{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T17:04:53Z","timestamp":1789491893057,"version":"build-2803163510"},"reference-count":45,"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":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002661","name":"Fund for Scientific Research","doi-asserted-by":"publisher","award":["7.6513.25"],"award-info":[{"award-number":["7.6513.25"]}],"id":[{"id":"10.13039\/501100002661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002661","name":"Fund for Scientific Research","doi-asserted-by":"publisher","award":["7.4583.25"],"award-info":[{"award-number":["7.4583.25"]}],"id":[{"id":"10.13039\/501100002661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018985","name":"Waalse Gewest","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018985","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Particle Therapy"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.ijpt.2026.101952","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T15:12:39Z","timestamp":1784733159000},"page":"101952","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Patient-Specific Deep Reinforcement Learning for Proton Beam Delivery Under Inter-Phase Variations"],"prefix":"10.1016","volume":"21","author":[{"given":"M\u00e9lanie","family":"Ghislain","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Estelle","family":"Lo\u00ffen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antoine","family":"Aspeel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damien","family":"Dasnoy-Sumell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Romain","family":"Schyns","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana Maria","family":"Barragan Montero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benoit","family":"Macq","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ijpt.2026.101952_bib1","doi-asserted-by":"crossref","first-page":"5169","DOI":"10.1118\/1.4737113","article-title":"A characterization of robust radiation therapy treatment planning methods\u2014from expected value to worst case optimization","volume":"39","author":"Fredriksson","year":"2012","journal-title":"Med Phys"},{"key":"10.1016\/j.ijpt.2026.101952_bib2","doi-asserted-by":"crossref","unstructured":"D. Pross, S. Wuyckens, S. Deffet, E. Sterpin, J.A. Lee, K. Souris, Beamlet-free optimization for Monte Carlo based treatment planning in proton therapy, (2023). arXiv 10.48550\/arXiv.2304.08105.","DOI":"10.1002\/mp.16813"},{"key":"10.1016\/j.ijpt.2026.101952_bib3","doi-asserted-by":"crossref","first-page":"5434","DOI":"10.1002\/mp.13850","article-title":"Towards fast and robust 4d optimization for moving tumors with scanned proton therapy","volume":"46","author":"Buti","year":"2019","journal-title":"Med Phys"},{"issue":"3","key":"10.1016\/j.ijpt.2026.101952_bib4","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.ijrobp.2015.06.019","article-title":"Reducing dose uncertainty for spot-scanning proton beam therapy of moving tumors by optimizing the spot delivery sequence","volume":"93","author":"Li","year":"2015","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.ijpt.2026.101952_bib5","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1186\/s13014-022-02055-w","article-title":"Developing an accurate model of spot-scanning treatment delivery time and sequence for a compact superconducting synchrocyclotron proton therapy system","volume":"17","author":"Zhao","year":"2022","journal-title":"Radiat Oncol"},{"issue":"4","key":"10.1016\/j.ijpt.2026.101952_bib6","doi-asserted-by":"crossref","first-page":"2540","DOI":"10.1002\/mp.16209","article-title":"Optimization of proton pencil beam positioning in collimated fields","volume":"50","author":"Behrends","year":"2023","journal-title":"Med Phys"},{"issue":"2","key":"10.1016\/j.ijpt.2026.101952_bib7","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.semradonc.2012.11.003","article-title":"Physics controversies in proton therapy","volume":"23","author":"Engelsman","year":"2013","journal-title":"Semin Radiat Oncol"},{"key":"10.1016\/j.ijpt.2026.101952_bib8","doi-asserted-by":"crossref","DOI":"10.1118\/1.4885956","article-title":"Implication of spot position error on plan quality and patient safety in pencil-beam-scanning proton therapy","volume":"41","author":"Yu","year":"2014","journal-title":"Med Phys"},{"issue":"3","key":"10.1016\/j.ijpt.2026.101952_bib9","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.semradonc.2019.02.005","article-title":"See, think, and act: real-time adaptive radiotherapy","volume":"29","author":"Keall","year":"2019","journal-title":"Semin Radiat Oncol"},{"key":"10.1016\/j.ijpt.2026.101952_bib10","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1016\/j.clon.2020.09.008","article-title":"Image-guided radiotherapy to manage respiratory motion: lung and liver","volume":"32","author":"Dhont","year":"2020","journal-title":"Clin Oncol"},{"key":"10.1016\/j.ijpt.2026.101952_bib11","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s13014-020-01530-6","article-title":"Reliability of itv approach to varying treatment fraction time: a retrospective analysis based on 2d cine mr images","volume":"15","author":"Cusumano","year":"2020","journal-title":"Radiat