{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T23:23:57Z","timestamp":1792452237520,"version":"4.5.0"},"reference-count":36,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T00:00:00Z","timestamp":1677196800000},"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":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Knowledge\u2010based planning (KBP) and multicriteria optimization (MCO) are two powerful tools to assist treatment planners in achieving optimal target coverage and organ\u2010at\u2010risk (OAR) sparing. The purpose of this work is to investigate if integrating MCO with conventional KBP can further improve treatment plan quality for prostate cancer stereotactic body radiation therapy (SBRT). A two\u2010phase study was designed to investigate the impact of MCO and KBP in prostate SBRT treatment planning. The first phase involved the creation of a KBP model based on thirty clinical SBRT plans, generated by manual optimization (KBP_M). A ten\u2010patient validation cohort was used to compare manual, MCO, and KBP_M optimization techniques. The next phase involved replanning the original model cohort with additional tradeoff optimization via MCO to create a second model, KBP_MCO. Plans were then generated using linear integration (KBP_M+MCO), non\u2010linear integration (KBP_MCO), and a combination of integration methods (KBP_MCO+MCO). All plans were analyzed for planning target volume (PTV) coverage, OAR constraints, and plan quality metrics. Comparisons were generated to evaluate plan and model quality. Phase 1 highlighted the necessity of KBP and MCO in treatment planning, as both optimization methods improved plan quality metrics (Conformity and Heterogeneity Indices) and reduced mean rectal dose by 2\u00a0Gy, as compared to manual planning. Integrating MCO with KBP did not further improve plan quality, as little significance was seen over KBP or MCO alone. Principal component score (PCS) fitting showed KBP_MCO improved bladder and rectum estimated and modeled dose correlation by 5% and 22%, respectively; however, model improvements did not significantly impact plan quality. KBP and MCO have shown to reduce OAR dose while maintaining desired PTV coverage in this study. Further integration of KBP and MCO did not show marked improvements in treatment plan quality while requiring increased time in model generation and optimization time.<\/jats:p>","DOI":"10.1002\/acm2.13940","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T17:22:10Z","timestamp":1677259330000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["The effect of integrating knowledge\u2010based planning with multicriteria optimization in treatment planning for prostate SBRT"],"prefix":"10.1002","volume":"24","author":[{"given":"Sandun","family":"Jayarathna","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University of Kansas Cancer Center  Kansas City KS USA"},{"name":"Department of Radiation Physics The University of Texas MD Anderson Cancer Center  Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinglei","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Kansas Cancer Center  Kansas City KS USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald C.","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Kansas Cancer Center  Kansas City KS USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. 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