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Title
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Multi-institutional generalizability of a plan complexity machine learning model for predicting pre-treatment quality assurance results in radiotherapy
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Author
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Abstract
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| Background and purpose: Treatment plans in radiotherapy are subject to measurement-based pre-treatment verifications. In this study, plan complexity metrics (PCMs) were calculated per beam and used as input features to develop a predictive model. The aim of this study was to determine the robustness against differences in machine type and institutional-specific quality assurance (QA). Material and methods: A number of 567 beams were collected, where 477 passed and 90 failed the pre-treatment QA. Treatment plans of different anatomical regions were included. One type of linear accelerator was represented. For all beams, 16 PCMs were calculated. A random forest classifier was trained to distinct between acceptable and non-acceptable beams. The model was validated on other datasets to investigate its robustness. Firstly, plans for another machine type from the same institution were evaluated. Secondly, an inter-institutional validation was conducted on three datasets from different centres with their associated QA.Results: Intra-institutionally, the PCMs beam modulation, mean MLC gap, Q1 gap, and Modulation Complexity Score were the most informative to detect failing beams. Eighty-tree percent of the failed beams (15/18) were detected correctly. The model could not detect over-modulated beams of another machine type. Interinstitutionally, the model performance reached higher accuracy for centres with comparable equipment both for treatment and QA as the local institute.Conclusions: The study demonstrates that the robustness decreases when major differences appear in the QA platform or in planning strategies, but that it is feasible to extrapolate institutional-specific trained models between centres with similar clinical practice. Predictive models should be developed for each machine type. |
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Language
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English
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Source (journal)
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Physics and Imaging in Radiation Oncology / European Society of Radiotherapy & Oncology
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Publication
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Elsevier
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2024
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ISSN
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2405-6316
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DOI
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10.1016/J.PHRO.2023.100525
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Volume/pages
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29
(2024)
, p. 1-8
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Article Reference
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100525
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ISI
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001143017000001
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Pubmed ID
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38204910
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Full text (Publisher's DOI)
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Full text (open access)
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