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计算机辅助超声实时弹性成像评估:145 个乳腺病变的初步经验。

Computer-assisted assessment of ultrasound real-time elastography: initial experience in 145 breast lesions.

机构信息

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.

Department of Medical Ultrasonics, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.

出版信息

Eur J Radiol. 2014 Jan;83(1):e1-7. doi: 10.1016/j.ejrad.2013.09.009. Epub 2013 Sep 23.

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.

摘要

目的

开发并评估一种基于超声实时弹性成像(RTE)的计算机辅助五分法弹性评分系统,用于对良恶性乳腺病变进行分类,以病理结果为参考标准。

材料与方法

本研究纳入了 145 个乳腺病变(67 个恶性,78 个良性)的常规超声(US)和 RTE 图像。采用水平集方法对 B 型图像上的每个病变进行自动勾画,并映射到 RTE 图像上。通过模糊 C 均值聚类方法对每个像素的相对弹性值进行重建,并分类为硬或软。根据病变内部及其周围组织的硬度程度,自动计算 RTE 图像的弹性评分。采用放射科医生的视觉评估进行比较诊断性能。组织病理学检查作为参考标准。采用学生 t 检验和受试者工作特征(ROC)曲线分析进行统计分析。

结果

将评分 4 或更高定义为恶性阳性,计算机辅助方案的诊断准确性、敏感度、特异度、阳性预测值(PPV)和阴性预测值(NPV)分别为 93.8%(136/145)、92.5%(62/67)、94.9%(74/78)、93.9%(62/66)和 93.7%(74/79),而手动评估分别为 89.7%(130/145)、85.1%(57/67)、93.6%(73/78)、92.0%(57/62)和 88.0%(73/83)。ROC 曲线下面积(Az 值)方面,该方法的 Az 值高于视觉评估(0.96 比 0.93)。

结论

计算机辅助五分法弹性评分系统的定量评估可以显著消除观察者间的变异性,从而提高分类乳腺病变的诊断信心,避免不必要的活检。

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