{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:24:48Z","timestamp":1787444688591,"version":"build-2736575974"},"reference-count":26,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jmirs.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Medical Imaging and Radiation Sciences"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.jmir.2026.102560","type":"journal-article","created":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:12:26Z","timestamp":1787443946000},"page":"102560","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Organ-at-risk contouring education in the era of AI-assisted practice: Insights from an Australian undergraduate radiation therapy program"],"prefix":"10.1016","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6714-4362","authenticated-orcid":false,"given":"Crispen","family":"Chamunyonga","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7658-1516","authenticated-orcid":false,"given":"Julie","family":"Burbery","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kerrie","family":"Mengersen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4298-2228","authenticated-orcid":false,"given":"Catriona","family":"Hargrave","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jmir.2026.102560_bib0001","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1186\/1748-717X-7-160","article-title":"Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer","volume":"7","author":"La Macchia","year":"2012","journal-title":"Radiat Oncol"},{"issue":"2","key":"10.1016\/j.jmir.2026.102560_bib0002","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1002\/mp.12045","article-title":"Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks","volume":"44","author":"Ibragimov","year":"2017","journal-title":"Med Phys"},{"key":"10.1016\/j.jmir.2026.102560_bib0003","doi-asserted-by":"crossref","first-page":"178","DOI":"10.3389\/fonc.2016.00178","article-title":"Comparison of automated atlas-based segmentation software for postoperative prostate cancer radiotherapy","volume":"6","author":"Delpon","year":"2016","journal-title":"Front Oncol"},{"key":"10.1016\/j.jmir.2026.102560_bib0004","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.radonc.2022.10.029","article-title":"Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system","volume":"177","author":"Costea","year":"2022","journal-title":"RadiOther Oncol"},{"issue":"3","key":"10.1016\/j.jmir.2026.102560_bib0005","article-title":"Evaluation of A deep learning-based auto-segmentation method for quality assurance of both male and female pelvic organ-At-risk contours In NCTN clinical trials","volume":"108","author":"Geng","year":"2020","journal-title":"Int J Radiat Oncol*Biology*Physics"},{"issue":"34","key":"10.1016\/j.jmir.2026.102560_bib0006","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000021800","article-title":"Comparison between atlas and convolutional neural network based automatic segmentation of multiple organs at risk in non-small cell lung cancer","volume":"99","author":"Zhang","year":"2020","journal-title":"Medicine (Baltimore)"},{"key":"10.1016\/j.jmir.2026.102560_bib0007","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.radonc.2020.05.038","article-title":"Organ at risk delineation for radiation therapy clinical trials: global harmonization group consensus guidelines","volume":"150","author":"Mir","year":"2020","journal-title":"RadiOther Oncol"},{"issue":"1","key":"10.1016\/j.jmir.2026.102560_bib0008","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.radonc.2015.07.041","article-title":"CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG oncology and TROG consensus guidelines","volume":"117","author":"Brouwer","year":"2015","journal-title":"RadiOther Oncol"},{"issue":"2","key":"10.1016\/j.jmir.2026.102560_bib0009","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.prro.2018.12.003","article-title":"Standardizing normal tissue contouring for radiation therapy treatment planning: an ASTRO consensus paper","volume":"9","author":"Wright","year":"2019","journal-title":"Pract Radiat Oncol"},{"issue":"4","key":"10.1016\/j.jmir.2026.102560_bib0010","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1016\/j.radi.2023.05.015","article-title":"What do we want? Training! when do we want it? Now? A training needs analysis for adaptive radiotherapy for therapeutic radiographers","volume":"29","author":"Joyce","year":"2023","journal-title":"Radiography (Lond)"},{"issue":"2","key":"10.1016\/j.jmir.2026.102560_bib0011","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.jmir.2019.04.001","article-title":"Keeping up with the hybrid magnetic resonance linear accelerators: how do radiation therapists stay current in the era of hybrid technologies?","volume":"50","author":"Eccles","year":"2019","journal-title":"J Med ImAging Radiat Sci"},{"key":"10.1016\/j.jmir.2026.102560_bib0012","first-page":"1","article-title":"Australian Society of Medical Imaging and Radiation Therapy Image Registration in radiation therapy position paper","author":"Barry","year":"2026","journal-title":"J Med Radiat Sci"},{"issue":"2","key":"10.1016\/j.jmir.2026.102560_bib0013","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1002\/jmrs.729","article-title":"Automation and artificial intelligence in radiation therapy treatment planning","volume":"71","author":"Jones","year":"2024","journal-title":"J