{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T11:34:11Z","timestamp":1778672051123,"version":"3.51.4"},"reference-count":17,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T00:00:00Z","timestamp":1714003200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100011703","name":"Yorkshire Cancer Research","doi-asserted-by":"publisher","award":["L389LM"],"award-info":[{"award-number":["L389LM"]}],"id":[{"id":"10.13039\/100011703","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To establish the clinical applicability of deep\u2010learning organ\u2010at\u2010risk autocontouring models (DL\u2010AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI\u2010based models. The correlation between geometric and dosimetric measures was also investigated to establish whether dosimetric assessment is required for clinical validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>CT and MRI\u2010based 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 <jats:italic>t<\/jats:italic>\u2010tests. 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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\u2010AC models or between the MRI DL\u2010AC 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Editing contours before training the DL\u2010AC 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\u2010AC models in the brain.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14345","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T04:18:22Z","timestamp":1714105102000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dosimetric impact of contour editing on CT and MRI deep\u2010learning autosegmentation for brain OARs"],"prefix":"10.1002","volume":"25","author":[{"given":"Nouf M.","family":"Alzahrani","sequence":"first","affiliation":[{"name":"Department of Diagnostic Radiology King Abdulaziz University  Jeddah Saudi Arabia"},{"name":"School of Medicine University of Leeds  Leeds UK"},{"name":"Department of Medical Physics and Engineering St James's University Hospital  Leeds UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ann M.","family":"Henry","sequence":"additional","affiliation":[{"name":"School of Medicine University of Leeds  Leeds UK"},{"name":"Department of Clinical Oncology St James's University Hospital  Leeds UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna K.","family":"Clark","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Engineering St James's University Hospital  Leeds UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bashar M.","family":"Al\u2010Qaisieh","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Engineering St James's University Hospital  Leeds UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Louise J.","family":"Murray","sequence":"additional","affiliation":[{"name":"School of Medicine University of Leeds  Leeds UK"},{"name":"Department of Clinical Oncology St James's University Hospital  Leeds UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael G.","family":"Nix","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Engineering St James's University Hospital  Leeds UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"e_1_2_9_2_1","first-page":"PP","article-title":"Image segmentation for MR brain tumor detection using machine learning: a review","author":"Soomro TA","year":"2022","journal-title":"IEEE Rev Biomed Eng"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.05.003"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13040667"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clon.2021.12.003"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.09.009"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2022.07.004"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/acf023"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2019.02.040"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.564737"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.01.016"},{"issue":"1","key":"e_1_2_9_12_1","article-title":"Anatomically consistent CNN\u2010based segmentation of organs\u2010at\u2010risk in cranial radiotherapy","volume":"7","author":"Mlynarski P","year":"2020","journal-title":"J Med Imaging (Bellingham)"},{"key":"e_1_2_9_13_1","doi-asserted-by":"crossref","unstructured":"Rusk\u00f3L CapalaME CzipczerV et al.Deep\u2010learning\u2010based segmentation of organs\u2010at\u2010risk in the head for MR\u2010assisted radiation therapy planning. 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