{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:27:32Z","timestamp":1792790852839,"version":"4.5.0"},"reference-count":10,"publisher":"IOP Publishing","issue":"16","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["P30CA008748"],"award-info":[{"award-number":["P30CA008748"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,8,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Purpose.<\/jats:italic>\n                    In high-dose-rate (HDR) prostate brachytherapy procedures, needle placement solely relies on physician experience. We investigated the feasibility of using reinforcement learning (RL) to provide needle positions and dwell times based on patient anatomy during pre-planning stage. This approach would reduce procedure time and ensure consistent plan quality.\n                    <jats:italic>Materials And Methods.<\/jats:italic>\n                    We trained an RL agent to adjust the position of one selected needle and all the dwell times on it to maximize a pre-defined reward function after observing the environment. The RL agent moves on to the next needle to adjust its location and dwell times until all needles are adjusted. Multiple rounds of process are played by the agent until the maximum number of rounds is reached. Plan data from 100 prostate HDR boost patients treated in our clinic were included in this study. Three RL models were trained on 5, 10 and 20 patients respectively, and the rest of 5 for evaluation, and 75 for testing. The dosimetry metrics and the number of used needles of RL plan were compared to those of the clinical results (ground truth).\n                    <jats:italic>Results.<\/jats:italic>\n                    On average, plans from all three models trained with different number of patients use 14 needles as clinical plans do, but reduces with statistical significance from clinical plans Rectum D2cc by 3.5%, Urethra D20% by 2%, and Prostate V150 by 5.5% at least when both plans are normalized to Prostate V100 = 95%. Meanwhile, all three RL plans trained on different number of patients have similar performance, with no statistically significant difference on Rectum D2cc and Urethra D20%.\n                    <jats:italic>Conclusion.<\/jats:italic>\n                    We present the first study demonstrating the feasibility of using RL to autonomously generate clinically acceptable HDR prostate brachytherapy plans. This RL-based method achieved equal or improved plan quality compared to conventional clinical approaches. With minimal data requirements and strong generalizability, this approach has substantial potential to standardize brachytherapy planning and reduce clinical variability.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae968b","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T22:57:11Z","timestamp":1786057031000},"page":"165008","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Automatic treatment planning using reinforcement learning for high-dose-rate prostate brachytherapy"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9021-1204","authenticated-orcid":true,"given":"Tonghe","family":"Wang","sequence":"first","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8973-5139","authenticated-orcid":true,"given":"Joel","family":"Beaudry","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Aramburu N\u00fa\u00f1ez","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pretesh","family":"Patel","sequence":"additional","affiliation":[{"name":"Emory University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Gorovets","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marisa","family":"Kollmeier","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio L","family":"Damato","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9023-5855","authenticated-orcid":true,"given":"Xiaofeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Emory University"},{"name":"Georgia Institute of Technology and Emory University School of Medicine"},{"name":"Emory University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,8,19]]},"reference":[{"key":"pmbae968bbib1","doi-asserted-by":"publisher","first-page":"258","DOI":"10.5114\/jcb.2015.53525","type":"journal-article","article-title":"Comparison of permanent (125)I seeds implants with two different techniques in 500 cases of prostate cancer","volume":"7","author":"Guinot","year":"2015","journal-title":"J. Contemp. Brachyther."},{"key":"pmbae968bbib2","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2024.110345","type":"journal-article","article-title":"A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy","volume":"197","author":"Hurkmans","year":"2024","journal-title":"Radiother. Oncol."},{"key":"pmbae968bbib3","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1001\/jama.2018.0587","type":"journal-article","article-title":"Radical prostatectomy, external beam radiotherapy, or external beam radiotherapy with brachytherapy boost and disease progression and mortality in patients with Gleason score 9-10 prostate cancer","volume":"319","author":"Kishan","year":"2018","journal-title":"JAMA"},{"key":"pmbae968bbib4","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1016\/S0360-3016(03)00627-8","type":"journal-article","article-title":"Biochemical outcomes after prostate brachytherapy with 5-year minimal follow-up: importance of patient selection and implant quality","volume":"57","author":"Kollmeier","year":"2003","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbae968bbib5","doi-asserted-by":"publisher","first-page":"3194","DOI":"10.1002\/mp.13577","type":"journal-article","article-title":"Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net","volume":"46","author":"Lei","year":"2019","journal-title":"Med. Phys."},{"key":"pmbae968bbib6","doi-asserted-by":"publisher","first-page":"7261","DOI":"10.1002\/mp.15206","type":"journal-article","article-title":"Catheter position prediction using deep-learning-based multi-atlas registration for high-dose rate prostate brachytherapy","volume":"48","author":"Lei","year":"2021a","journal-title":"Med. Phys."},{"key":"pmbae968bbib7","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1002\/mp.14895","type":"journal-article","article-title":"Male pelvic multi\u2010organ segmentation on transrectal ultrasound using anchor\u2010free mask CNN","volume":"48","author":"Lei","year":"2021b","journal-title":"Med. Phys."},{"key":"pmbae968bbib8","article-title":"Proximal policy optimization algorithms","author":"Schulman","year":"2017","type":"preprint"},{"key":"pmbae968bbib9","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abd00e","type":"journal-article","article-title":"Simultaneous needle catheter selection and dwell time optimization for preplanning of high-dose-rate brachytherapy of prostate cancer","volume":"66","author":"Wang","year":"2021","journal-title":"Phys. Med. Biol."},{"key":"pmbae968bbib10","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.brachy.2024.10.009","type":"journal-article","article-title":"Instant plan quality prediction on transrectal ultrasound for high-dose-rate prostate brachytherapy","volume":"24","author":"Wang","year":"2025","journal-title":"Brachytherapy"}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae968b","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae968b\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae968b","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae968b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae968b\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae968b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae968b\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae968b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T06:21:16Z","timestamp":1787120476000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae968b"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,19]]},"references-count":10,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8,19]]},"published-print":{"date-parts":[[2026,8,28]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/ae968b","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,19]]},"assertion":[{"value":"Automatic treatment planning using reinforcement learning for high-dose-rate prostate brachytherapy","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2026 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-03-27","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-06","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-19","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}