{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T13:03:15Z","timestamp":1792242195323,"version":"4.4.0"},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T00:00:00Z","timestamp":1696982400000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005010","name":"Associazione Italiana per la Ricerca sul Cancro","doi-asserted-by":"publisher","award":["IG23150"],"award-info":[{"award-number":["IG23150"]}],"id":[{"id":"10.13039\/501100005010","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005010","name":"Associazione Italiana per la Ricerca sul Cancro","doi-asserted-by":"publisher","award":["IG25951"],"award-info":[{"award-number":["IG25951"]}],"id":[{"id":"10.13039\/501100005010","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","phiro.science","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Physics and Imaging in Radiation Oncology"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1016\/j.phro.2023.100501","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T11:55:43Z","timestamp":1697198143000},"page":"100501","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":31,"special_numbering":"C","title":["Real-world validation of Artificial Intelligence-based Computed Tomography auto-contouring for prostate cancer radiotherapy planning"],"prefix":"10.1016","volume":"28","author":[{"given":"Gabriele","family":"Palazzo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paola","family":"Mangili","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiara","family":"Deantoni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9285-4390","authenticated-orcid":false,"given":"Andrei","family":"Fodor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Broggi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2452-1305","authenticated-orcid":false,"given":"Roberta","family":"Castriconi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Giulia","family":"Ubeira Gabellini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonella","family":"del Vecchio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadia G.","family":"Di Muzio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0013-8494","authenticated-orcid":false,"given":"Claudio","family":"Fiorino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.phro.2023.100501_b0005","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/S0167-8140(98)00021-8","article-title":"Intra- and inter-observer variability in contouring prostate and seminal vesicles: implications for conformal treatment planning","volume":"47","author":"Fiorino","year":"1998","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0010","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/S0360-3016(03)00659-X","article-title":"The impact of contouring uncertainty on rectal 3D dose-volume data: results of a dummy run in a multicenter trial (AIROPROS01-02)","volume":"57","author":"Foppiano","year":"2003","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.phro.2023.100501_b0015","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.ijrobp.2011.12.057","article-title":"Contouring variability of the penile bulb on CT images: quantitative assessment using a generalized concordance index","volume":"84","author":"Carillo","year":"2012","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.phro.2023.100501_b0020","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1111\/1754-9485.12844","article-title":"Multi-observer contouring of male pelvic anatomy: highly variable agreement across conventional and emerging structures of interest","volume":"63","author":"Roach","year":"2019","journal-title":"J Med Imaging Radiat Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0025","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.ijrobp.2022.11.021","article-title":"Assessing interobserver variability in the delineation of structures in Radiation Oncology: a systematic review","volume":"115","author":"Guzen","year":"2023","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.phro.2023.100501_b0030","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.radonc.2008.08.007","article-title":"A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer","volume":"90","author":"Huyskens","year":"2009","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0035","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics11111964","article-title":"Automatic segmentation of pelvic cancers using deep learning: State-of-the-art approaches and challenges","volume":"11","author":"Kalantar","year":"2021","journal-title":"Diagnostics"},{"key":"10.1016\/j.phro.2023.100501_b0040","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"},{"key":"10.1016\/j.phro.2023.100501_b0045","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ab2818","article-title":"Attention-enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision","volume":"64","author":"Kearney","year":"2019","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.phro.2023.100501_b0050","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/aaf11c","article-title":"Fully automated organ segmentation in male pelvic CT images","volume":"63","author":"Balagopal","year":"2018","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.phro.2023.100501_b0055","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1002\/jmrs.64","article-title":"Evaluation of atlas-based auto-segmentation software in prostate cancer patients","volume":"61","author":"Greenham","year":"2014","journal-title":"J Med Radiat Sci"},{"key":"10.1016\/j.phro.2023.100501_b0060","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.phro.2022.04.007","article-title":"Autosegmentation based on different sized training datasets of consistently curated volumes and impact on rectal contours in prostate cancer radiation therapy","volume":"22","author":"Olsson","year":"2022","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0065","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1186\/s13014-021-01896-1","article-title":"Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models","volume":"16","author":"Urago","year":"2021","journal-title":"Radiat Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0070","doi-asserted-by":"crossref","first-page":"e80","DOI":"10.1016\/j.prro.2020.05.013","article-title":"Clinical evaluation of deep learning and atlas-based auto-contouring of bladder and rectum for prostate radiation therapy","volume":"11","author":"Zabel","year":"2021","journal-title":"Pract Radiat Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0075","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.phro.2021.08.002","article-title":"Comparison of atlas-based auto-segmentation accuracy for radiotherapy in prostate cancer","volume":"19","author":"Aoyama","year":"2021","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0080","first-page":"E131","article-title":"Assessing the role of artificial intelligence (AI) in clinical oncology: utility of machine learning in radiotherapy target volume