Alzahrani, N.M., Henry, A.M. orcid.org/0000-0002-5379-6618, Clark, A.K. et al. (3 more authors) (2024) Dosimetric impact of contour editing on CT and MRI deep‐learning autosegmentation for brain OARs. Journal of Applied Clinical Medical Physics, 25 (5). e14345. ISSN 1526-9914
Abstract
Purpose To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-based models. The correlation between geometric and dosimetric measures was also investigated to establish whether dosimetric assessment is required for clinical validation.
Method CT and MRI-based deep learning autosegmentation models were trained using edited and unedited clinical contours. Autosegmentations were dosimetrically compared to gold standard contours for a test cohort. D1%, D5%, D50%, and maximum dose were used as clinically relevant dosimetric measures. The statistical significance of dosimetric differences between the gold standard and autocontours was established using paired Student's t-tests. Clinically significant cases were identified via dosimetric headroom to the OAR tolerance. Pearson's Correlations were used to investigate the relationship between geometric measures and absolute percentage dose changes for each autosegmentation model.
Results Except for the right orbit, when delineated using MRI models, the dosimetric statistical analysis revealed no superior model in terms of the dosimetric accuracy between the CT DL-AC models or between the MRI DL-AC for any investigated brain OARs. The number of patients where the clinical significance threshold was exceeded was higher for the optic chiasm D1% than other OARs, for all autosegmentation models. A weak correlation was consistently observed between the outcomes of dosimetric and geometric evaluations.
Conclusions Editing contours before training the DL-AC model had no significant impact on dosimetry. The geometric test metrics were inadequate to estimate the impact of contour inaccuracies on dose. Accordingly, dosimetric analysis is needed to evaluate the clinical applicability of DL-AC models in the brain.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | autosegmentation; brain cancer; CT scans; deep learning; dosimetric evaluation; MRI scans; organs at risk |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Medical Research (LIMR) > Division of Oncology |
Funding Information: | Funder Grant number Cancer Research UK Supplier No: 138573 A28832 |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 May 2024 13:34 |
Last Modified: | 21 Aug 2024 13:43 |
Published Version: | https://aapm.onlinelibrary.wiley.com/doi/10.1002/a... |
Status: | Published |
Publisher: | Wiley Open Access |
Identification Number: | 10.1002/acm2.14345 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:212346 |