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Med. Biol."],"published-print":{"date-parts":[[2024,6,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Treatment plan optimization in high dose rate brachytherapy often requires manual fine-tuning of penalty weights for each objective, which can be time-consuming and dependent on the planner's experience. To automate this process, this study used a multi-criteria approach called multi-objective Bayesian optimization with q-noisy expected hypervolume improvement as its acquisition function (MOBO-qNEHVI).\n                    <jats:italic>Approach.<\/jats:italic>\n                    The treatment plans of 13 prostate cancer patients were retrospectively imported to a research treatment planning system, RapidBrachyMTPS, where fast mixed integer optimization (FMIO) performs dwell time optimization given a set of penalty weights to deliver 15 Gy to the target volume. MOBO-qNEHVI was used to find patient-specific Pareto optimal penalty weight vectors that yield clinically acceptable dose volume histogram metrics. The relationship between the number of MOBO-qNEHVI iterations and the number of clinically acceptable plans per patient (acceptance rate) was investigated. The performance time was obtained for various parameter configurations.\n                    <jats:italic>Main results.<\/jats:italic>\n                    MOBO-qNEHVI found clinically acceptable treatment plans for all patients. With increasing the number of MOBO-qNEHVI iterations, the acceptance rate grew logarithmically while the performance time grew exponentially. Fixing the penalty weight of the tumour volume to maximum value, adding the target dose as a parameter, initiating MOBO-qNEHVI with 25 parallel sampling of FMIO, and running 6 MOBO-qNEHVI iterations found solutions that delivered 15 Gy to the hottest 95% of the clinical target volume while respecting the dose constraints to the organs at risk. The average acceptance rate for each patient was 89.74% \u00b1 8.11%, and performance time was 66.6 \u00b1 12.6 s. The initiation took 22.47 \u00b1 7.57 s, and each iteration took 7.35 \u00b1 2.45 s to find one Pareto solution.\n                    <jats:italic>Significance.<\/jats:italic>\n                    MOBO-qNEHVI combined with FMIO can automatically explore the trade-offs between treatment plan objectives in a patient specific manner within a minute. This approach can reduce the dependency of plan quality on planner\u2019s experience and reduce dose to the organs at risk.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ad4448","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T18:43:03Z","timestamp":1714156983000},"page":"115024","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization"],"prefix":"10.1088","volume":"69","author":[{"given":"Hossein","family":"Jafarzadeh","sequence":"first","affiliation":[{"name":"McGill University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majd","family":"Antaki","sequence":"additional","affiliation":[{"name":"McGill University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4488-8796","authenticated-orcid":true,"given":"Ximeng","family":"Mao","sequence":"additional","affiliation":[{"name":"mila\u2014Quebec AI Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie","family":"Duclos","sequence":"additional","affiliation":[{"name":"McGill University Health Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farhard","family":"Maleki","sequence":"additional","affiliation":[{"name":"University of Calgary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirin A","family":"Enger","sequence":"additional","affiliation":[{"name":"McGill University"},{"name":"mila\u2014Quebec AI Institute"},{"name":"Lady Davis Institute for Medical Research, Montreal, Quebec,"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,5,21]]},"reference":[{"key":"pmbad4448bib1","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/S0168-9002(03)01368-8","type":"journal-article","article-title":"Geant4-a simulation toolkit","volume":"506","author":"Agostinelli","year":"2003","journal-title":"Nucl. 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