{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T13:42:17Z","timestamp":1788183737842,"version":"build-2803163510"},"reference-count":29,"publisher":"IOP Publishing","issue":"12","license":[{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2025,6,22]]},"abstract":"Abstract<\/jats:title>\n \n Objective.<\/jats:italic> In high-dose-rate prostate brachytherapy, uncertainties are likely to cause a deviation from the nominal treatment plan, potentially leading to failure in achieving clinical objectives. Robust optimisation has the potential to maximise the probability that objectives are met during treatment despite these uncertainties. Approach.<\/jats:italic> A probabilistic robust optimiser that incorporating fourteen major uncertainty sources was developed and evaluated on 49 patients. Three objective functions were maximised to generate the approximate Pareto front of 200 robust-optimised plans, approximating the robustness of: (1) The minimum dose to the hottest 90% of the prostate (\n \n \n \n \n D<\/mml:mi>\n \n 90<\/mml:mn>\n <\/mml:mrow>\n \n P<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:msubsup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>), (2) The maximum dose to the urethra (\n \n \n \n \n D<\/mml:mi>\n \n 0.01<\/mml:mn>\n \n \u2009cc<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:mrow>\n \n U<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:msubsup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>), and (3) The maximum dose to the rectum (\n \n \n \n \n D<\/mml:mi>\n \n \n 0<\/mml:mtext>\n <\/mml:mrow>\n \n .1 cc<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:mrow>\n \n R<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:msubsup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>). Plans were then robustly evaluated using 1000 uncertainty scenarios each simulating a possible deviation from the planned treatment. The percentage of scenarios meeting the \n \n \n \n \n D<\/mml:mi>\n \n 90<\/mml:mn>\n <\/mml:mrow>\n \n P<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:msubsup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>, \n \n \n \n \n D<\/mml:mi>\n \n 0.01<\/mml:mn>\n \n \u2009cc<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:mrow>\n \n U<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:msubsup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>, and \n \n \n \n \n D<\/mml:mi>\n \n 0.1<\/mml:mn>\n \n \u2009cc<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:mrow>\n \n R<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:msubsup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula> metrics were determined, along with the overall pass rate, defined as the percentage of scenarios meting all three metrics simultaneously. These pass-rates, along with nominal metrics, were, were used to select the best robust-optimised plan. A radiation oncologist evaluated the best robust-optimised plans against the treatment planning system (TPS)-optimised plan for ten patients. The same selection criteria were then applied to a further cohort of 39 patients and the same plan comparisons performed. Main results<\/jats:italic>. All best robust-optimised plans had higher overall pass-rates (mean: 50.7 \u00b1 1.5%, SD: 14.2%) then TPS-optimised plans (mean: 32.0 \u00b1 1.5%, SD: 12.3%). The mean \n \n \n \n \n D<\/mml:mi>\n \n 0.01<\/mml:mn>\n \n \u2009cc<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:mrow>\n \n U<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:msubsup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula> pass-rate was 66.0 \u00b1 1.3% (SD: 12.1) for the robust-optimised plans compared with 47.2 \u00b1 1.3% (SD: 9.3%) for TPS-optimised plans. The \n \n \n \n \n D<\/mml:mi>\n \n 90<\/mml:mn>\n <\/mml:mrow>\n \n P<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:msubsup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula> pass-rates was higher for robust-optimised plans (mean: 85.6 \u00b1 1.1%, SD: 9.5%) then TPS-optimised (mean: 82.2 \u00b1 1.1%, SD: 13.8%) in 36 patients. \n \n \n \n \n D<\/mml:mi>\n \n 0.1<\/mml:mn>\n \n \u2009cc<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:mrow>\n \n R<\/mml:mtext>\n <\/mml:mrow>\n <\/mml:msubsup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula> pass-rates remained consistently high for both optimisation methods. Significance<\/jats:italic>. The robust optimisation algorithm generated plans with greater robustness than the TPS-optimised plans for nine out of ten patients evaluated by a radiation oncologist, in an average algorithm runtime of 1-minute-49 s.