TY - JOUR
T1 - Breast vibro-acoustography
T2 - Initial experience in benign lesions
AU - Alizad, Azra
AU - Mehrmohammadi, Mohammad
AU - Ghosh, Karthik
AU - Glazebrook, Katrina N.
AU - Carter, Rickey E.
AU - Karaberkmez, Leman Gunbery
AU - Whaley, Dana H.
AU - Fatemi, Mostafa
N1 - Funding Information:
This study was supported by BCTR0504550 from Susan G. Komen for the Cure, grants R21CA121579, R01CA148994, and R01CA127235 from the National Institute of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health or Susan G Komen for the Cure. The authors are grateful to Dr. Matthew Urban and Dr. James Greenleaf for their helpful scientific discussions, to Mr. Randall Kinnick for technical support, to Mr. Thomas Kinter for computer support, and to Ms. Jennifer Milliken for administrative support. Disclosure of conflict of interest: Mayo Clinic and some of authors have financial interests associated with the technology used in this research; the technology has been licensed in part to industry.
Publisher Copyright:
© 2014 Alizad et al.; licensee BioMed Central.
PY - 2014/12/30
Y1 - 2014/12/30
N2 - Background: Vibro-acoustography (VA) is a newly developed imaging technology that is based on low-frequency vibrations induced in the object by the radiation force of ultrasound. VA is sensitive to the dynamic characteristics of tissue. Here, we evaluate the performance of VA in identifying benign lesions and compare the results to those of mammography. Methods: An integrated mammography-VA system designed for in vivo breast imaging was tested on a group of female volunteers, age = 18 years, with suspected breast lesions based on clinical examination. A set of VA scans was acquired after each corresponding mammography. Most lesions were classified as benign based on their histological results. However, in 4 cases, initial diagnosis based on clinical imaging determined that the lesions were cysts. These cysts were aspirated with needle aspiration and disappeared completely under direct ultrasound visualization. Therefore, no biopsies were performed on these cases and lesions were classified as benign based on clinical findings per clinical standards. To define the VA characteristics of benign breast masses, we adopted the features that are normally attributed to such masses in mammography. In a blinded assessment, three radiologists evaluated the VA images independently. The diagnostic accuracy of VA for detection of benign lesions was assessed by comparing the reviewers' evaluations with clinical data. Results: Out of a total 29 benign lesions in the group, the reviewers were able to locate all lesions on VA images and mammography, 100% with (95% confidence interval (CI): 88% to 100%). Two reviewers were also able to correctly classify 83% (95% CI: 65% to 92%), and the third reviewer 86% (95% CI: 65% to 95%) of lesions, as benign on VA images and 86% (95% CI: 69% to 95%) on mammography. Conclusions: The results suggest that the mammographic characteristics of benign lesion may also be used to identify such lesions in VA. Furthermore, the results show the ability of VA to detect benign breast abnormalities with a performance comparable to mammography. Therefore, the VA technology has the potential to be utilized as a complementary tool for breast imaging applications. Additional studies are needed to compare the capabilities of VA and traditional ultrasound imaging.
AB - Background: Vibro-acoustography (VA) is a newly developed imaging technology that is based on low-frequency vibrations induced in the object by the radiation force of ultrasound. VA is sensitive to the dynamic characteristics of tissue. Here, we evaluate the performance of VA in identifying benign lesions and compare the results to those of mammography. Methods: An integrated mammography-VA system designed for in vivo breast imaging was tested on a group of female volunteers, age = 18 years, with suspected breast lesions based on clinical examination. A set of VA scans was acquired after each corresponding mammography. Most lesions were classified as benign based on their histological results. However, in 4 cases, initial diagnosis based on clinical imaging determined that the lesions were cysts. These cysts were aspirated with needle aspiration and disappeared completely under direct ultrasound visualization. Therefore, no biopsies were performed on these cases and lesions were classified as benign based on clinical findings per clinical standards. To define the VA characteristics of benign breast masses, we adopted the features that are normally attributed to such masses in mammography. In a blinded assessment, three radiologists evaluated the VA images independently. The diagnostic accuracy of VA for detection of benign lesions was assessed by comparing the reviewers' evaluations with clinical data. Results: Out of a total 29 benign lesions in the group, the reviewers were able to locate all lesions on VA images and mammography, 100% with (95% confidence interval (CI): 88% to 100%). Two reviewers were also able to correctly classify 83% (95% CI: 65% to 92%), and the third reviewer 86% (95% CI: 65% to 95%) of lesions, as benign on VA images and 86% (95% CI: 69% to 95%) on mammography. Conclusions: The results suggest that the mammographic characteristics of benign lesion may also be used to identify such lesions in VA. Furthermore, the results show the ability of VA to detect benign breast abnormalities with a performance comparable to mammography. Therefore, the VA technology has the potential to be utilized as a complementary tool for breast imaging applications. Additional studies are needed to compare the capabilities of VA and traditional ultrasound imaging.
KW - Benign breast lesion
KW - Breast neoplasms
KW - Breast ultrasonography
KW - Mammography
KW - Vibro-acoustography
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U2 - 10.1186/s12880-014-0040-1
DO - 10.1186/s12880-014-0040-1
M3 - Article
C2 - 25547172
AN - SCOPUS:84924069149
SN - 1471-2342
VL - 14
JO - BMC Medical Imaging
JF - BMC Medical Imaging
IS - 1
M1 - 40
ER -