{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T12:38:32Z","timestamp":1793104712707,"version":"4.5.0"},"reference-count":25,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":29,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["P30CA008748"],"award-info":[{"award-number":["P30CA008748"]}],"id":[{"id":"10.13039\/100000054","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":[[2026,9]]},"DOI":"10.1016\/j.phro.2026.101100","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T22:51:55Z","timestamp":1790981515000},"page":"101100","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Three-dimensional deep learning nomogram for post-plan quality assurance in high-dose-rate prostate brachytherapy"],"prefix":"10.1016","volume":"41","author":[{"given":"Joel","family":"Beaudry","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tonghe","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirin A.","family":"Enger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio L.","family":"Damato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.phro.2026.101100_bb0005","doi-asserted-by":"crossref","DOI":"10.2349\/biij.2.2.e34","article-title":"High dose rate (HDR) brachytherapy quality assurance: a practical guide","volume":"2","author":"Wilkinson","year":"2006","journal-title":"Biomed Imaging Interv J"},{"key":"10.1016\/j.phro.2026.101100_bb0010","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/S0360-3016(02)02786-4","article-title":"The Anderson nomograms for permanent interstitial prostate implants: a briefing for practitioners","volume":"53","author":"Cohen","year":"2002","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.phro.2026.101100_bb0015","doi-asserted-by":"crossref","first-page":"121","DOI":"10.5114\/jcb.2011.24817","article-title":"The use of nomograms in LDR-HDR prostate brachytherapy","volume":"3","author":"Pujades","year":"2011","journal-title":"J Contemp Brachytherapy"},{"key":"10.1016\/j.phro.2026.101100_bb0020","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1016\/S0360-3016(00)00725-2","article-title":"An independent dose-to-point calculation program for the verification of high-dose-rate brachytherapy treatment planning","volume":"48","author":"Cohen","year":"2000","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.phro.2026.101100_bb0025","doi-asserted-by":"crossref","first-page":"3626","DOI":"10.1118\/1.4956888","article-title":"SU-G-201-15: nomogram as an efficient dosimetric verification tool in HDR prostate brachytherapy","volume":"43","author":"Liang","year":"2016","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2026.101100_bb0030","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.radonc.2011.09.022","article-title":"Methods of verifying the output of the treatment planning system used for high dose rate (HDR) prostate brachytherapy","volume":"103","author":"Huckle","year":"2012","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.phro.2026.101100_bb0035","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.brachy.2005.11.002","article-title":"Quality assurance of treatment plans for interstitial and intracavitary high-dose-rate brachytherapy","volume":"5","author":"Das","year":"2006","journal-title":"Brachytherapy"},{"key":"10.1016\/j.phro.2026.101100_bb0040","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.brachy.2022.12.007","article-title":"A precise and simple isodose-volume-based verification method for HDR and LDR brachytherapy plans","volume":"22","author":"Schiefer","year":"2023","journal-title":"Brachytherapy"},{"key":"10.1016\/j.phro.2026.101100_bb0045","doi-asserted-by":"crossref","first-page":"e168","DOI":"10.1002\/mp.13445","article-title":"Radiation therapy quality assurance tasks and tools: the many roles of machine learning","volume":"47","author":"Kalet","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2026.101100_bb0050","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1016\/j.ijrobp.2020.04.045","article-title":"RapidBrachyDL: rapid radiation dose calculations in brachytherapy via deep learning","volume":"108","author":"Mao","year":"2020","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.phro.2026.101100_bb0055","doi-asserted-by":"crossref","first-page":"4591","DOI":"10.1002\/mp.17230","article-title":"Neural network dose prediction for cervical brachytherapy: overcoming data scarcity for applicator-specific models","volume":"51","author":"Moore","year":"2024","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2026.101100_bb0060","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.brachy.2025.06.005","article-title":"High-dose-rate (HDR) brachytherapy boost in combination with external beam radiotherapy for localized prostate cancer: an evidence-based