Oncol"},{"issue":"4","key":"10.1016\/j.ijpt.2026.101952_bib12","doi-asserted-by":"crossref","first-page":"232","DOI":"10.3857\/roj.2019.00633","article-title":"Current status of proton therapy techniques for lung cancer","volume":"37","author":"Han","year":"2019","journal-title":"Radiat Oncol J"},{"key":"10.1016\/j.ijpt.2026.101952_bib13","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.ejmp.2019.02.006","article-title":"Real-time tumor tracking using fluoroscopic imaging with deep neural network analysis","volume":"59","author":"Hirai","year":"2019","journal-title":"Phys Med"},{"key":"10.1016\/j.ijpt.2026.101952_bib14","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.3390\/bioengineering12111197","article-title":"Marker-less lung tumor tracking from real-time color x-ray fluoroscopic images using cross-patient deep learning model","volume":"12","author":"Yan","year":"2025","journal-title":"Bioengineering"},{"key":"10.1016\/j.ijpt.2026.101952_bib15","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.radonc.2022.02.031","article-title":"Magnetic resonance imaging (mri) guided proton therapy: a review of the clinical challenges, potential benefits and pathway to implementation","volume":"170","author":"Pham","year":"2022","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijpt.2026.101952_bib16","article-title":"Imaging, automation and workflow challenges for online adaptive proton therapy","volume":"36","author":"Wagenaar","year":"2025","journal-title":"Phys Imag Radiat Oncol"},{"issue":"9","key":"10.1016\/j.ijpt.2026.101952_bib17","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.3390\/cancers15092555","article-title":"Image-guided proton therapy: a comprehensive review","volume":"15","author":"Lane","year":"2023","journal-title":"Cancers"},{"key":"10.1016\/j.ijpt.2026.101952_bib18","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.3390\/cancers15041014","article-title":"Implementation of machine learning models to ensure radiotherapy quality for multicenter clinical trials","volume":"15","author":"Geng","year":"2023","journal-title":"Cancers"},{"key":"10.1016\/j.ijpt.2026.101952_bib19","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.ijpt.2026.101952_bib20","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1016\/j.ijrobp.2022.12.004","article-title":"Automated robust planning for impt in oropharyngeal cancer patients using machine learning","volume":"115","author":"van Bruggen","year":"2023","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.ijpt.2026.101952_bib21","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1159\/000492670","article-title":"Deep reinforcement learning in medicine","volume":"5","author":"Jonsson","year":"2019","journal-title":"Kidney Dis"},{"key":"10.1016\/j.ijpt.2026.101952_bib22","unstructured":"Y. Li, Deep reinforcement learning: an overview, (2018). arXiv 10.48550\/arXiv.1701.07274."},{"key":"10.1016\/j.ijpt.2026.101952_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102193","article-title":"Deep reinforcement learning in medical imaging: a literature review","volume":"73","author":"Zhou","year":"2021","journal-title":"Med Image Anal"},{"issue":"2","key":"10.1016\/j.ijpt.2026.101952_bib24","doi-asserted-by":"crossref","first-page":"214","DOI":"10.3390\/biomedicines9020214","article-title":"Reinforcement learning for radiotherapy dose fractioning automation","volume":"9","author":"Moreau","year":"2021","journal-title":"Biomedicines"},{"key":"10.1016\/j.ijpt.2026.101952_bib25","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1002\/mp.12625","article-title":"Deep reinforcement learning for automated radiation adaptation in lung cancer","volume":"44","author":"Tseng","year":"2017","journal-title":"Med Phys"},{"key":"10.1016\/j.ijpt.2026.101952_bib26","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1002\/mp.14712","article-title":"Improving efficiency of training a virtual treatment planner network via knowledge-guided deep reinforcement learning","volume":"48","author":"Shen","year":"2021","journal-title":"Med Phys"},{"key":"10.1016\/j.ijpt.2026.101952_bib27","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ac2bb5","article-title":"Graph neural networks and deep reinforcement learning for simultaneous beam orientation and trajectory optimization of cyberknife","volume":"66","author":"Kafaei","year":"2021","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.ijpt.2026.101952_bib28","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-02910-y","article-title":"Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy","volume":"11","author":"Niraula","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.ijpt.2026.101952_bib29","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-32032-6","article-title":"A clinical decision support system for AI assisted decision-making in response-adaptive radiotherapy (arclids)","volume":"13","author":"Niraula","year":"2023","journal-title":"Sci Rep"},{"key":"10.1016\/j.ijpt.2026.101952_bib30","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1002\/mp.17654","article-title":"Automating the optimization of proton pbs treatment planning using policy gradient deep reinforcement learning","volume":"52","author":"Wang","year":"2025","journal-title":"Med Phys"},{"key":"10.1016\/j.ijpt.2026.101952_bib31","unstructured":"M. Madondo, Y. Shao, Y. Liu, J. Zhou, X. Yang, Z. Tian, Patient-specific deep reinforcement learning for automatic replanning in head-and-neck cancer proton therapy, (2025). arXiv 10.48550\/arXiv.2506.10073."