• 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 DateSubjectsProfilesView

    My Account

    LoginRegister

    Local Links

    The Christie WebsiteChristie Library and Knowledge Service

    Statistics

    Display statistics

    An evaluation of MR based deep learning auto-contouring for planning head and neck radiotherapy

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Authors
    Hague, Christina
    McPartlin, Andrew J
    Lee, Lip W
    Hughes, Christopher
    Mullan, Damian
    Beasley, William J
    Green, Andrew
    Price, Gareth J
    Whitehurst, Philip
    Slevin, Nicholas J
    van Herk, Marcel
    West, Catharine M L
    Chuter, Robert
    Show allShow less
    Affiliation
    Department of Head and Neck Clinical Oncology, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, United Kingdom
    Issue Date
    2021
    
    Metadata
    Show full item record
    Abstract
    Introduction: Auto contouring models help consistently define volumes and reduce clinical workload. This study aimed to evaluate the cross acquisition of a Magnetic Resonance (MR) deep learning auto contouring model for organ at risk (OAR) delineation in head and neck radiotherapy. Methods: Two auto contouring models were evaluated using deep learning contouring expert (DLCExpert) for OAR delineation: a CT model (modelCT) and an MR model (modelMRI). Models were trained to generate auto contours for the bilateral parotid glands and submandibular glands. Auto-contours for modelMRI were trained on diagnostic images and tested on 10 diagnostic, 10 MR radiotherapy planning (RTP), eight MR-Linac (MRL) scans and, by modelCT, on 10 CT planning scans. Goodness of fit scores, dice similarity coefficient (DSC) and distance to agreement (DTA) were calculated for comparison. Results: ModelMRI contours improved the mean DSC and DTA compared with manual contours for the bilateral parotid glands and submandibular glands on the diagnostic and RTP MRs compared with the MRL sequence. There were statistically significant differences seen for modelMRI compared to modelCT for the left parotid (mean DTA 2.3 v 2.8 mm), right parotid (mean DTA 1.9 v 2.7 mm), left submandibular gland (mean DTA 2.2 v 2.4 mm) and right submandibular gland (mean DTA 1.6 v 3.2 mm). Conclusion: A deep learning MR auto-contouring model shows promise for OAR auto-contouring with statistically improved performance vs a CT based model. Performance is affected by the method of MR acquisition and further work is needed to improve its use with MRL images.
    Citation
    Hague C, McPartlin A, Lee LW, Hughes C, Mullan D, Beasley W, et al. An evaluation of MR based deep learning auto-contouring for planning head and neck radiotherapy. Radiother Oncol. 2021;158:112-7.
    Journal
    Radiotherapy and Oncology
    URI
    http://hdl.handle.net/10541/623836
    DOI
    10.1016/j.radonc.2021.02.018
    PubMed ID
    33636229
    Additional Links
    https://dx.doi.org/10.1016/j.radonc.2021.02.018
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.radonc.2021.02.018
    Scopus Count
    Collections
    All Christie Publications

    entitlement

     
    DSpace software (copyright © 2002 - 2025)  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.