{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T12:31:49Z","timestamp":1785328309846,"version":"3.55.0"},"reference-count":20,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T00:00:00Z","timestamp":1650672000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To quantify the clinical performance of a machine learning (ML) algorithm for organ\u2010at\u2010risk (OAR) dose prediction for lung stereotactic body radiation therapy (SBRT) and estimate the treatment planning benefit from having upfront access to these dose predictions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>ML models were trained using multi\u2010center data consisting of 209 patients previously treated with lung SBRT. Two prescription levels were investigated, 50 Gy in five fractions and 54\u00a0Gy in three fractions. Models were generated using a gradient\u2010boosted regression tree algorithm using grid searching with fivefold cross\u2010validation. Twenty patients not included in the training set were used to test OAR dose prediction performance, ten for each prescription. We also performed blinded re\u2010planning based on OAR dose predictions but without access to clinically delivered plans. Differences between predicted and delivered doses were assessed by root\u2010mean square deviation (RMSD), and statistical differences between predicted, delivered, and re\u2010planned doses were evaluated with one\u2010way analysis of variance (ANOVA) tests.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>ANOVA tests showed no significant differences between predicted, delivered, and replanned OAR doses (all <jats:italic>p<\/jats:italic> \u2265 0.36). The RMSD was 2.9, 3.9, 4.3, and 1.7Gy for max dose to the spinal cord, great vessels, heart, and trachea, respectively, for 50 Gy in five fractions. Average improvements of 1.0, 1.4, and 2.0 Gy were seen for spinal cord, esophagus, and trachea max doses in blinded replans compared to clinically delivered plans with 54\u00a0Gy in three fractions, and 1.8, 0.7, and 1.5 Gy, respectively, for the esophagus, heart and bronchus max doses with 50 Gy in five fractions. Target coverage was similar with an average PTV V100% of 94.7% for delivered plans compared to 97.3% for blinded re\u2010plans for 50 Gy in five fractions, and respectively 98.4% versus 99.2% for 54\u00a0Gy in three fractions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study validated ML\u2010based OAR dose prediction for lung SBRT, showing potential for improved OAR dose sparing and more consistent plan quality using dose predictions for patient\u2010specific planning guidance.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.13609","type":"journal-article","created":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T07:40:35Z","timestamp":1650699635000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Organ\u2010at\u2010risk dose prediction using a machine learning algorithm: Clinical validation and treatment planning benefit for lung SBRT"],"prefix":"10.1002","volume":"23","author":[{"given":"N. Patrik","family":"Brodin","sequence":"first","affiliation":[{"name":"Institute for Onco\u2010Physics Albert Einstein College of Medicine  Bronx NY USA"},{"name":"Department of Radiation Oncology Montefiore Medical Center  Bronx NY USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leslie","family":"Schulte","sequence":"additional","affiliation":[{"name":"Siris Medical  Burlingame CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Velten","sequence":"additional","affiliation":[{"name":"Institute for Onco\u2010Physics Albert Einstein College of Medicine  Bronx NY USA"},{"name":"Department of Radiation Oncology Montefiore Medical Center  Bronx NY USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Martin","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Montefiore Medical Center  Bronx NY USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sydney","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Massachusetts General Hospital  Boston MA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Montefiore Medical Center  Bronx NY USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0589-2653","authenticated-orcid":false,"given":"Amar","family":"Basavatia","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Montefiore Medical Center  Bronx NY USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nitin","family":"Ohri","sequence":"additional","affiliation":[{"name":"Institute for Onco\u2010Physics Albert Einstein College of Medicine  Bronx NY USA"},{"name":"Department of Radiation Oncology Montefiore Medical Center  Bronx NY USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madhur K.","family":"Garg","sequence":"additional","affiliation":[{"name":"Institute for Onco\u2010Physics Albert Einstein College of Medicine  Bronx NY USA"},{"name":"Department of Radiation Oncology Montefiore Medical Center  Bronx NY USA"},{"name":"Department of Urology Montefiore Medical Center  Bronx NY USA"},{"name":"Department of Otorhinolaryngology\u2010Head &amp; 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