delineation","volume":"5","author":"Boon","year":"2018","journal-title":"Medicines (Basel)"},{"key":"10.1016\/j.phro.2023.100501_b0085","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.radonc.2020.10.002","article-title":"The role of computational methods for automating and improving clinical target volume definition","volume":"153","author":"Unkelbach","year":"2020","journal-title":"Radiother Oncol"},{"issue":"1500\u20131513","key":"10.1016\/j.phro.2023.100501_b0090","article-title":"Technology-driven research for radiotherapy innovation","volume":"14","author":"Fiorino","year":"2020","journal-title":"Mol Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0095","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.radonc.2018.05.030","article-title":"Artificial intelligence in radiation oncology: a specialty-wide disruptive transformation?","volume":"129","author":"Thompson","year":"2018","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0100","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.radonc.2020.10.001","article-title":"Grand challenges for medical physics in radiation oncology","volume":"153","author":"Fiorino","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0105","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.1002\/mp.12831","article-title":"Artificial intelligence will soon change the landscape of medical physics research and practice","volume":"45","author":"Xing","year":"2018","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2023.100501_b0110","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1016\/j.ijrobp.2020.11.011","article-title":"Using auto-segmentation to reduce contouring and dose inconsistency in clinical trials: the simulated impact on RTOG 0617","volume":"109","author":"Thor","year":"2021","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.phro.2023.100501_b0115","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.13479","article-title":"Varian ethos online adaptive radiotherapy for prostate cancer: early results of contouring accuracy, treatment plan quality, and treatment time","volume":"23","author":"Byrne","year":"2022","journal-title":"J Appl Clin Med Phys"},{"issue":"121\u2013128","key":"10.1016\/j.phro.2023.100501_b0120","article-title":"Evaluating the effectiveness of deep learning contouring across multiple radiotherapy centres","volume":"24","author":"Walker","year":"2022","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0125","doi-asserted-by":"crossref","DOI":"10.1186\/s13014-021-01831-4","article-title":"Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers","volume":"16","author":"Wong","year":"2021","journal-title":"Radiat Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0130","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.phro.2022.04.008","article-title":"Real-world analysis of manual editing of deep learning contouring in the thorax region","volume":"22","author":"Vaassen","year":"2022","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0135","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.ijrobp.2008.08.038","article-title":"Phase I-II study of hypofractionated simultaneous integrated boost with tomotherapy for prostate cancer","volume":"74","author":"Di Muzio","year":"2009","journal-title":"Int J Radiat Oncol Biol Phys"},{"issue":"19","key":"10.1016\/j.phro.2023.100501_b0140","doi-asserted-by":"crossref","first-page":"4970","DOI":"10.3390\/cancers13194970","article-title":"Ten year results of extensive nodal radiotherapy and moderately hypofractionated simultaneous integrated boost in unfavorable intermediate-, high-, and very high-risk prostate cancer","volume":"13","author":"Di Muzio","year":"2021","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.phro.2023.100501_b0145","doi-asserted-by":"crossref","first-page":"e236","DOI":"10.1016\/j.prro.2020.09.012","article-title":"Clinical implementation of knowledge-based automatic plan optimization for helical tomotherapy","volume":"11","author":"Castriconi","year":"2021","journal-title":"Pract Radiat Oncol"},{"key":"10.1016\/j.phro.2023.100501_b0150","doi-asserted-by":"crossref","DOI":"10.2307\/1932409","article-title":"Measures of the amount of ecologic association between species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"key":"10.1016\/j.phro.2023.100501_b0155","doi-asserted-by":"crossref","first-page":"45","DOI":"10.3389\/fninf.2013.00045","article-title":"The design of simpleitk","volume":"7","author":"Lowekamp","year":"2013","journal-title":"Front Neuroinf"},{"key":"10.1016\/j.phro.2023.100501_b0160","doi-asserted-by":"crossref","first-page":"5105","DOI":"10.1002\/mp.13200","article-title":"Comparative evaluation of autocontouring in clinical practice: a practical method using the Turing test","volume":"45","author":"Gooding","year":"2018","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2023.100501_b0165","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2023.1213068","article-title":"A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy","volume":"13","author":"Doolan","year":"2023","journal-title":"Front Oncol."},{"key":"10.1016\/j.phro.2023.100501_b0170","doi-asserted-by":"crossref","first-page":"4079","DOI":"10.1002\/mp.16537","article-title":"Incremental retraining, clinical implementation, and acceptance rate of deep learning auto-segmentation for male pelvis in a multiuser environment","volume":"50","author":"Duan","year":"2023","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2023.100501_b0175","first-page":"2088","article-title":"Implementation of a commercial deep learning-based auto segmentation software in radiotherapy: evaluation of effectiveness and impact on workflow","volume":"12","author":"Radici","year":"2022","journal-title":"Life (Basel)"}],"container-title":["Physics and Imaging in Radiation Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2405631623000921?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2405631623000921?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T21:47:46Z","timestamp":1778881666000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2405631623000921"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":35,"alternative-id":["S2405631623000921"],"URL":"https:\/\/doi.org\/10.1016\/j.phro.2023.100501","relation":{},"ISSN":["2405-6316"],"issn-type":[{"value":"2405-6316","type":"print"}],"subject":[],"published":{"date-parts":[[2023,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Real-world validation of Artificial Intelligence-based Computed Tomography auto-contouring for prostate cancer radiotherapy planning","name":"articletitle","label":"Article Title"},{"value":"Physics and Imaging in Radiation Oncology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.phro.2023.100501","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Author(s). Published by Elsevier B.V. on behalf of European Society of Radiotherapy & Oncology.","name":"copyright","label":"Copyright"}],"article-number":"100501"}}