<\/jats:p>","DOI":"10.1088\/1361-6560\/addf0b","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T09:21:45Z","timestamp":1748856105000},"page":"125006","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["A robust optimisation genetic algorithm for HDR prostate brachytherapy including all major uncertainties"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3053-6052","authenticated-orcid":true,"given":"Andrew C","family":"Kennedy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3862-2644","authenticated-orcid":true,"given":"Michael J J","family":"Douglass","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raghavendra V","family":"Gowda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8482-6982","authenticated-orcid":false,"given":"Alexandre M C","family":"Santos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"pmbaddf0bbib1","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.ijrobp.2005.02.015","article-title":"Acute genitourinary toxicity after high dose rate (HDR) brachytherapy combined with hypofractionated external-beam radiation therapy for localized prostate cancer Second analysis to determine the correlation between the urethral dose in HDR brachytherapy and the severity of acute genitourinary toxicity","volume":"63","author":"Akimoto","year":"2005","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbaddf0bbib2","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1287\/ijoc.2018.0815","article-title":"Robust optimization of dose-volume metrics for prostate HDR-brachytherapy incorporating target and oar volume delineation uncertainties","volume":"31","author":"Balvert","year":"2019","journal-title":"INFORMS J. Comput."},{"key":"pmbaddf0bbib3","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.brachy.2022.11.013","article-title":"Towards artificial intelligence-based automated treatment planning in clinical practice A prospective study of the first clinical experiences in high-dose-rate prostate brachytherapy","volume":"22","author":"Barten","year":"2023","journal-title":"Brachytherapy"},{"key":"pmbaddf0bbib4","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab1817","article-title":"A GPU-based multi-criteria optimization algorithm for HDR brachytherapy","volume":"64","author":"B\u00e9langer","year":"2019","journal-title":"Phys. Med. Biol."},{"key":"pmbaddf0bbib5","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.procs.2015.05.225","article-title":"Optimised robust treatment plans for prostate cancer focal brachytherapy","volume":"51","author":"Betts","year":"2015","journal-title":"Proc. Comput. Sci."},{"key":"pmbaddf0bbib6","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1016\/j.canrad.2020.06.016","article-title":"Time of PTV is ending, robust optimization comes next","volume":"24","author":"Biston","year":"2020","journal-title":"Cancer\/Radioth\u00e9rapie"},{"key":"pmbaddf0bbib7","doi-asserted-by":"publisher","first-page":"7565","DOI":"10.1002\/mp.15249","article-title":"Robust optimization should be used to replace PTV in radiotherapy treatment planning","volume":"48","author":"Biston","year":"2021","journal-title":"Med. Phys."},{"key":"pmbaddf0bbib8","doi-asserted-by":"publisher","first-page":"89497","DOI":"10.1109\/ACCESS.2020.2990567","article-title":"Pymoo multi-objective optimization in python","volume":"8","author":"Blank","year":"2020","journal-title":"IEEE Access"},{"key":"pmbaddf0bbib9","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab44ff","article-title":"Fast automated multi-criteria planning for HDR brachytherapy explored for prostate cancer","volume":"64","author":"Breedveld","year":"2019","journal-title":"Phys. Med. Biol."},{"key":"pmbaddf0bbib10","doi-asserted-by":"publisher","first-page":"2746","DOI":"10.1002\/mp.14132","article-title":"Accelerated robust optimization algorithm for proton therapy treatment planning","volume":"47","author":"Buti","year":"2020","journal-title":"Med. Phys."},{"key":"pmbaddf0bbib11","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"pmbaddf0bbib12","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abe04e","article-title":"Robust optimization for HDR prostate brachytherapy applied to organ reconstruction uncertainty","volume":"66","author":"der Meer Mc","year":"2021","journal-title":"Phys. Med. Biol."},{"key":"pmbaddf0bbib13","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1002\/mp.16804","article-title":"Robust stochastic optimization of needle configurations for robotic HDR prostate brachytherapy","volume":"51","author":"Gerlach","year":"2024","journal-title":"Med. Phys."},{"key":"pmbaddf0bbib14","doi-asserted-by":"publisher","first-page":"15326","DOI":"10.1016\/j.radonc.2020.09.038","article-title":"What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans","volume":"153","author":"Hernandez","year":"2020","journal-title":"Radiother. Oncol."},{"key":"pmbaddf0bbib15","author":"International Commission on Radiation Units and Measurements","year":"1999"},{"key":"pmbaddf0bbib16","doi-asserted-by":"publisher","first-page":"282","DOI":"10.5114\/jcb.2014.45586","article-title":"The impact of activating source dwell positions outside the CTV on the dose to treated normal tissue volumes in TRUS guided 3D conformal interstitial HDR brachytherapy of prostate cancer","volume":"6","author":"Karlsson","year":"2014","journal-title":"J. Contemp. Brachytherapy"},{"key":"pmbaddf0bbib17","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1007\/s13246-023-01279-8","article-title":"Being certain about uncertainties a robust evaluation method for high-dose-rate prostate brachytherapy treatment plans including the combination of uncertainties","volume":"46","author":"Kennedy","year":"2023a","journal-title":"Phys. Eng. Sci. Med."