{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T11:38:05Z","timestamp":1765280285382},"reference-count":12,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T00:00:00Z","timestamp":1641427200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T00:00:00Z","timestamp":1641427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"mount sinai hospital-university health network academic medical organization innovation fund"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Radiat Oncol"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>High-quality radiotherapy (RT) planning for children and young adults with primary brain tumours is essential to minimize the risk of late treatment effects. The feasibility of using automated machine-learning (ML) to aid RT planning in this population has not previously been studied.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods and materials<\/jats:title>\n                <jats:p>We developed a ML model that identifies learned relationships between image features and expected dose in a training set of 95 patients with a primary brain tumour treated with focal radiotherapy to a dose of 54\u00a0Gy in 30 fractions. This ML method was then used to create predicted dose distributions for 15 previously-treated brain tumour patients across two institutions, as a testing set. Dosimetry to target volumes and organs-at-risk (OARs) were compared between the clinically-delivered (human-generated) plans versus the ML plans.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The ML method was able to create deliverable plans in all 15 patients in the testing set. All ML plans were generated within 30\u00a0min of initiating planning. Planning target volume coverage with 95% of the prescription dose was attained in all plans. OAR doses were similar across most structures evaluated; mean doses to brain and left temporal lobe were lower in ML plans than manual plans (mean difference to left temporal, \u2013\u20092.3\u00a0Gy, <jats:italic>p<\/jats:italic>\u2009=\u20090.006; mean differences to brain, \u2013\u20091.3\u00a0Gy, <jats:italic>p<\/jats:italic>\u2009=\u20090.017), whereas mean doses to right cochlea and lenses were higher in ML plans (+\u20091.6\u20132.2\u00a0Gy, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05 for each).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Use of an automated ML method to aid RT planning for children and young adults with primary brain tumours is dosimetrically feasible and can be successfully used to create high-quality 54\u00a0Gy RT plans. Further evaluation after clinical implementation is planned.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13014-021-01967-3","type":"journal-article","created":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T11:02:44Z","timestamp":1641466964000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A pilot study of machine-learning based automated planning for primary brain tumours"],"prefix":"10.1186","volume":"17","author":[{"given":"Derek S.","family":"Tsang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Grace","family":"Tsui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris","family":"McIntosh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Purdie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Glenn","family":"Bauman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hitesh","family":"Dama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Normand","family":"Laperriere","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barbara-Ann","family":"Millar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David B.","family":"Shultz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sameera","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Khandwala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David C.","family":"Hodgson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,6]]},"reference":[{"key":"1967_CR1","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.semradonc.2009.09.005","volume":"20","author":"TE Merchant","year":"2010","unstructured":"Merchant TE, Pollack IF, Loeffler JS. Brain tumors across the age spectrum: biology, therapy, and late effects. Semin Radiat Oncol. 2010;20:58\u201366.","journal-title":"Semin Radiat Oncol"},{"key":"1967_CR2","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.ijrobp.2016.10.005","volume":"97","author":"N Li","year":"2017","unstructured":"Li N, et al. Highly efficient training, refinement, and validation of a knowledge-based planning quality-control system for radiation therapy clinical trials. Int J Radiat Oncol Biol Phys. 2017;97:164\u201372.","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"1967_CR3","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.radonc.2018.06.023","volume":"128","author":"B Heijmen","year":"2018","unstructured":"Heijmen B, et al. Fully automated, multi-criterial planning for volumetric modulated arc therapy\u2014an international multi-center validation for prostate cancer. 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DST discloses that the institution has received Mount Sinai Hospital-University Health Network Academic Medical Organization Innovation Fund, in support of this work. CM has royalty bearing license of automated planning AI technology from RaySearch Laboratories, and patent \u201cMethod And System for Automated Quality Assurance And Automated Treatment Planning in Radiation Therapy\u201d. TP has received support from CIHR and NSERC, and has royalties from RaySearch Laboratories.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"3"}}