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Digital breast tomosynthesis for breast cancer detection: a diagnostic test accuracy systematic review and meta-analysis

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Abstract

Objectives

No consensus exists on digital breast tomosynthesis (DBT) utilization for breast cancer detection. We performed a diagnostic test accuracy systematic review and meta-analysis comparing DBT, combined DBT and digital mammography (DM), and DM alone for breast cancer detection in average-risk women.

Methods

MEDLINE and EMBASE were searched until September 2018. Comparative design studies reporting on the diagnostic accuracy of DBT and/or DM for breast cancer detection were included. Demographic, methodologic, and diagnostic accuracy data were extracted. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. Accuracy metrics were pooled using bivariate random-effects meta-analysis. The impact of multiple covariates was assessed using meta-regression. PROSPERO ID: CRD 42018111287.

Results

Thirty-eight studies reporting on 488,099 patients (13,923 with breast cancer) were included. Eleven studies were at low risk of bias. DBT alone, combined DBT and DM, and DM alone demonstrated sensitivities of 88% (95% confidence interval [CI] 83–92), 88% (CI 83–92), and 79% (CI 75–82), as well as specificities of 84% (CI 76–89), 81% (CI 73–88), and 79% (CI 71–85), respectively. The greater sensitivities of DBT alone and combined DBT and DM compared to DM alone were preserved in the combined meta-regression models accounting for other covariates (p = 0.003–0.006). No significant difference in diagnostic accuracy between DBT alone and combined DBT and DM was identified (p = 0.175–0.581).

Conclusions

DBT is more sensitive than DM, while the addition of DM to DBT provides no additional diagnostic benefit. Consideration of these findings in breast cancer imaging guidelines is recommended.

Key Points

• Digital breast tomosynthesis with or without additional digital mammography is more sensitive in detecting breast cancer than digital mammography alone in women at average risk for breast cancer.

• The addition of digital mammography to digital breast tomosynthesis provides no additional diagnostic benefit in detecting breast cancer compared to digital breast tomosynthesis alone.

• The specificity of digital breast tomosynthesis with or without additional digital mammography is no different than digital mammography alone in the detection of breast cancer.

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Abbreviations

BI-RADS:

Breast Imaging Reporting and Data System

CI:

Confidence interval

DBT:

Digital breast tomosynthesis

DM:

Digital mammography

hsROC:

Hierarchical summary receiver operating characteristic

PRISMA:

Preferred Reporting Items for Systematic reviews and Meta-Analysis

QUADAS:

Quality Assessment of Diagnostic Accuracy Studies

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Acknowledgments

We would like to thank Dr. Gordon Guyatt for providing methodological guidance for this study.

Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Radiology, McMaster University, Hamilton, ON, Canada

    Mostafa Alabousi & Nanxi Zha

  2. Department of Medicine, University of Ottawa, Ottawa, ON, Canada

    Jean-Paul Salameh & Anahita Dehmoobad Sharifabadi

  3. Department of Clinical Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada

    Jean-Paul Salameh

  4. The Schulich School of Medicine, Western University, London, ON, Canada

    Lucy Samoilov

  5. The Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada

    Alex Pozdnyakov

  6. Department of Health Research Methods, Evidence, and Impact (HEI), McMaster University, Hamilton, Ontario, Canada

    Behnam Sadeghirad

  7. The Michael G. DeGroote Institute for Pain Research and Care, McMaster University, Hamilton, Ontario, Canada

    Behnam Sadeghirad

  8. Joint Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada

    Vivianne Freitas

  9. Department of Radiology and Epidemiology, University of Ottawa, Ottawa, Canada

    Matthew D. F. McInnes

  10. Ottawa Hospital Research Institute, Clinical Epidemiology Program, Ottawa, Canada

    Matthew D. F. McInnes

  11. Department of Radiology, McMaster University, St. Joseph’s Healthcare, Hamilton, ON, Canada

    Abdullah Alabousi

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  1. Mostafa Alabousi
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Correspondence to Abdullah Alabousi.

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Statistics and biometry

Authors Dr. Behanm Sadeghirad and Dr. Matthew DF McInnes have significant statistical expertise.

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Alabousi, M., Zha, N., Salameh, JP. et al. Digital breast tomosynthesis for breast cancer detection: a diagnostic test accuracy systematic review and meta-analysis. Eur Radiol 30, 2058–2071 (2020). https://doi.org/10.1007/s00330-019-06549-2

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