Quantifying lesion enhancement on contrast-enhanced mammography: a review of published data

S. L. Savaridas (Lead / Corresponding author), S. L. Tennant

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)
    64 Downloads (Pure)

    Abstract

    Aim: To review and discuss the current published data on FUNCTIONAL DATA DERIVED FROM contrast-enhanced spectral mammography (CESM) for investigation of breast lesions.

    Materials and methods: Literature searches were conducted in MEDLINE and PUBMED. Due to the novel nature of CESM and sparsity of published literature pertaining to associated functional data, the Medical Subject Headings (MeSH) used were intentionally broad.

    Results: After inclusion and exclusion criteria, 23 papers were included; 13 pertained to assessment of intensity or pattern of lesion enhancement, and 10 considered textural analysis for lesion assessment, including those using computer-aided detection (CAD) software. Meta-analysis of data was not possible due to heterogeneity of methodology.

    Conclusions: There is consistent evidence that benign lesions tend to demonstrate different enhancement characteristics to cancers, with benign lesions tending to demonstrate weaker, homogeneous contrast medium uptake. Limited evidence suggests malignant lesions exhibit "wash-out" or decreasing pattern of enhancement, and benign lesions a progressively enhancing one. The application of textural analysis and radiomics to CESM images shows promising results for differentiating benign and malignant lesions, with potential to predict immunohistological features. A large-scale multicentre study, ideally using multivendor CESM equipment, will be needed to confirm this.

    Original languageEnglish
    Pages (from-to)e313-e320
    Number of pages8
    JournalClinical Radiology
    Volume77
    Issue number4
    Early online date26 Jan 2022
    DOIs
    Publication statusPublished - Apr 2022

    ASJC Scopus subject areas

    • Radiology Nuclear Medicine and imaging

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