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Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs

  • Somphone Siviengphanom
    Correspondence
    Address correspondence to: S.S.
    Affiliations
    Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Level 7, Susan Wakil Health Building D18, Sydney, NSW 2006, Australia.
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  • Ziba Gandomkar
    Affiliations
    Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Level 7, Susan Wakil Health Building D18, Sydney, NSW 2006, Australia.
    Search for articles by this author
  • Sarah J. Lewis
    Affiliations
    Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Level 7, Susan Wakil Health Building D18, Sydney, NSW 2006, Australia.
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  • Patrick C. Brennan
    Affiliations
    Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Level 7, Susan Wakil Health Building D18, Sydney, NSW 2006, Australia.
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Published:November 16, 2021DOI:https://doi.org/10.1016/j.acra.2021.09.025

      Rationale and Objectives

      Breast cancer is a highly complex heterogeneous disease. Current validated prognostic factors (e.g., histological grade, lymph node involvement, receptor status, and proliferation index), as well as multigene tests (e.g., Oncotype DX and PAM50) are helpful to describe breast cancer characteristics and predict the chance of recurrence risk and survival. Nevertheless, they are invasive and cannot capture a complete heterogeneity of the entire breast tumor resulting in up to 30% of patients being either over- or under-treated for breast cancer. Furthermore, multigene testings are time consuming and expensive. Radiomics is emerging as a reliable, accurate, non-invasive, and cost-effective approach of using quantitative image features to classify breast cancer characteristics and predict patient outcomes. Several recent radiomics reviews have been conducted in breast cancer, however, specific mammography-based radiomics studies have not been well discussed. This scoping review aims to assess and summarize the current evidence on the potential usefulness of mammography-based (i.e., digital mammography, digital breast tomosynthesis, and contrast-enhanced mammography) radiomics in predicting factors that describe breast cancer characteristics, recurrence, and survival.

      Materials and Methods

      PubMed database and eligible text reference were searched using relevant keywords to identify studies published between 2015 and December 19, 2020. Studies collected were screened and assessed based on the inclusion and exclusion criteria.

      Results

      Eighteen eligible studies were included and organized into three main sections: radiomics predicting breast cancer characteristics, radiomics predicting breast cancer recurrence and survival, and radiomics integrating with clinical data. Majority of publications reported retrospective studies while three studies examined prospective cohorts. Encouraging results were reported, suggesting the potential clinical value of mammography-based radiomics. Further efforts are required to standardize radiomics approaches and catalogue reproducible and relevant mammographic radiomic features. The role of integrating radiomics with other information is discussed.

      Conclusion

      The potential role of mammography-based radiomics appears promising but more efforts are required to further evaluate its reliability as a routine clinical tool.

      Key Words

      Abbreviations:

      AI (artificial intelligence), ALNM (axillary lymph node metastasis), BC (Breast cancer), CC (craniocaudal), CEM (contrast-enhanced mammography), CESM (contrast-enhanced spectral mammography), DBT (digital breast tomosynthesis), DCE-MRI (dynamic contrast-enhanced magnetic resonance imaging), DL (deep learning), DLR (deep learning radiomics), DM (digital mammography), ER (estrogen receptor), FOS (first order statistics), GLCM (gray level co-occurrence matrix), GLRLM (gray level run length matrix), HER2 (human epidermal growth factor receptor 2), HR (hormone receptor), iDFS (invasive disease free survival), ML (machine learning), MLO (mediolateral oblique), MRI (magnetic resonance imaging), PR (progesterone receptor), ROI (region of interest), TILs (tumour infiltrating lymphocytes), TN (triple negative), TNBC (triple negative breast cancer), TP (triple positive), VOI (volume of interest)
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