• Login
    View Item 
    •   Home
    • The Christie Research Publications Repository
    • All Christie Publications
    • View Item
    •   Home
    • The Christie Research Publications Repository
    • All Christie Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of ChristieCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsThis CollectionTitleAuthorsIssue DateSubmit DateSubjects

    My Account

    LoginRegister

    Local Links

    The Christie WebsiteChristie Library and Knowledge Service

    Statistics

    Display statistics

    Overcoming challenges of translating deep learning models for Glioblastoma: the ZGBM consortium

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Authors
    Shuaib, H.
    Barker, G. J.
    Sasieni, P.
    De Vita, E.
    Chelliah, A.
    Andrei, R.
    Ashkan, K.
    Beaumont, E.
    Brazil, L.
    Rowland-Hill, C.
    Lau, A.
    Luis, A.
    Powell, J.
    Swampillai, A.
    Tenant, Sean
    Thust, S. C.
    Wastling, S.
    Young, T.
    Booth, T. C.
    Brock, J.
    Currie, S.
    Fatani, K.
    Foweraker, K.
    Glendenning, J.
    Hoggard, N.
    Kanodia, A. K.
    Krishnan, A.
    Thurston, M. D.
    Lewis, J.
    Linares, C.
    Mathew, R. K.
    Ramalingam, S.
    Sawlani, V.
    Welsh, L.
    Williams, M.
    Show allShow less
    Affiliation
    Guy's & St Thomas' NHS Foundation Trust, King's College, London
    Issue Date
    2022
    
    Metadata
    Show full item record
    Abstract
    Objective: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep learning models. Methods: MR imaging data were analysed from a random sample of 5 patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. Results: All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. Conclusion: The imaging protocol and scheduling varies across the UK, making it challenging to develop machine learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres.
    Citation
    Shuaib H, Barker GJ, Sasieni P, De Vita E, Chelliah A, Andrei R, et al. Overcoming challenges of translating deep learning models for Glioblastoma: the ZGBM consortium. The British Journal of Radiology. British Institute of Radiology; 2022.
    Journal
    British Journal of Radiology
    URI
    http://hdl.handle.net/10541/625331
    DOI
    10.1259/bjr.20220206
    PubMed ID
    35616700
    Additional Links
    https://dx.doi.org/10.1259/bjr.20220206
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1259/bjr.20220206
    Scopus Count
    Collections
    All Christie Publications

    entitlement

    Related articles

    • Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.
    • Authors: Jayachandran Preetha C, Meredig H, Brugnara G, Mahmutoglu MA, Foltyn M, Isensee F, Kessler T, Pflüger I, Schell M, Neuberger U, Petersen J, Wick A, Heiland S, Debus J, Platten M, Idbaih A, Brandes AA, Winkler F, van den Bent MJ, Nabors B, Stupp R, Maier-Hein KH, Gorlia T, Tonn JC, Weller M, Wick W, Bendszus M, Vollmuth P
    • Issue date: 2021 Dec
    • IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation.
    • Authors: Choi Y, Nam Y, Lee YS, Kim J, Ahn KJ, Jang J, Shin NY, Kim BS, Jeon SS
    • Issue date: 2020 Jul
    • Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine.
    • Authors: Perkuhn M, Stavrinou P, Thiele F, Shakirin G, Mohan M, Garmpis D, Kabbasch C, Borggrefe J
    • Issue date: 2018 Nov
    • Deep learning-based convolutional neural network for intramodality brain MRI synthesis.
    • Authors: Osman AFI, Tamam NM
    • Issue date: 2022 Apr
    • Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images.
    • Authors: Shin I, Kim H, Ahn SS, Sohn B, Bae S, Park JE, Kim HS, Lee SK
    • Issue date: 2021 May
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.