{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T07:31:40Z","timestamp":1753860700621,"version":"3.41.2"},"reference-count":26,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T00:00:00Z","timestamp":1738886400000},"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":[[2025,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Intra\u2010 and inter\u2010observer contour uncertainty is a continuous challenge in treatment planning for radiotherapy. Our proposed solution to address this challenge is the use of partial contours for treatment planning, focusing on uninvolved or non\u2010overlapping portions of the organs\u2010at\u2010risk (OARs) with the planning target volume (PTV).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The partial contours systematically eliminate overlapping regions. The partial contours were evaluated against fully contoured OARs. We incorporated advanced tools like knowledge\u2010based planning (KBP) to create treatment plans and artificial intelligence (AI) to create auto\u2010segmented contours. We developed two models, Rapid Plan (RP) and Rapid Plan partial uninvolved (RP_Part_Un), using 70 previous clinically approved volumetric arc therapy (VMAT) plans each prescribed with 70 Gy\/28 fractions. From these models, we created three plans, RP, RP_Part_Un, and MIM AI_Part_Un. In this retrospective study, 60 prostate patients were analyzed using the three plans. For determining OAR sparing, <jats:italic>D<\/jats:italic><jats:sub>max<\/jats:sub> and <jats:italic>D<\/jats:italic><jats:sub>mean<\/jats:sub> along the percent volume receiving a dose over a range (V<jats:sub>10<\/jats:sub> Gy V<jats:sub>70<\/jats:sub> Gy) between each plan were compared. Geometric evaluations, dice similarity coefficient (DSC), and overlay index (OI) between the OAR contours from partial\u2010contoured manual structure sets and partial\u2010contoured AI structure sets were analyzed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>When comparing the <jats:italic>DSC<\/jats:italic> and <jats:italic>OI<\/jats:italic> for full contours to the partial contours, in both groups, all comparisons were significantly increased for both organs. This indicated the partial contours had a higher degree of concordance. In patients with SpaceOAR, RP_Part_Un plans exhibited significantly reduced bladder <jats:italic>D<\/jats:italic><jats:sub>max<\/jats:sub> and <jats:italic>D<\/jats:italic><jats:sub>mean<\/jats:sub> compared to RP plans, while rectum <jats:italic>D<\/jats:italic><jats:sub>max<\/jats:sub> and D<jats:sub>mean<\/jats:sub> showed no significant differences. For patients without SpaceOAR, RP_Part_Un significantly lowered rectum <jats:italic>D<\/jats:italic><jats:sub>mean<\/jats:sub>. MIM AI_Part_Un plans demonstrated lower rectum <jats:italic>D<\/jats:italic><jats:sub>max<\/jats:sub> in both patient groups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Partial contours, defined at a specified distance from the PTV, yielded dosimetry comparable to fully contoured plans, highlighting their potential efficacy in treatment planning.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.70004","type":"journal-article","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T10:47:01Z","timestamp":1738925221000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The implementation of knowledge\u2010based planning with partial OAR contours for prostate radiotherapy"],"prefix":"10.1002","volume":"26","author":[{"given":"Ositomiwa O.","family":"Osipitan","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Cone Health Cancer Center  Greensboro North Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Wiant","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Cone Health Cancer Center  Greensboro North Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8939-7080","authenticated-orcid":false,"given":"Han","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Cone Health Cancer Center  Greensboro North Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,7]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms232214257"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2013.108415"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1606220"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12674"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/0284186X.2018.1478126"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2020.05.013"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010018\u20101114\u2010y"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010018\u20100994"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/59\/18\/5575"},{"key":"e_1_2_10_11_1","first-page":"11","article-title":"Knowledge\u2010based radiation therapy (KBRT) treatment planning versus planning by experts: validation of a KBRT algorithm for prostate cancer treatment planning","volume":"10","author":"Nwankwo O","year":"2012","journal-title":"Radiat Oncol"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13337"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13526"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clon.2023.01.014"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.09.008"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12111834"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.urology.2021.05.013"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2014.11.009"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2017.06.026"},{"key":"e_1_2_10_20_1","unstructured":"MIM Software Inc.MIM Contour ProtegeAI (Version 4.0) [Software]. MIM Maestro. Cleveland OH (2024). 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