Detection of Breast Lesions by Automatic Breast US
Study Details
Study Description
Brief Summary
Mammography is considered the standard imaging method for breast cancer screening, and is known to result in reduced mortality from breast cancer. However, the diagnostic yield of mammography depends particularly on breast tissue density, with sensitivity as low as 30-48% in dense-breast women. Ultrasound is an attractive adjunct imaging method to mammography as it is widely accessible, relatively inexpensive and well-tolerated by patients. The addition of hand-held ultrasound to mammography has been demonstrated to significantly increase breast cancer detection in women with dense breasts. It is however dependent on the expertise and skill of the operator.
In recent years, the FDA has approved the use of the automated breast ultrasound (ABUS) for use in screening of women with dense breast. Unlike handheld ultrasound, the ABUS is relatively simple to use, necessitating less technical training, and results in higher reproducibility.
The research aim is to evaluation of automated breast ultrasound (ABUS) regarding the detection and classification of breast lesions, compared to hand-held ultrasound, according to the American College of Radiology Breast Imaging-Reporting and Data System (BI-RADS) classification. The investigator will also evaluate parameters regarding patients' comfort, workflow, and duration of image interpretation.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
|
Detailed Description
ABUS produces a 3D volume acquisition using a 6-15 Mega Herz reverse curve transducer. Images are digitally reformatted on a dedicated workstation to produce axial, sagittal and coronal reformatted images. Unlike handheld ultrasound, the ABUS is relatively simple to use, necessitating less technical training, and results in higher reproducibility. Previous studies have shown that automated breast ultrasound is very well tolerated by patients can be useful in detection of solid and cystic lesions and in evaluating tumor extent preoperatively.
Automated breast ultrasound will be performed on the "Invenia ABUS" (Automated Breast Ultrasound System) designed for automated breast imaging by General Electric (GE) Healthcare. Images will be acquired using a 15 centimeter field-of-view reverse curve ultra-broadband transducer of 6-15 Mega Herz. Using mechanical compression assist, the transducer is placed on each breast and six volumes are acquired with six sweeps (right anterior-posterior, right lateral, right medial; left anterior-posterior, left lateral, left medial). Acquisition time expected 15 minutes per patient, approximately 30-40 second acquisition per volume.
ABUS examinations will be performed by the investigators institution's radiographers, with variable degrees of experience in performing hand-held breast ultrasound examinations (HHUS).
Reformatted images and volumes will be view on a designated workstation of 2 megapixel high resolution monitor, using customized hanging protocols, multi-slice 3D viewing and patented clinical algorithms. Interpretation of images will be done by our institution's breast imaging radiologists with more than 15 years of experience in performing and reading hand-held breast ultrasound studies.
Each breast will be assigned a final ABUS BI-RADS score according to the American College of Radiology classification ranging from 1 to 6. A discrepancy between the ABUS Breast Imaging-Reporting and Data System score and the hand-held ultrasound BI-RADS score (HHUS BI-RADS 1-2 & ABUS BI-RADS >2, or HHUS BI-RADS >3 & ABUS BI-RADS 1-2) will result in the referral of the woman to second-look hand-held ultrasound to determine the reason for the mismatch.
Study Design
Outcome Measures
Primary Outcome Measures
- ability to identify all US breast findings correctly. [6 month]
The investigators will compare all lesions detected by abus and hand held US to pathology when present .
Eligibility Criteria
Criteria
Inclusion Criteria:
Women referred to hand-held screening ultrasound examination (BI-RADS=1/2). Women scheduled to undergo ultrasound-guided needle biopsy due to suspicious breast mass detected during hand-held ultrasound (BI-RADS>2).
Exclusion Criteria:
Women under 25 years.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Assuta Medical Centers | Tel Aviv | Israel |
Sponsors and Collaborators
- Assuta Medical Center
Investigators
- Principal Investigator: Yuliana Weinstein, MD, Assuta Medical Centers
Study Documents (Full-Text)
None provided.More Information
Publications
- ACR BI-RADS Atlas 5th Edition, Breast Imaging Reporting and Data System 2013
- Brem RF, Lenihan MJ, Lieberman J, Torrente J. Screening breast ultrasound: past, present, and future. AJR Am J Roentgenol. 2015 Feb;204(2):234-40. doi: 10.2214/AJR.13.12072. Review.
- Chang JM, Moon WK, Cho N, Park JS, Kim SJ. Breast cancers initially detected by hand-held ultrasound: detection performance of radiologists using automated breast ultrasound data. Acta Radiol. 2011 Feb 1;52(1):8-14. doi: 10.1258/ar.2010.100179.
- Drukker K, Horsch KJ, Pesce LL, Giger ML. Interreader scoring variability in an observer study using dual-modality imaging for breast cancer detection in women with dense breasts. Acad Radiol. 2013 Jul;20(7):847-53. doi: 10.1016/j.acra.2013.02.007. Epub 2013 Apr 17.
- Kelly KM, Dean J, Comulada WS, Lee SJ. Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts. Eur Radiol. 2010 Mar;20(3):734-42. doi: 10.1007/s00330-009-1588-y. Epub 2009 Sep 2.
- Lander MR, Tabár L. Automated 3-D breast ultrasound as a promising adjunctive screening tool for examining dense breast tissue. Semin Roentgenol. 2011 Oct;46(4):302-8. doi: 10.1053/j.ro.2011.06.003. Review.
- Prosch H, Halbwachs C, Strobl C, Reisner LM, Hondl M, Weber M, Mostbeck GH. [Automated breast ultrasound vs. handheld ultrasound: BI-RADS classification, duration of the examination and patient comfort]. Ultraschall Med. 2011 Oct;32(5):504-10. doi: 10.1055/s-0031-1273414. Epub 2011 May 31. German.
- Skaane P, Gullien R, Eben EB, Sandhaug M, Schulz-Wendtland R, Stoeblen F. Interpretation of automated breast ultrasound (ABUS) with and without knowledge of mammography: a reader performance study. Acta Radiol. 2015 Apr;56(4):404-12. doi: 10.1177/0284185114528835. Epub 2014 Mar 28.
- Tozaki M, Fukuma E. Accuracy of determining preoperative cancer extent measured by automated breast ultrasonography. Jpn J Radiol. 2010 Dec;28(10):771-3. doi: 10.1007/s11604-010-0499-9. Epub 2010 Dec 30.
- Wenkel E, Heckmann M, Heinrich M, Schwab SA, Uder M, Schulz-Wendtland R, Bautz WA, Janka R. Automated breast ultrasound: lesion detection and BI-RADS classification--a pilot study. Rofo. 2008 Sep;180(9):804-8. doi: 10.1055/s-2008-1027563. Epub 2008 Aug 14.
- Zintsmaster BS, Morrison J, Sharman S, Shah BA. Differences in pain perceptions between automated breast ultrasound and digital screening mammography. J Diag Med Sonography 2013;29(2):62-65.
- 0089-16ASMC