{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T21:08:52Z","timestamp":1773176932906,"version":"3.50.1"},"reference-count":38,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>During head and neck (HN) radiation therapy, patients may undergo anatomical changes due to tumor shrinkage or weight loss. For these patients, adaptive radiation therapy (ART) is required to correct treatment plans and to ensure that the prescribed radiation dose is delivered to the tumor while minimizing dose to the surrounding organs\u2010at\u2010risk (OARs). Patient pre\u2010treatment images and segmentation labels are always available during ART and may be incorporated into deep learning (DL) auto\u2010segmentation models to improve performance on mid\u2010treatment images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Existing DL methods typically incorporate pre\u2010treatment data during training. In this work, we investigated whether including pre\u2010treatment data at inference time would affect model performance, as inference\u2010time inclusion would eliminate the requirement for costly model retraining for new patient cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a general adaptive model (GAM) that included pre\u2010treatment data at inference time through additional input channels. We compared the GAM with a patient\u2010specific model (PSM), which included pre\u2010treatment data during training, a reference model (RM), which did not include pre\u2010treatment data, and a rigid image registration (RIR) method. Models were developed using a large dataset of pre\u2010 and mid\u2010treatment computed tomography images and segmentation labels (primary gross tumor volume [GTVp] and 16 OARs) for 110 patients who underwent ART for HN cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The GAM showed improved performance over the PSM and RM for several structures, with the largest differences in dice similarity coefficient for difficult\u2010to\u2010segment structures: the GTVp (RM: 0.17, PSM: 0.36, GAM: 0.61, RR: 0.65) and left\/right brachial plexus (RM: 0.38\/0.35, PSM: 0.43\/0.43, GAM: 0.49\/0.49, RR: 0.36\/0.38). The GAM attained similar performance to RR for all structures except the brainstem (GAM: 0.82, RR: 0.74), mandible (GAM: 0.88, RR: 0.68), and spinal cord (GAM: 0.76, RR: 0.51), for which the GAM performed higher.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The inclusion of patient pre\u2010treatment images and segmentation labels can improve auto\u2010segmentation performance during HN ART, in particular for structures with high variability or low contrast. Including pre\u2010treatment data at DL model inference time (GAM) may give improvements over standard DL models for the GTVp and several OARs, while eliminating the need for costly model retraining with new patient cohorts. However, rigid registration provides similar performance to adaptive DL models for the GTVp and most OARs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17732","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T20:55:54Z","timestamp":1741726554000},"page":"4590-4597","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A general model for head and neck auto\u2010segmentation with patient pre\u2010treatment imaging during adaptive radiation therapy"],"prefix":"10.1002","volume":"52","author":[{"given":"Brett","family":"Clark","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering University of Melbourne  Melbourne Australia"},{"name":"Department of Physical Sciences Peter MacCallum Cancer Centre  Melbourne Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas","family":"Hardcastle","sequence":"additional","affiliation":[{"name":"Department of Physical Sciences Peter MacCallum Cancer Centre  Melbourne Australia"},{"name":"Centre for Medical Radiation Physics University of Wollongong  Wollongong Australia"},{"name":"Sir Peter MacCallum Department of Oncology University of Melbourne  Melbourne Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mathieu","family":"Gaudreault","sequence":"additional","affiliation":[{"name":"Department of Physical Sciences Peter MacCallum Cancer Centre  Melbourne Australia"},{"name":"Sir Peter MacCallum Department of Oncology University of Melbourne  Melbourne Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leigh A.","family":"Johnston","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of Melbourne  Melbourne Australia"},{"name":"Melbourne Brain Centre Imaging Unit University of Melbourne  Melbourne Australia"},{"name":"Graeme Clark Institute University of Melbourne  Melbourne Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James C.","family":"Korte","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of Melbourne  Melbourne Australia"},{"name":"Department of Physical Sciences Peter MacCallum Cancer Centre  Melbourne Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/s41199\u2010019\u20100046\u2010z"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2005.07.957"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2012.10.010"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2019.03.004"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2009.11.020"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.295913"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2008.01.018"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1006\/cviu.1995.1004"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.927467"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2019.02.001"},{"key":"e_1_2_9_12_1","unstructured":"RonnebergerO FischerP BroxT.U\u2010Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:150504597[cs]. Published online May 18 2015. Accessed November 2 2020.http:\/\/arxiv.org\/abs\/1505.04597"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2024.110410"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/accaca"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2021.07.550"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15352"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2022.06.001"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2018.10.037"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13495"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3091011"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3128408"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1080\/21681163.2019.1673824"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278\u2010017\u20100037\u20108"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16997"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592\u2010020\u201001008\u2010z"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102035"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13300"},{"key":"e_1_2_9_29_1","unstructured":"KingmaDP BaJ.Adam: A Method for Stochastic Optimization. Published online January 29 2017. doi:10.48550\/arXiv.1412.6980"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106236"},{"key":"e_1_2_9_31_1","article-title":"Pytorch: an imperative style, high\u2010performance deep learning library","volume":"32","author":"Paszke A","year":"2019","journal-title":"Adv Neural Inform Process Syst"},{"key":"e_1_2_9_32_1","unstructured":"FalconW.The PyTorch Lightning team. PyTorch Lightning. Published onlineMarch 2019. doi:10.5281\/zenodo.3828935"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.2307\/1932409"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.5.1.015006"},{"key":"e_1_2_9_35_1","volume-title":"Advances in Neural Information Processing Systems","author":"Nadeau C","year":"1999"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16056"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.07.041"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2017.10.016"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14378"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.17732","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T08:06:35Z","timestamp":1762934795000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.17732"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,7]]},"references-count":38,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["10.1002\/mp.17732"],"URL":"https:\/\/doi.org\/10.1002\/mp.17732","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,7]]},"assertion":[{"value":"2024-06-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}