{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T12:46:47Z","timestamp":1791809207558,"version":"4.3.4"},"reference-count":22,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"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","thegreenjournal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Radiotherapy and Oncology"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.radonc.2026.111782","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T06:15:25Z","timestamp":1789452925000},"page":"111782","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Applying a pretrained DL-based IMPT planning workflow in an external center: A feasibility study in oropharyngeal cancer"],"prefix":"10.1016","volume":"224","author":[{"given":"Ilse G.","family":"van Bruggen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne Lisa","family":"Wolf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1346-2277","authenticated-orcid":false,"given":"Michiel","family":"Kroesen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koen","family":"Crama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minke J.","family":"Brinkman-Akker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes A.","family":"Langendijk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erik W.","family":"Korevaar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Both","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.radonc.2026.111782_b0005","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1002\/mp.16758","article-title":"Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy","volume":"51","author":"Zhang","year":"2024","journal-title":"Med Phys"},{"key":"10.1016\/j.radonc.2026.111782_b0010","doi-asserted-by":"crossref","first-page":"3286","DOI":"10.1002\/mp.14215","article-title":"Automating proton treatment planning with beam angle selection using Bayesian optimization","volume":"47","author":"Taasti","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.radonc.2026.111782_b0015","doi-asserted-by":"crossref","first-page":"5451","DOI":"10.1002\/mp.15702","article-title":"Feasibility study of fast intensity-modulated proton therapy dose prediction method using deep neural networks for prostate cancer","volume":"49","author":"Wang","year":"2022","journal-title":"Med Phys"},{"key":"10.1016\/j.radonc.2026.111782_b0020","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.14065","article-title":"Automated treatment planning for proton pencil beam scanning using deep learning dose prediction and dose-mimicking optimization","volume":"24","author":"Maes","year":"2023","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.radonc.2026.111782_b0025","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1002\/acm2.13350","article-title":"Clinical implementation of automated treatment planning for whole-brain radiotherapy","volume":"22","author":"Han","year":"2021","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.radonc.2026.111782_b0030","article-title":"Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer","volume":"28","author":"Bakx","year":"2023","journal-title":"Phys Imaging Radiat Oncol"},{"issue":"6","key":"10.1016\/j.radonc.2026.111782_b0035","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1038\/s41591-021-01359-w","article-title":"Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer","volume":"27","author":"McIntosh","year":"2021","journal-title":"Nat Med"},{"key":"10.1016\/j.radonc.2026.111782_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2024.110522","article-title":"Clinical implementation of deep learning robust IMPT planning in oropharyngeal cancer patients: a blinded clinical study","volume":"200","author":"van Bruggen","year":"2024","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2026.111782_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.clon.2024.08.005","article-title":"Artificial intelligence for radiation treatment planning: bridging gaps from retrospective promise to clinical reality","volume":"37","author":"Conroy","year":"2025","journal-title":"Clin Oncol"},{"key":"10.1016\/j.radonc.2026.111782_b0050","doi-asserted-by":"crossref","first-page":"e49","DOI":"10.12771\/emj.2024.e49","article-title":"Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review","volume":"47","author":"Jeong","year":"2024","journal-title":"Ewha Med J"},{"key":"10.1016\/j.radonc.2026.111782_b0055","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ac678a","article-title":"Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency","volume":"67","author":"Barragan-Montero","year":"2022","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.radonc.2026.111782_b0060","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.semradonc.2022.06.011","article-title":"Quality assurance for AI-based applications in radiation therapy","volume":"32","author":"Claessens","year":"2022","journal-title":"Semin Radiat Oncol"},{"key":"10.1016\/j.radonc.2026.111782_b0065","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.radonc.2020.09.008","article-title":"Overview of artificial intelligence-based applications in radiotherapy: recommendations for implementation and quality assurance","volume":"153","author":"Vandewinckele","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2026.111782_b0070","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.phro.2020.11.002","article-title":"Machine learning applications in radiation oncology: current use and needs to support clinical implementation","volume":"16","author":"Brouwer","year":"2020","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.radonc.2026.111782_b0075","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.radonc.2020.10.027","article-title":"Dose prediction with deep learning for prostate cancer radiation therapy: model adaptation to different treatment planning practices","volume":"153","author":"Kandalan","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2026.111782_b0080","article-title":"A multi-centre evaluation of deep learning based radiotherapy planning for left-sided node-negative breast cancer","volume":"36","author":"Besouw","year":"2025","journal-title":"Phys Imaging Radiat Oncol"},{"key":"10.1016\/j.radonc.2026.111782_b0085","doi-asserted-by":"crossref","first-page":"e209","DOI":"10.1016\/j.clon.2024.03.022","article-title":"Multi-center dose prediction using attention-aware deep learning algorithm based on transformers for cervical cancer radiotherapy","volume":"36","author":"Wu","year":"2024","journal-title":"Clin Oncol"},{"key":"10.1016\/j.radonc.2026.111782_b0090","doi-asserted-by":"crossref","first-page":"4480","DOI":"10.1002\/mp.16408","article-title":"Machine learning-based automatic proton therapy planning: impact of post-processing and dose-mimicking in plan robustness","volume":"50","author":"Borderias-Villarroel","year":"2023","journal-title":"Med Phys"},{"key":"10.1016\/j.radonc.2026.111782_b0095","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.radonc.2019.08.005","article-title":"Practical robustness evaluation in radiotherapy - a photon and proton-proof alternative to PTV-based plan evaluation","volume":"141","author":"Korevaar","year":"2019","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2026.111782_b0100","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.zemedi.2017.06.003","article-title":"Modeling tumor control probability for spatially inhomogeneous risk of failure based on clinical outcome data","volume":"27","author":"L\u00fchr","year":"2017","journal-title":"Z Med Phys"},{"key":"10.1016\/j.radonc.2026.111782_b0105","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.radonc.2020.09.033","article-title":"Radiotherapy treatment plannINg study guidelines (RATING): a framework for setting up and reporting on scientific treatment planning studies","volume":"153","author":"Hansen","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2026.111782_b0110","doi-asserted-by":"crossref","first-page":"354","DOI":"10.14338\/IJPT-20-00089.1","article-title":"National protocol for model-based selection for proton therapy in head and neck cancer","volume":"8","author":"Langendijk","year":"2021","journal-title":"Int J Part Ther"}],"container-title":["Radiotherapy and Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167814026006225?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167814026006225?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T12:08:18Z","timestamp":1791806898000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167814026006225"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":22,"alternative-id":["S0167814026006225"],"URL":"https:\/\/doi.org\/10.1016\/j.radonc.2026.111782","relation":{},"ISSN":["0167-8140"],"issn-type":[{"value":"0167-8140","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Applying a pretrained DL-based IMPT planning workflow in an external center: A feasibility study in oropharyngeal cancer","name":"articletitle","label":"Article Title"},{"value":"Radiotherapy and Oncology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.radonc.2026.111782","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.","name":"copyright","label":"Copyright"}],"article-number":"111782"}}