Interpretation of automated breast ultrasound (ABUS) with and without knowledge of mammography: a reader performance study

Abstract

Background

Automated breast ultrasonography (ABUS) has the potential to be an important adjunct to mammography in women with dense breasts.

Purpose

To compare reader performance and inter-observer variation of ABUS alone and in combination with mammography.

Material and Methods

This retrospective study had ethical committee approval. All women gave written informed consent. One hundred and fourteen breasts in 90 women examined by digital mammography and ABUS were interpreted by five radiologists using BI-RADS categories. The 114 breasts included 38 cancers and 76 normal or benign findings. In the first reading session ABUS only was interpreted, and in the second ABUS plus digital mammography. Image interpretations were done without knowledge of clinical or imaging results. A consensus panel analyzed false negative and false positive interpretations. Reading time was recorded for one radiologist. AUC was used for performance measurement, and kappa statistic for inter-observer variability.

Results

Mean size for cancers was 16.2 mm; area under the curve (AUC) values for ABUS alone and for combined reading were, respectively: reader A, 0.592–0.744; reader B, 0.740–0.947; reader, C 0.759–0.823; reader D, 0.670–0.688; reader E, 0.904–0.923; and all readers combined 0.730–0.823. The higher AUC for combined reading was statistically significant (P < 0.05) for reader B and for all readers. There was a considerable inter-observer variability. Observer agreement revealed following kappa values for ABUS alone and combined reading, respectively: reader A, 0.22–0.30; reader B, 0.33–0.44; reader C, 0.32–0.39; reader D, 0.07–0.14; and reader E, 0.34–0.43. Shadowing from dense parenchyma was the most common cause of false positive ABUS interpretations. Mean interpretation time for a bilateral normal ABUS examination was 9 min.

Conclusion

Observer agreement was higher and all radiologists improved diagnostic performance using combined ABUS and mammography interpretation. Combined reading should be standard if ABUS is implemented in screening of women with dense breasts.

Keywords

Automated breast ultrasonography hand-held ultrasound breast cancer screening mammography inter-observer variability

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References

Tabar

Vitak

Chen

THH

. Swedish two-county trial: Impact of mammographic screening on breast cancer mortality during 3 decades. Radiology 2011; 260: 658–663.

Hofvind

Ursin

Tretli

. Breast cancer mortality in participants of the Norwegian breast cancer screening program. Cancer 2013; 119: 3106–3112.

Kuhl

Schrading

Leutner

. Mammography, breast ultrasound, and magnetic resonance imaging for surveillance of women at high familial risk for breast cancer. J Clin Oncol 2005; 23: 8469–8476.

Berg

Blume

Cormack

. Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 2008; 299: 2151–2163.

Buchberger

Niehoff

Obrist

. Clinically and mammographically occult breast lesions: Detection and classification with high-resolution sonography. Semin Ultrasound CT MR 2000; 21: 325–336.

Corsetti

Houssami

Ferrari

. Breast screening with ultrasound in women with mammography-negative dense breasts: Evidence on incremental cancer detection and false positives, and associated cost. Eur J Cancer 2008; 44: 539–544.

Crystal

Strano

Shcharynski

. Using sonography to screen women with mammographically dense breasts. Am J Roentgenol 2003; 181: 177–182.

Kaplan

. Clinical utility of bilateral whole-breast US in the evaluation of women with dense breast tissue. Radiology 2001; 221: 641–649.

Kolb

Lichy

Newhouse

. Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: An analysis of 27,825 patient evaluations. Radiology 2002; 225: 165–175.

10.

Nothacker

Duda

Hahn

. Early detection of breast cancer: benefits and risks of supplemental breast ultrasound in asymptomatic women with mammographically dense breast tissue. A systematic review. BMC Cancer 2009; 9: 335–335.

11.

Fischer

Korthauer

Baum

. Short first-pass MRI of the breast. Acta Radiol 2012; 53: 267–269.

12.

Chang

Moon

Cho

. Breast cancers initially detected by hand-held ultrasound: detection performance of radiologists using automated breast ultrasound data. Acta Radiol 2011; 52: 8–14.

13.

Skaane

Engedal

Skjennald

. Interobserver variation in the interpretation of breast imaging: Comparison of mammography, ultrasonography, and both combined in the interpretation of palpable noncalcified breast masses. Acta Radiol 1997; 38: 497–502.

14.

Hooley

Greenberg

Stackhouse

. Screening US in patients with mammographically dense breasts: Initial experience with Connecticut Public Act 09-41. Radiology 2012; 265: 59–69.

15.

Schaefer

FKW

Waldmann

Katalinic

. Influence of additional breast ultrasound on cancer detection in a cohort study for quality assurance in breast diagnosis – analysis of 102,577 diagnostic procedures. Eur Radiol 2010; 20: 1085–1092.

16.

Kelly

Dean

Comulada

. Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts. Eur Radiol 2010; 20: 734–742.

17.

Oberaigner

Buchberger

Frede

. Introduction of organised mammography screening in Tyrol: results of a one-year pilot phase. BMC Public Health 2011; 11: 91–91.

18.

Parris

Wakefield

Frimmer

. Real world performance of screening breast ultrasound following enactment of Connecticut Bill 458. Breast J 2013; 19: 64–70.

19.

Skaane

Engedal

. Analysis of sonographic features in the differentiation of fibroadenoma and invasive ductal carcinoma. Am J Roentgenol 1998; 170: 109–114.

20.

Stavros

Thickman

Rapp

. Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. Radiology 1995; 196: 123–134.

21.

Ciatto

Bernardi

Calabrese

. A first evaluation of breast density assessment by Quantra software as compared to visual classification. The Breast 2012; 21: 503–506.

22.

Zhang

Lai

X-J

Zhu

Q-L

. Interobserver agreement for sonograms of breast lesions obtained by an automated breast volume scanner. Eur J Radiol 2012; 81: 2179–2183.

23.

Wang

H-Y

Jiang

Y-X

Zhu

Q-L

. Differentiation of benign and malignant breast lesions: A comparison between automatically generated breast volume scans and handheld ultrasound examinations. Eur J Radiol 2012; 81: 3190–3200.

24.

Kim

Kang

Choi

. Radiologists’ performance for detecting lesions and the interobserver variability of automated whole breast ultrasound. Korean J Radiol 2013; 14: 154–163.

25.

Wenkel

Heckmann

Heinrich

. Automated breast ultrasound: Lesion detection and BI-RADS classification – a pilot study. Fortschr Roentgenstr 2008; 180: 804–808.

26.

Wojcinski

Farrokh

Hille

. The automated breast volume scanner (ABVS): initial experiences in lesion detection compared with conventional handheld B-mode ultrasound: a pilot study of 50 cases. Int J Womens Health 2011; 3: 337–346.

27.

Stoblen

Landt

Stelkens-Gebhardt

. First evaluation of the diagnostic accuracy of an automated 3D ultrasound system in a breast screening setting. Anticancer Research 2011; 31: 2569–2574.

28.

Drukker

Horsch

Pesce

. Interreader scoring variability in an observer study using dual-modality imaging for breast cancer detection in women with dense breasts. Acad Radiol 2013; 20: 847–853.

29.

Ciatto

Houssami

Bernardi

. Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study. Lancet Oncol 2013; 14: 583–589.

30.

Skaane

Bandos

Gullien

. Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. Radiology 2013; 267: 47–56.