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Superb microvascular imaging in diagnosis of benign and malignant breast lesions / 中国医学影像技术
Chinese Journal of Medical Imaging Technology ; (12): 77-81, 2019.
Article in Chinese | WPRIM | ID: wpr-861496
ABSTRACT
Objective To explore the value of superb microvascular imaging (SMI) for differentiating benign and malignant breast lesions. Methods Totally 281 patients (300 lesions) confirmed with pathology were enrolled, and the data of 2D gray-scale, CDFI and SMI were analyzed retrospectively. Adler's grading was applied to classify blood flow in breast lesions, and the ability of CDFI and SMI in detecting blood flow was compared. The vascular morphology features of breast lesions were also observed with CDFI and SMI, and then were classified and compared between benign and malignant lesions. Results The detection rate of blood flow with CDFI was 78.33% (235/300), with SMI was 90.33% (271/300), and the difference was statistically significant (χ2=116.066, P<0.001). Both CDFI and SMI showed statistically significant differences in blood flow Adler grading between benign and malignant lesions (both P<0.001). The difference of Adler grades with CDFI and SMI was significant (Z=-9.722, P<0.001). The differences in vascular morphology between benign and malignant breast lesions detected with CDFI and SMI were statistically significant (both P<0.001). The benign lesions mostly presented non-vascular, linear or curvilinear and tree-like, whereas enlarged and twisted penetrating and radial vessels were found more common in malignant lesions. Conclusion SMI can increase blood flow detection of breast lesions. SMI Adler grading of blood flow and vascular morphology are helpful to identifying benign and malignant breast lesions.

Full text: Available Index: WPRIM (Western Pacific) Type of study: Diagnostic study Language: Chinese Journal: Chinese Journal of Medical Imaging Technology Year: 2019 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Type of study: Diagnostic study Language: Chinese Journal: Chinese Journal of Medical Imaging Technology Year: 2019 Type: Article