},{"key":"10.1016\/j.ijpt.2026.101952_bib32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejmp.2021.12.009","article-title":"Deep reinforcement learning for treatment planning in high-dose-rate cervical brachytherapy","volume":"94","author":"Pu","year":"2022","journal-title":"Phys Med"},{"key":"10.1016\/j.ijpt.2026.101952_bib33","series-title":"Reinforcement Learning: An Introduction","author":"Sutton","year":"1998"},{"key":"10.1016\/j.ijpt.2026.101952_bib34","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1561\/2200000071","article-title":"An introduction to deep reinforcement learning","volume":"11","author":"Fran\u00e7ois-Lavet","year":"2018","journal-title":"Found Trends Mach Learn"},{"key":"10.1016\/j.ijpt.2026.101952_bib35","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1287\/opre.26.2.282","article-title":"The optimal control of partially observable markov processes over the infinite horizon","volume":"26","author":"Sondik","year":"1978","journal-title":"Operat Res"},{"key":"10.1016\/j.ijpt.2026.101952_bib36","first-page":"1","article-title":"Stable-baselines3: reliable reinforcement learning implementations","volume":"22","author":"Raffin","year":"2021","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.ijpt.2026.101952_bib37","unstructured":"S. Wuyckens, D. Dasnoy, G. Janssens, V. Hamaide, M. Huet, Opentps - open-source treatment planning system for research in proton therapy, arXiv (2023)."},{"key":"10.1016\/j.ijpt.2026.101952_bib38","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.ejmp.2022.02.018","article-title":"Improved healthy tissue sparing in proton therapy of lung tumors using statistically sound robust optimization and evaluation","volume":"96","author":"Badiu","year":"2022","journal-title":"Phys Med"},{"key":"10.1016\/j.ijpt.2026.101952_bib39","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.phro.2020.06.004","article-title":"Online adaptive dose restoration in intensity modulated proton therapy of lung cancer to account for inter-fractional density changes","volume":"15","author":"Border\u00edas Villarroel","year":"2020","journal-title":"Phys Imag Radiat Oncol"},{"key":"10.1016\/j.ijpt.2026.101952_bib40","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1002\/acm2.12420","article-title":"Validation and clinical implementation of an accurate monte carlo code for pencil beam scanning proton therapy","volume":"19","author":"Huang","year":"2018","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.ijpt.2026.101952_bib41","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.1118\/1.4943377","article-title":"Fast multipurpose monte carlo simulation for proton therapy using multi- and many-core cpu architectures","volume":"43","author":"Souris","year":"2016","journal-title":"Med Phys"},{"key":"10.1016\/j.ijpt.2026.101952_bib42","first-page":"1","article-title":"Prescribing, recording, and reporting proton-beam therapy","volume":"7","year":"2007","journal-title":"ICRU Rep"},{"key":"10.1016\/j.ijpt.2026.101952_bib43","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.radonc.2020.02.025","article-title":"4d strategies for lung tumors treated with hypofractionated scanning proton beam therapy","volume":"146","author":"Mastella","year":"2020","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ijpt.2026.101952_bib44","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ad9dad","article-title":"Mrgrt real-time target localization using foundation models for contour point tracking and promptable mask refinement","volume":"70","author":"Bl\u00f6cker","year":"2025","journal-title":"Phys Med Biol"},{"key":"10.1016\/j.ijpt.2026.101952_bib45","article-title":"Gtv segmentation in mri guided radiotherapy with promptable foundation models","volume":"71","author":"Bl\u00f6cker","year":"2026","journal-title":"Phys Med Biol"}],"container-title":["International Journal of Particle Therapy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2331518026006554?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2331518026006554?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T16:28:55Z","timestamp":1789489735000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2331518026006554"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":45,"alternative-id":["S2331518026006554"],"URL":"https:\/\/doi.org\/10.1016\/j.ijpt.2026.101952","relation":{},"ISSN":["2331-5180"],"issn-type":[{"value":"2331-5180","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Patient-Specific Deep Reinforcement Learning for Proton Beam Delivery Under Inter-Phase Variations","name":"articletitle","label":"Article Title"},{"value":"International Journal of Particle Therapy","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijpt.2026.101952","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 Particle Therapy Co-operative Group.","name":"copyright","label":"Copyright"}],"article-number":"101952"}}