},{"key":"pmbaddf0bbib18","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.14182","article-title":"A robust evaluation of 49 high-dose-rate prostate brachytherapy treatment plans including all major uncertainties","volume":"25","author":"Kennedy","year":"2023b","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"pmbaddf0bbib19","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.10675215","article-title":"DrAndrewKennedy\/RobustBrachy v1.0","author":"Kennedy","year":"2024"},{"key":"pmbaddf0bbib20","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.radonc.2013.11.002","article-title":"Review of clinical brachytherapy uncertainties Analysis guidelines of GEC-ESTRO and the AAPM","volume":"110","author":"Kirisits","year":"2014","journal-title":"Radiother. Oncol."},{"key":"pmbaddf0bbib21","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.radonc.2019.08.005","article-title":"Practical robustness evaluation in radiotherapy\u2014a photon and proton-proof alternative to PTV-based plan evaluation","volume":"141","author":"Korevaar","year":"2019","journal-title":"Radiother. Oncol."},{"key":"pmbaddf0bbib22","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1002\/mp.14762","article-title":"Optimization in treatment planning of high dose-rate brachytherapy\u2014review and analysis of mathematical models","volume":"48","author":"Mor\u00e9n","year":"2021","journal-title":"Med. Phys."},{"key":"pmbaddf0bbib23","article-title":"CuPy A NumPy-compatible library for NVIDIA GPU calculations","author":"Okuta","year":"2017"},{"key":"pmbaddf0bbib24","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1016\/j.radonc.2010.08.021","article-title":"PTV margins should not be used to compensate for uncertainties in 3D image guided intracavitary brachytherapy","volume":"97","author":"Tanderup","year":"2010","journal-title":"Radiother. Oncol."},{"key":"pmbaddf0bbib25","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1088\/1361-6560\/aae659","article-title":"Robust radiotherapy planning","volume":"63","author":"Unkelbach","year":"2018","journal-title":"Phys. Med. Biol."},{"key":"pmbaddf0bbib26","first-page":"441","article-title":"Robust evolutionary bi-objective optimization for prostate cancer treatment with high-dose-rate brachytherapy","author":"van der Meer","year":"2020"},{"key":"pmbaddf0bbib27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13014-019-1335-8","article-title":"Statistical evaluation of worst-case robust optimization intensity-modulated proton therapy plans using an exhaustive sampling approach","volume":"14","author":"Yang","year":"2019","journal-title":"Radiat. Oncol."},{"key":"pmbaddf0bbib28","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.prro.2018.12.002","article-title":"Robustness analysis for external beam radiation therapy treatment plans describing uncertainty scenarios and reporting their dosimetric consequences","volume":"9","author":"Yock","year":"2019","journal-title":"Pract. Radiat. Oncol."},{"key":"pmbaddf0bbib29","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1002\/acm2.12291","article-title":"Robust optimization in lung treatment plans accounting for geometric uncertainty","volume":"19","author":"Zhang","year":"2018","journal-title":"J. Appl. Clin. Med. Phys."}],"container-title":["Physics in Medicine & Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/addf0b","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/addf0b\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/addf0b","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/addf0b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/addf0b\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/addf0b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/addf0b\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/addf0b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T08:53:58Z","timestamp":1749545638000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/addf0b"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,10]]},"references-count":29,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,6,10]]},"published-print":{"date-parts":[[2025,6,22]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/addf0b","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,10]]},"assertion":[{"value":"A robust optimisation genetic algorithm for HDR prostate brachytherapy including all major uncertainties","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 2025 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2024-12-05","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-05-30","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-06-10","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}