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    Intensity standardization of MRI prior to radiomic feature extraction for artificial intelligence research in glioma-a systematic review

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    Authors
    Fatania, K.
    Mohamud, F.
    Clark, A.
    Nix, M.
    Short, S. C.
    O'Connor, James P B
    Scarsbrook, A. F.
    Currie, S.
    Affiliation
    Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
    Issue Date
    2022
    
    Metadata
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    Abstract
    Objectives: Radiomics is a promising avenue in non-invasive characterisation of diffuse glioma. Clinical translation is hampered by lack of reproducibility across centres and difficulty in standardising image intensity in MRI datasets. The study aim was to perform a systematic review of different methods of MRI intensity standardisation prior to radiomic feature extraction. Methods: MEDLINE, EMBASE, and SCOPUS were searched for articles meeting the following eligibility criteria: MRI radiomic studies where one method of intensity normalisation was compared with another or no normalisation, and original research concerning patients diagnosed with diffuse gliomas. Using PRISMA criteria, data were extracted from short-listed studies including number of patients, MRI sequences, validation status, radiomics software, method of segmentation, and intensity standardisation. QUADAS-2 was used for quality appraisal. Results: After duplicate removal, 741 results were returned from database and reference searches and, from these, 12 papers were eligible. Due to a lack of common pre-processing and different analyses, a narrative synthesis was sought. Three different intensity standardisation techniques have been studied: histogram matching (5/12), limiting or rescaling signal intensity (8/12), and deep learning (1/12)-only two papers compared different methods. From these studies, histogram matching produced the more reliable features compared to other methods of altering MRI signal intensity. Conclusion: Multiple methods of intensity standardisation have been described in the literature without clear consensus. Further research that directly compares different methods of intensity standardisation on glioma MRI datasets is required.
    Citation
    Fatania K, Mohamud F, Clark A, Nix M, Short SC, O’Connor J, et al. Intensity standardization of MRI prior to radiomic feature extraction for artificial intelligence research in glioma—a systematic review. European Radiology. Springer Science and Business Media LLC; 2022.
    Journal
    European Radiology
    URI
    http://hdl.handle.net/10541/625268
    DOI
    10.1007/s00330-022-08807-2
    PubMed ID
    35486171
    Additional Links
    https://dx.doi.org/10.1007/s00330-022-08807-2
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00330-022-08807-2
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