{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:28:00Z","timestamp":1769696880761,"version":"3.49.0"},"reference-count":15,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>A knowledge\u2010based planning technique is developed based on Bayesian stochastic frontier analysis. A novel missing data management is applied in order to handle missing organs\u2010at\u2010risk and work with a complete\u00a0dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Geometric metrics are used to predict DVH metrics for lung SBRT with a retrospective database of 299 patients. In total, 16 DVH metrics were predicted for the main bronchus, heart, esophagus, spinal cord PRV, great vessels, and chest wall. The predictive model is tested on a test group of 50\u00a0patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Mean difference between the observed and predicted values ranges between 1.5 \u00b1 1.9 Gy and 4.9 \u00b1 5.3 Gy for the spinal cord PRV D0.35cc and the main bronchus D0.035cc,\u00a0respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The missing data model implanted in the predictive model is robust in the estimation of the parameters. Bayesian stochastic frontier analysis with missing data management can be used to predict DVH metrics for lung SBRT treatment\u00a0planning.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15922","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T11:28:32Z","timestamp":1660130912000},"page":"6293-6302","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Knowledge\u2010based planning algorithm for lung SBRT with robust Bayesian stochastic frontier analysis and missing data management"],"prefix":"10.1002","volume":"49","author":[{"given":"Angelika","family":"Kroshko","sequence":"first","affiliation":[{"name":"Service de physique m\u00e9dicale et de radioprotection Centre int\u00e9gr\u00e9 de canc\u00e9rologie, CHU de Qu\u00e9bec \u2013 Universit\u00e9 Laval  1401 18e rue Quebec QC G1J1Z4 Canada"},{"name":"D\u00e9partement de Physique de G\u00e9nie Physique et d'Optique Universit\u00e9 Laval  Quebec QC Canada"},{"name":"Centre de recherche sur le cancer Universit\u00e9 Laval  Qu\u00e9bec QC Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivier","family":"Morin","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of California San Francisco  San Francisco California USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Louis","family":"Archambault","sequence":"additional","affiliation":[{"name":"Service de physique m\u00e9dicale et de radioprotection Centre int\u00e9gr\u00e9 de canc\u00e9rologie, CHU de Qu\u00e9bec \u2013 Universit\u00e9 Laval  1401 18e rue Quebec QC G1J1Z4 Canada"},{"name":"D\u00e9partement de Physique de G\u00e9nie Physique et d'Optique Universit\u00e9 Laval  Quebec QC Canada"},{"name":"Centre de recherche sur le cancer Universit\u00e9 Laval  Qu\u00e9bec QC 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