Detection of Breast Lesions by Automatic Breast US

NCT ID: NCT03047122

Last Updated: 2017-03-21

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 1200 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2017-03-01
• Study Completion Date: 2018-12-31

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.

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.

Conditions

Ultrasonography; Breast

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: PROSPECTIVE

Eligibility 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.

• Minimum Eligible Age: 25 Years
• Maximum Eligible Age: 99 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

Assuta Medical Center

OTHER

Sponsor Role: lead

Responsible Party

Michal Guindy

Medical Director of Imaging Services

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Yuliana Weinstein, MD

Role: PRINCIPAL_INVESTIGATOR

Assuta Medical Centers

Locations

Assuta Medical Centers

Tel Aviv, , Israel

Site Status: RECRUITING

Countries

Israel

Central Contacts

Michal Guindy, MD

Role: CONTACT

micahlgu@assuta.co.il

+972-50-8800102

Facility Contacts

Yuliana Weinstein, MD

Role: primary

yulianawe@assuta.co.il

972-52-4262514

Michal Guindy, MD

Role: backup

michalgu@assuta.co.il

972-50-8800102

References

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Reference Type: BACKGROUND

PMID: 22035673 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 19727744 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 25615743 (View on PubMed)

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.

Reference Type: BACKGROUND

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.

Reference Type: BACKGROUND

PMID: 18704878 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 21191745 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 24682405 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 21498319 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 23601952 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 21630181 (View on PubMed)

ACR BI-RADS Atlas 5th Edition, Breast Imaging Reporting and Data System 2013

Reference Type: BACKGROUND

Study Documents

Document Type: U.S. Food and Drug Administration. somo-v Automated Breast Ultrasound System

View Document

Other Identifiers

0089-16ASMC

Identifier Type: -

Identifier Source: org_study_id