Advertisement

Comparing two visualization protocols for tomosynthesis in screening: specificity and sensitivity of slabs versus planes plus slabs

  • Valentina IottiEmail author
  • Paolo Giorgi Rossi
  • Andrea Nitrosi
  • Sara Ravaioli
  • Rita Vacondio
  • Cinzia Campari
  • Vanessa Marchesi
  • Moira Ragazzi
  • Marco Bertolini
  • Giulia Besutti
  • Carlo Alberto Mori
  • Pierpaolo Pattacini
  • the RETomo Working Group
Breast
  • 10 Downloads

Abstract

Objectives

Tomosynthesis (DBT) has proven to be more sensitive than digital mammography, but it requires longer reading time. We retrospectively compared accuracy and reading times of a simplified protocol with 1-cm-thick slabs versus a standard protocol of slabs + 1-mm-spaced planes, both integrated with synthetic 2D.

Methods

We randomly selected 894 DBTs (including 12 cancers) from the experimental arm of the RETomo trial. DBTs were read by two radiologists to estimate specificity. A second set of 24 cancers (8 also present in the first set) mixed within 276 negative DBTs was read by two radiologists. In total, 28 cancers with 64 readings were used to estimate sensitivity. Radiologists read with both protocols separated by a 3-month washout. Only women that were positive at the screening reading were assessed. Variance was estimated taking into account repeated measures.

Results

Sensitivity was 82.8% (53/64, 95% confidence interval (95% CI) 67.2–92.2) and 90.6% (95% CI 80.2–95.8) with simplified and standard protocols, respectively. In the random screening setting, specificity was 97.9% (1727/1764, 95% CI 97.1–98.5) and 96.3% (95% CI 95.3–97.1), respectively. Inter-reader agreement was 0.68 and 0.54 with simplified and standard protocols, respectively. Median reading times with simplified protocol were 20% to 30% shorter than with standard protocol.

Conclusions

A simplified protocol reduced reading time and false positives but may have a negative impact on sensitivity.

Key Points

• The adoption of digital breast tomosynthesis (DBT) in screening, more sensitive than mammography, could be limited by its potential effect on the radiologists’ workload, i.e., increased reading time and fatigue.

• A DBT simplified protocol with slab only, compared to a standard protocol (slab plus planes) both integrated with synthetic 2D, reduced time and false positives but had a negative impact on sensitivity.

Keywords

Breast neoplasms Mass screening Mammography Workflow Sensitivity and specificity 

Abbreviations

ASMN

Arcispedale Santa Maria Nuova

AUSL

Azienda Unità Sanitaria Locale

BIRADS

Breast imaging-reporting and data system

CAD

Computer-aided detection

CAM

Initials of reader 1

CC

Craniocaudal

CI

Confidence interval

DBT

Digital breast tomosynthesis

DCIS

Ductal carcinoma in situ

DM

Digital mammography

FDA

Food and Drug Administration

IQR

Interquartile ranges

IRCCS

Istituto di Ricovero e Cura a Carattere Scientifico (Research Hospital)

MLO

Mediolateral oblique

PACS

Picture archiving and communication system

RV

Initials of reader 2

SR

Initials of reader 3

Sy-2D

Synthetic 2D

Notes

Acknowledgments

Many thanks to all the personnel for their committed work during data collection and to all women who participated in the study for their fundamental contribution. We want to sincerely thank Carlo Alberto Mori, MD, for his precious support, devoted work for this study, and inestimable experience in breast diagnosis. The following are also members of the RETomo working group. Screening readers and post-recall assessment: Coriani C, MD; Pescarolo M, MD; Stefanelli G, MD; Tondelli G, MD; Beretti F, MD; Caffarri S, MD. Screening Coordinating Center: Paterlini L, MD. Radiographers Coordinator: Canovi L, Colli M, Boschini M. Scientific direction: Cavuto S; Braglia L. We thank Jacqueline Costa for editing the text. The results of this study were presented orally at a scientific session at the RSNA Annual Meeting in Chicago 2017 and at the ECR in Vienna 2018.

Funding

The study has been partially funded by the Regione Emilia-Romagna (Public Health System) and sustained by the institutional funds of the Reggio Emilia Local Health Authority (AUSL)-IRCCS.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Paolo Giorgi Rossi, PhD.

Conflict of interest

VI, AN, RV, and PP have received speakers’ fees and travel grants from GE Healthcare. CAM received financial support from GE Healthcare to allow the conclusion of the study after his retiring.

PGR, SR, CC, VM, MR, and MB disclosed no relevant relationships.