consensus statement","volume":"24","author":"Patel","year":"2025","journal-title":"Brachytherapy"},{"key":"10.1016\/j.phro.2026.101100_bb0065","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.brachy.2011.09.008","article-title":"American brachytherapy society consensus guidelines for high-dose-rate prostate brachytherapy","volume":"11","author":"Yamada","year":"2012","journal-title":"Brachytherapy"},{"key":"10.1016\/j.phro.2026.101100_bb0070","series-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.phro.2026.101100_bb0075","series-title":"MONAI: an open-source framework for deep learning in healthcare","author":"Cardoso","year":"2022"},{"key":"10.1016\/j.phro.2026.101100_bb0080","article-title":"AID-RT: standardising artificial intelligence documentation in RadioTherapy with a domain-specific model card","volume":"38","author":"Barrag\u00e1n-Montero","year":"2026","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.phro.2026.101100_bb0085","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.brachy.2017.10.015","article-title":"Dosimetric impact of inter-observer catheter reconstruction variability in ultrasound-based high-dose-rate prostate brachytherapy","volume":"17","author":"Nicolae","year":"2018","journal-title":"Brachytherapy"},{"key":"10.1016\/j.phro.2026.101100_bb0090","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.brachy.2017.12.003","article-title":"Treatment delivery verification in brachytherapy: prospects of technology innovation","volume":"17","author":"Tanderup","year":"2018","journal-title":"Brachytherapy"},{"key":"10.1016\/j.phro.2026.101100_bb0095","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/acecd2","article-title":"Deep learning-based dose map prediction for high-dose-rate brachytherapy","volume":"68","author":"Li","year":"2023","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.phro.2026.101100_bb0100","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.brachy.2024.11.007","article-title":"Towards U-net-based intraoperative 2D dose prediction in high dose rate prostate brachytherapy","volume":"24","author":"Knull","year":"2025","journal-title":"Brachytherapy"},{"key":"10.1016\/j.phro.2026.101100_bb0105","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.brachy.2024.10.009","article-title":"Instant plan quality prediction on transrectal ultrasound for high-dose-rate prostate brachytherapy","volume":"24","author":"Wang","year":"2025","journal-title":"Brachytherapy"},{"key":"10.1016\/j.phro.2026.101100_bb0110","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ijrobp.2024.12.026","article-title":"Clinical application of deep learning-assisted needles reconstruction in prostate ultrasound brachytherapy","volume":"122","author":"Goulet","year":"2025","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.phro.2026.101100_bb0115","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1002\/mp.14328","article-title":"Deep learning applications in automatic needle segmentation in ultrasound-guided prostate brachytherapy","volume":"47","author":"Wang","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2026.101100_bb0120","doi-asserted-by":"crossref","first-page":"2735","DOI":"10.1002\/mp.14128","article-title":"Multi-needle localization with attention U-net in US-guided HDR prostate brachytherapy","volume":"47","author":"Zhang","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.phro.2026.101100_bb0125","doi-asserted-by":"crossref","first-page":"3","DOI":"10.5114\/jcb.2026.160423","article-title":"Polish brachytherapy society clinical recommendations and expert consensus on radical high-dose-rate brachytherapy for prostate cancer","volume":"18","author":"Chyrek","year":"2026","journal-title":"J Contemp Brachytherapy"}],"container-title":["Physics and Imaging in Radiation Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2405631626002009?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2405631626002009?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T12:27:57Z","timestamp":1793104077000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2405631626002009"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":25,"alternative-id":["S2405631626002009"],"URL":"https:\/\/doi.org\/10.1016\/j.phro.2026.101100","relation":{},"ISSN":["2405-6316"],"issn-type":[{"value":"2405-6316","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Three-dimensional deep learning nomogram for post-plan quality assurance in high-dose-rate prostate brachytherapy","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.2026.101100","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V. on behalf of European Society of Radiotherapy & Oncology.","name":"copyright","label":"Copyright"}],"article-number":"101100"}}