• 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

    Larynx cancer survival model developed through open-source federated learning

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    36208652.pdf
    Size:
    1.770Mb
    Format:
    PDF
    Description:
    Identified with Open Access button
    Download
    Authors
    Rønn Hansen, C.
    Price, Gareth J
    Field, M.
    Sarup, N.
    Zukauskaite, R.
    Johansen, J.
    Grau Eriksen, J.
    Aly, F.
    McPartlin, Andrew J
    Holloway, L.
    Thwaites, D.
    Brink, C.
    Show allShow less
    Affiliation
    Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark
    Issue Date
    2022
    
    Metadata
    Show full item record
    Abstract
    Introduction: Federated learning has the potential to perfrom analysis on decentralised data; however, there are some obstacles to survival analyses as there is a risk of data leakage. This study demonstrates how to perform a stratified Cox regression survival analysis specifically designed to avoid data leakage using federated learning on larynx cancer patients from centres in three different countries. Methods: Data were obtained from 1821 larynx cancer patients treated with radiotherapy in three centres. Tumour volume was available for all 786 of the included patients. Parameter selection among eleven clinical and radiotherapy parameters were performed using best subset selection and cross-validation through the federated learning system, AusCAT. After parameter selection, β regression coefficients were estimated using bootstrap. Calibration plots were generated at 2 and 5-years survival, and inner and outer risk groups' Kaplan-Meier curves were compared to the Cox model prediction. Results: The best performing Cox model included log(GTV), performance status, age, smoking, haemoglobin and N-classification; however, the simplest model with similar statistical prediction power included log(GTV) and performance status only. The Harrell C-indices for the simplest model were for Odense, Christie and Liverpool 0.75[0.71-0.78], 0.65[0.59-0.71], and 0.69[0.59-0.77], respectively. The values are slightly higher for the full model with C-index 0.77[0.74-0.80], 0.67[0.62-0.73] and 0.71[0.61-0.80], respectively. Smoking during treatment has the same hazard as a ten-years older nonsmoking patient. Conclusion: Without any patient-specific data leaving the hospitals, a stratified Cox regression model based on data from centres in three countries was developed without data leakage risks. The overall survival model is primarily driven by tumour volume and performance status.
    Citation
    Rønn Hansen C, Price G, Field M, Sarup N, Zukauskaite R, Johansen J, et al. Larynx cancer survival model developed through open-source federated learning. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology. 2022 Oct 5. PubMed PMID: 36208652. Epub 2022/10/09. eng.
    Journal
    Radiotherapy and Oncology
    URI
    http://hdl.handle.net/10541/625707
    DOI
    10.1016/j.radonc.2022.09.023
    PubMed ID
    36208652
    Additional Links
    https://dx.doi.org/10.1016/j.radonc.2022.09.023
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.radonc.2022.09.023
    Scopus Count
    Collections
    All Christie Publications

    entitlement

    Related articles

    • Open-source distributed learning validation for a larynx cancer survival model following radiotherapy.
    • Authors: Hansen CR, Price G, Field M, Sarup N, Zukauskaite R, Johansen J, Eriksen JG, Aly F, McPartlin A, Holloway L, Thwaites D, Brink C
    • Issue date: 2022 Aug
    • Survival outcome prediction in cervical cancer: Cox models vs deep-learning model.
    • Authors: Matsuo K, Purushotham S, Jiang B, Mandelbaum RS, Takiuchi T, Liu Y, Roman LD
    • Issue date: 2019 Apr
    • Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.
    • Authors: Jochems A, Deist TM, El Naqa I, Kessler M, Mayo C, Reeves J, Jolly S, Matuszak M, Ten Haken R, van Soest J, Oberije C, Faivre-Finn C, Price G, de Ruysscher D, Lambin P, Dekker A
    • Issue date: 2017 Oct 1
    • Cetuximab and Radiotherapy in Laryngeal Preservation for Cancers of the Larynx and Hypopharynx: A Secondary Analysis of a Randomized Clinical Trial.
    • Authors: Bonner J, Giralt J, Harari P, Spencer S, Schulten J, Hossain A, Chang SC, Chin S, Baselga J
    • Issue date: 2016 Sep 1
    • Novel head and neck cancer survival analysis approach: random survival forests versus Cox proportional hazards regression.
    • Authors: Datema FR, Moya A, Krause P, Bäck T, Willmes L, Langeveld T, Baatenburg de Jong RJ, Blom HM
    • Issue date: 2012 Jan
    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.