Statistics and biometry

One of the authors (Paolo Giorgi Rossi, PhD) has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

References

  1. 1.
    European Commission Initiative on Breast Cancer. Recommendations on breast cancer screening. Available via http://ecibc.jrc.ec.europa.eu/recommendations/list/3 Access 24 May 2017
  2. 2.
    Oeffinger KC, Fontham ET, Etzioni R et al (2015) Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society. JAMA 314(15):1599–1614CrossRefGoogle Scholar
  3. 3.
    Siu AL, U.S. Preventive Services Task Force (2016) Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 164:279–296CrossRefGoogle Scholar
  4. 4.
    Perry N, Broeders M, de Wolf C, Törnberg S, Holland R, von Karsa L (2008) European guidelines for quality assurance in breast cancer screening and diagnosis. Fourth edition--summary document. Ann Oncol 19(4):614–622Google Scholar
  5. 5.
    Council of the European Union (2003) Council recommendation of 2 December 2003 on cancer screening (2003/878/EC). OJ L 327, Office for Official Publications of the European Communities; 34–38Google Scholar
  6. 6.
    Dibden A, Offman J, Parmar D et al (2014) Reduction in interval cancer rates following the introduction of two-view mammography in the UK breast screening programme. Br J Cancer 110:560–564CrossRefGoogle Scholar
  7. 7.
    Mandelson MT, Oestreicher N, Porter PL et al (2000) Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers. J Natl Cancer Inst 92(13):1081–1087CrossRefGoogle Scholar
  8. 8.
    Vedantham S, Karellas A, Vijayaraghavan GR, Kopans DB (2015) Digital breast tomosynthesis: state of the art. Radiology 277:663–684Google Scholar
  9. 9.
    Ciatto S, Houssami N, Bernardi D et al (2013) Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study. Lancet Oncol 14:583–589CrossRefGoogle Scholar
  10. 10.
    Skaane P, Bandos AI, Gullien R et al (2013) Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. Radiology 267(1):47–56CrossRefGoogle Scholar
  11. 11.
    Skaane P, Bandos AI, Gullien R et al (2013) Prospective trial comparing full-field digital mammography (FFDM) versus combined FFDM and tomosynthesis in a population-based screening programme using independent double reading with arbitration. Eur Radiol 23:2061–2071CrossRefGoogle Scholar
  12. 12.
    Lång K, Andersson I, Rosso A, Tingberg A, Timberg P, Zackrisson S (2016) Performance of one-view breast tomosynthesis as a stand-alone breast cancer screening modality: results from the Malmo Breast Tomosynthesis Screening Trial, a population-based study. Eur Radiol 26:184–190Google Scholar
  13. 13.
    Gilbert FJ, Tucker L, Gillan MG et al (2015) Accuracy of digital breast tomosynthesis for depicting breast cancer subgroups in a UK retrospective reading study (TOMMY Trial). Radiology 277(3):697–706.  https://doi.org/10.1148/radiol.2015142566 CrossRefPubMedGoogle Scholar
  14. 14.
    Bernardi D, Macaskill P, Pellegrini M et al (2016) Breast cancer screening with tomosynthesis (3D mammography) with acquired or synthetic 2D mammography compared with 2D mammography alone (STORM-2): a population-based prospective study. Lancet Oncol 17(8):1105–1113CrossRefGoogle Scholar
  15. 15.
    US Food and Drug Administration. Premarket approval. SENOCLAIRE. Available via http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMA/pma.cfm?id=P130020S001 Access 24 May 2017
  16. 16.
    McDonald ES, Oustimov A, Weinstein SP, Synnestvedt MB, Schnall M, Conant EF (2016) Effectiveness of digital breast tomosynthesis compared with digital mammography: outcomes analysis from 3 years of breast cancer screening. JAMA Oncol 2:737–743Google Scholar
  17. 17.
    Caumo F, Zorzi M, Brunelli S et al (2017) Digital breast tomosynthesis with synthesized two-dimensional images versus full-field digital mammography for population screening: outcomes from the Verona screening program. Radiology 170745.  https://doi.org/10.1148/radiol.2017170745
  18. 18.
    Houssami N (2018) Evidence on synthesized two-dimensional mammography versus digital mammography when using tomosynthesis (three-dimensional mammography) for population breast cancer screening. Clin Breast Cancer 18(4):255–260.e1.  https://doi.org/10.1016/j.clbc.2017.09.012 CrossRefPubMedGoogle Scholar
  19. 19.
    Morra L, Sacchetto D, Durando M et al (2015) Breast cancer: computer-aided detection with digital breast tomosynthesis. Radiology 277(1):56–63CrossRefGoogle Scholar
  20. 20.
    Pattacini P, Nitrosi A, Giorgi Rossi P et al (2018) Digital mammography versus digital mammography plus tomosynthesis for breast cancer screening: the Reggio Emilia Tomosynthesis randomized Trial. Radiology 288(2):375–385Google Scholar
  21. 21.
