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A Review of Breast Imaging for Timely Diagnosis of Disease

Author

Listed:
  • Giulia Bicchierai

    (Diagnostic Senology Unit, Azienda Ospedaliero-Universitaria Careggi, 50139 Florence, Italy)

  • Federica Di Naro

    (Diagnostic Senology Unit, Azienda Ospedaliero-Universitaria Careggi, 50139 Florence, Italy)

  • Diego De Benedetto

    (Diagnostic Senology Unit, Azienda Ospedaliero-Universitaria Careggi, 50139 Florence, Italy)

  • Diletta Cozzi

    (Emergency Radiology Department, Azienda Ospedaliero-Universitaria Careggi, 50139 Florence, Italy
    Foundation SIRM, 20122 Milan, Italy)

  • Silvia Pradella

    (Emergency Radiology Department, Azienda Ospedaliero-Universitaria Careggi, 50139 Florence, Italy
    Foundation SIRM, 20122 Milan, Italy)

  • Vittorio Miele

    (Emergency Radiology Department, Azienda Ospedaliero-Universitaria Careggi, 50139 Florence, Italy)

  • Jacopo Nori

    (Diagnostic Senology Unit, Azienda Ospedaliero-Universitaria Careggi, 50139 Florence, Italy)

Abstract

Breast cancer (BC) is the cancer with the highest incidence in women in the world. In this last period, the COVID-19 pandemic has caused in many cases a drastic reduction of routine breast imaging activity due to the combination of various factors. The survival of BC is directly proportional to the earliness of diagnosis, and especially during this period, it is at least fundamental to remember that a diagnostic delay of even just three months could affect BC outcomes. In this article we will review the state of the art of breast imaging, starting from morphological imaging, i.e., mammography, tomosynthesis, ultrasound and magnetic resonance imaging and contrast-enhanced mammography, and their most recent evolutions; and ending with functional images, i.e., magnetic resonance imaging and contrast enhanced mammography.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5509-:d:559153
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    References listed on IDEAS

    as
    1. 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.
    2. Tron Anders Moger & Jayson O. Swanson & Åsne Sørlien Holen & Berit Hanestad & Solveig Hofvind, 2019. "Cost differences between digital tomosynthesis and standard digital mammography in a breast cancer screening programme: results from the To-Be trial in Norway," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(8), pages 1261-1269, November.
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    Cited by:

    1. Abdur Rasool & Chayut Bunterngchit & Luo Tiejian & Md. Ruhul Islam & Qiang Qu & Qingshan Jiang, 2022. "Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis," IJERPH, MDPI, vol. 19(6), pages 1-19, March.

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