminate the interobserver variability and thereby improve the diagnostic confidence of classifying the breast lesions to avoid unnecessary biopsy.
Authors: Ying Shi; Xing-Hua Wang; Huan-Hu Zhang; Hai-Qing Zhang; Ji-Zheng Tu; Kun Wei; Juan Li; Xiao-Li Liu Journal: Int J Clin Exp Med Date: 2014-04-15
Authors: Karem D Marcomini; Eduardo F C Fleury; Vilmar M Oliveira; Antonio A O Carneiro; Homero Schiabel; Robert M Nishikawa Journal: Bioengineering (Basel) Date: 2018-08-09