    Bernardi D, Ciatto S, Pellegrini M et al (2012) Application of breast tomosynthesis in screening: incremental effect on mammography acquisition and reading time. Br J Radiol 85:e1174–e1178CrossRefGoogle Scholar
  22. 22.
    Dang PA, Freer PE, Humphrey KL, Halpern EF, Rafferty EA (2014) Addition of tomosynthesis to conventional digital mammography: effect on image interpretation time of screening examinations. Radiology 270:49–56Google Scholar
  23. 23.
    Campari C, Giorgi Rossi P, Mori CA et al (2016) Impact of the introduction of digital mammography in an organized screening program on the recall and detection rate. J Digit Imaging 29(2):235–242CrossRefGoogle Scholar
  24. 24.
    Tang ML, Tang NS, Chan IS, Chan BP (2002) Sample size determination for establishing equivalence/noninferiority via ratio of two proportions in matched-pair design. Biometrics 58(4):957–963Google Scholar
  25. 25.
    Meijer CJ, Berkhof J, Castle PE et al (2009) Guidelines for human papillomavirus DNA test requirements for primary cervical cancer screening in women 30 years and older. Int J Cancer 124(3):516–520.  https://doi.org/10.1002/ijc.24010 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Eliasziw M, Donner A (1991) Application of the McNemar test to non-independent matched pair data. Stat Med 10:1981–1991CrossRefGoogle Scholar
  27. 27.
    Wolter KM (2007) Introduction to variance estimation, 2nd edn. Springer, New YorkGoogle Scholar
  28. 28.
    Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46CrossRefGoogle Scholar
  29. 29.
    StataCorp (2013) Stata: Release 13. Statistical Software. StataCorp LP, College StationGoogle Scholar
  30. 30.
    Galati F, Marzocca F, Bassetti E et al (2017) Added value of digital breast tomosynthesis combined with digital mammography according to reader agreement: changes in BI-RADS rate and follow-up management. Breast Care (Basel) 12(4):218–222.  https://doi.org/10.1159/000477537 CrossRefGoogle Scholar
  31. 31.
    Choi WJ, Kim HH, Lee SY et al (2016) A comparison between digital breast tomosynthesis and full-field digital mammography for the detection of breast cancers. Breast Cancer 23(6):886–892CrossRefGoogle Scholar
  32. 32.
    Carbonaro LA, Di Leo G, Clauser P et al (2016) Impact on the recall rate of digital breast tomosynthesis as an adjunct to digital mammography in the screening setting. A double reading experience and review of the literature. Eur J Radiol 85(4):808–814.  https://doi.org/10.1016/j.ejrad.2016.01.004. CrossRefPubMedGoogle Scholar
  33. 33.
    van Schie G, Wallis MG, Leifland K, Danielsson M, Karssemeijer N (2013) Mass detection in reconstructed digital breast tomosynthesis volumes with a computer-aided detection system trained on 2D mammograms. Med Phys 40(4):041902Google Scholar
  34. 34.
    Samala RK, Chan HP, Hadjiiski L, Helvie MA, Wei J, Cha K (2016) Mass detection in digital breast tomosynthesis: deep convolutional neural network with transfer learning from mammography. Med Phys 43(12):6654Google Scholar
  35. 35.
    Balleyguier C, Arfi-Rouche J, Levy L et al (2017) Improving digital breast tomosynthesis reading time: a pilot multi-reader, multi-case study using concurrent computer-aided detection (CAD). Eur J Radiol 97:83–89CrossRefGoogle Scholar
  36. 36.
    Benedikt RA, Boatsman JE, Swann CA, Kirkpatrick AD, Toledano AY (2017) Concurrent computer-aided detection improves reading time of digital breast tomosynthesis and maintains interpretation performance in a multireader multicase study. AJR Am J Roentgenol 24:1–10Google Scholar
  37. 37.
    Dustler M, Andersson M, Fornvik D, Timberg P, Timberg A (2013) A study of the feasibility of using slabbing to reduce tomosynthesis review time. Proc. SPIE 8673 Medical Imaging 86731LGoogle Scholar
  38. 38.
    Friedewald SM (2017) Breast tomosynthesis: practical considerations. Radiol Clin North Am 55(3):493–502.  https://doi.org/10.1016/j.rcl.2016.12.004 CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Valentina Iotti
    • 1
    Email author
  • Paolo Giorgi Rossi
    • 2
  • Andrea Nitrosi
    • 3
  • Sara Ravaioli
    • 1
  • Rita Vacondio
    • 1
  • Cinzia Campari
    • 4
  • Vanessa Marchesi
    • 1
  • Moira Ragazzi
    • 5
  • Marco Bertolini
    • 3
  • Giulia Besutti
    • 1
    • 6
  • Carlo Alberto Mori
    • 1
  • Pierpaolo Pattacini
    • 1
  • the RETomo Working Group
  1. 1.Radiology UnitAUSL Reggio Emilia, IRCCSReggio EmiliaItaly
  2. 2.Epidemiology UnitAUSL Reggio Emilia, IRCCSReggio EmiliaItaly
  3. 3.Medical Physics UnitAUSL Reggio Emilia, IRCCSReggio EmiliaItaly
  4. 4.Screening Coordinating UnitAUSL Reggio Emilia, IRCCSReggio EmiliaItaly
  5. 5.Pathology UnitAUSL Reggio Emilia, IRCCSReggio EmiliaItaly
  6. 6.Clinical and Experimental Medicine PhD ProgramUniversity of Modena and Reggio EmiliaModenaItaly

Personalised recommendations