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Diagnostic Performance of Breast Magnetic Resonance Imaging in Non-Calcified Equivocal Breast Findings: Results from a Systematic Review and Meta-Analysis

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  • Barbara Bennani-Baiti
  • Nabila Bennani-Baiti
  • Pascal A Baltzer

Abstract

Objectives: To evaluate the performance of MRI for diagnosis of breast cancer in non-calcified equivocal breast findings. Materials and Methods: We performed a systematic review and meta-analysis of peer-reviewed studies in PubMed from 01/01/1986 until 06/15/2015. Eligible were studies applying dynamic contrast-enhanced breast MRI as an adjunct to conventional imaging (mammography, ultrasound) to clarify equivocal findings without microcalcifications. Reference standard for MRI findings had to be established by histopathological sampling or imaging follow-up of at least 12 months. Number of true or false positives and negatives and other characteristics were extracted, and possible bias was determined using the QUADAS-2 applet. Statistical analyses included data pooling and heterogeneity testing. Results: Fourteen out of 514 studies comprising 2,316 lesions met our inclusion criteria. Pooled diagnostic parameters were: sensitivity (99%, 95%-CI: 93–100%), specificity (89%, 95%-CI: 85–92%), PPV (56%, 95%-CI: 42–70%) and NPV (100%, 95%-CI: 99–100%). These estimates displayed significant heterogeneity (P

Suggested Citation

  • Barbara Bennani-Baiti & Nabila Bennani-Baiti & Pascal A Baltzer, 2016. "Diagnostic Performance of Breast Magnetic Resonance Imaging in Non-Calcified Equivocal Breast Findings: Results from a Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-16, August.
    • Handle: RePEc:plo:pone00:0160346
    DOI: 10.1371/journal.pone.0160346
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    Cited by:

    1. Giulia Bicchierai & Federica Di Naro & Diego De Benedetto & Diletta Cozzi & Silvia Pradella & Vittorio Miele & Jacopo Nori, 2021. "A Review of Breast Imaging for Timely Diagnosis of Disease," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
    2. Stephan Ellmann & Evelyn Wenkel & Matthias Dietzel & Christian Bielowski & Sulaiman Vesal & Andreas Maier & Matthias Hammon & Rolf Janka & Peter A Fasching & Matthias W Beckmann & Rüdiger Schulz Wendt, 2020. "Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-15, January.
    3. M Wielema & M D Dorrius & R M Pijnappel & G H De Bock & P A T Baltzer & M Oudkerk & P E Sijens, 2020. "Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-23, May.

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