Med Radiat Sci"},{"key":"10.1016\/j.jmir.2026.102560_bib0014","series-title":"Guidance On Auto-Contouring in Radiotherapy","year":"2024"},{"key":"10.1016\/j.jmir.2026.102560_bib0015","first-page":"460","article-title":"Exploring the risks of automation bias in healthcare artificial intelligence applications: a bowtie analysis","volume":"5","author":"Abdelwanis","year":"2024","journal-title":"J Saf Sci Resil"},{"issue":"1","key":"10.1016\/j.jmir.2026.102560_bib0016","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1186\/s12909-021-02992-2","article-title":"Implementation of web-based open-source radiotherapy delineation software (WORDS) in organs at risk contouring training for newly qualified radiotherapists: quantitative comparison with conventional one-to-one coaching approach","volume":"21","author":"Yuen","year":"2021","journal-title":"BMC Med Educ"},{"key":"10.1016\/j.jmir.2026.102560_bib0017","article-title":"Practice-based training strategy for therapist-driven prostate MR-Linac adaptive radiotherapy. Technical innovations & patient support in radiation","volume":"27","author":"Li","year":"2023","journal-title":"Oncology"},{"issue":"4","key":"10.1016\/j.jmir.2026.102560_bib0018","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1016\/j.ijrobp.2014.11.009","article-title":"Evaluating the impact of a Canadian national anatomy and radiology contouring boot camp for radiation oncology residents","volume":"91","author":"Jaswal","year":"2015","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.jmir.2026.102560_bib0019","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.radonc.2019.11.004","article-title":"Systematic review of educational interventions to improve contouring in radiotherapy","volume":"144","author":"Cacicedo","year":"2020","journal-title":"RadiOther Oncol"},{"issue":"2","key":"10.1016\/j.jmir.2026.102560_bib0020","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1002\/jmrs.791","article-title":"Artificial Intelligence and the future of radiotherapy planning: the Australian radiation therapists prepare to be ready","volume":"71","author":"Panettieri","year":"2024","journal-title":"J Med Radiat Sci"},{"issue":"2","key":"10.1016\/j.jmir.2026.102560_bib0021","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.jmir.2020.01.008","article-title":"The impact of artificial intelligence and machine learning in radiation therapy: considerations for future curriculum enhancement","volume":"51","author":"Chamunyonga","year":"2020","journal-title":"J Med ImAging Radiat Sci"},{"issue":"3","key":"10.1016\/j.jmir.2026.102560_bib0022","article-title":"Artificial intelligence\u2013Based autosegmentation: advantages in delineation, absorbed dose-distribution, and logistics","volume":"9","author":"Sarria","year":"2024","journal-title":"Adv Radiat Oncol"},{"issue":"1","key":"10.1016\/j.jmir.2026.102560_bib0023","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1136\/amiajnl-2011-000089","article-title":"Automation bias: a systematic review of frequency, effect mediators, and mitigators","volume":"19","author":"Goddard","year":"2012","journal-title":"J Am Med Inform Assoc"},{"issue":"3","key":"10.1016\/j.jmir.2026.102560_bib0024","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.radi.2017.03.016","article-title":"Occupational burnout among radiation therapists in Australia: findings from a mixed methods study","volume":"23","author":"Singh","year":"2017","journal-title":"Radiography"},{"key":"10.1016\/j.jmir.2026.102560_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2024.110546","article-title":"Is full-automation in radiotherapy treatment planning ready for take off?","volume":"201","author":"Callens","year":"2024","journal-title":"Radiother Oncol"},{"issue":"3","key":"10.1016\/j.jmir.2026.102560_bib0026","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.ijrobp.2016.11.050","article-title":"Multi-institutional randomized trial testing the utility of an interactive three-dimensional contouring atlas among radiation oncology residents","volume":"98","author":"Gillespie","year":"2017","journal-title":"Int J Radiat Oncol Biol Phys"}],"container-title":["Journal of Medical Imaging and Radiation Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1939865426003747?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1939865426003747?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:12:29Z","timestamp":1787443949000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1939865426003747"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":26,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["S1939865426003747"],"URL":"https:\/\/doi.org\/10.1016\/j.jmir.2026.102560","relation":{},"ISSN":["1939-8654"],"issn-type":[{"value":"1939-8654","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Organ-at-risk contouring education in the era of AI-assisted practice: Insights from an Australian undergraduate radiation therapy program","name":"articletitle","label":"Article Title"},{"value":"Journal of Medical Imaging and Radiation Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jmir.2026.102560","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Inc. on behalf of Canadian Association of Medical Radiation Technologists.","name":"copyright","label":"Copyright"}],"article-number":"102560"}}