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    Voxel-based analysis: Roadmap for clinical translation

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    Authors
    McWilliam, Alan
    Palma, G.
    Abravan, Azadeh
    Acosta, O.
    Appelt, A.
    Aznar, Marianne Camille
    Monti, S.
    Onjukka, E.
    Panettieri, V.
    Placidi, L.
    Rancati, T.
    Vasquez Osorio, Eliana
    Witte, M.
    Cella, L.
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    Affiliation
    The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK.
    Issue Date
    2023
    
    Metadata
    Show full item record
    Abstract
    Voxel-based analysis (VBA) allows the full, 3-dimensional, dose distribution to be considered in radiotherapy outcome analysis. This provides new insights into anatomical variability of pathophysiology and radiosensitivity by removing the need for a priori definition of organs assumed to drive the dose response associated with patient outcomes. This approach may offer powerful biological insights demonstrating the heterogeneity of the radiobiology across tissues and potential associations of the radiotherapy dose with further factors. As this methodological approach becomes established, consideration needs to be given to translating VBA results to clinical implementation for patient benefit. Here, we present a comprehensive roadmap for VBA clinical translation. Technical validation needs to demonstrate robustness to methodology, where clinical validation must show generalisability to external datasets and link to a plausible pathophysiological hypothesis. Finally, clinical utility requires demonstration of potential benefit for patients in order for successful translation to be feasible. For each step on the roadmap, key considerations are discussed and recommendations provided for best practice.
    Citation
    McWilliam A, Palma G, Abravan A, Acosta O, Appelt A, Aznar M, et al. Voxel-based analysis: Roadmap for clinical translation. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology. 2023 Sep 6;188:109868. PubMed PMID: 37683811. Epub 2023/09/09. eng.
    Journal
    Radiotherapy and Oncology
    URI
    http://hdl.handle.net/10541/626554
    DOI
    10.1016/j.radonc.2023.109868
    PubMed ID
    37683811
    Additional Links
    https://doi.org/10.1016/j.radonc.2023.109868
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.radonc.2023.109868
    Scopus Count
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