{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T22:52:37Z","timestamp":1790376757167,"version":"4.1.0"},"reference-count":46,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"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":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Daily online adaptive radiotherapy (ART) improves dose metrics for gynecological cancer patients, but the on\u2010treatment process is resource\u2010intensive requiring longer appointments and additional time from the entire adaptive team. To optimize resource allocation, we propose a model to identify high\u2010priority patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>For 49 retrospective cervical and endometrial cancer patients, we calculated two initial plans: the treated standard\u2010of\u2010care (Initial<jats:sub>SOC<\/jats:sub>) and a reduced margin initial plan (Initial<jats:sub>ART<\/jats:sub>) for adapting with the Ethos treatment planning system. Daily doses corresponding to standard and reduced margins (Daily<jats:sub>SOC<\/jats:sub> and Daily<jats:sub>ART<\/jats:sub>) were determined by re\u2010segmenting the anatomy based on the treatment CBCT and calculating dose on a synthetic CT. These initial and daily doses were used to estimate the ART benefit (=\u00a0Daily<jats:sub>SOC<\/jats:sub>\u2010Daily<jats:sub>ART<\/jats:sub>) versus initial plan differences (=\u00a0Initial<jats:sub>SOC<\/jats:sub>\u2013Initial<jats:sub>ART<\/jats:sub>) via multivariate linear regression. Dosimetric benefits were modeled with initial plan differences () of  (cc),  (Gy), and  (Gy). Anatomy (intact uterus or post\u2010hysterectomy), DoseType (simultaneous integrated boost [SIB] vs. single dose), and\/or prescription value. To establish a logistic model, we classified the top 10% in each metric as high\u2010benefit patients. We then built a logistic model to predict these patients from the previous predictors. Leave\u2010one\u2010out validation and ROC analysis were used to evaluate the accuracy. To improve the clinical efficiency of this predictive process, we also created knowledge\u2010based plans for the \u0394Initial plans () and repeated the analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In both  and  our multivariate analysis showed low <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> values 0.34\u20130.52 versus 0.14\u20130.38. The most significant predictor in each multivariate model was the corresponding \u2206Initial metric (e.g.,  Bowel (V40\u00a0Gy), <jats:italic>p<\/jats:italic>\u00a0&lt;\u00a01e\u221205). In the logistic model, the metrics with the strongest correlation to the high\u2010benefit patients were  (cc),  (Gy), , and  prescription. The models for original and knowledge\u2010based plans had an AUC of 0.85 versus 0.78. The sensitivity and specificity were 0.92\/0.72 and 0.69\/0.80, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This methodology will allow clinics to prioritize patients for resource\u2010intensive daily online ART.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14596","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T11:26:11Z","timestamp":1737977171000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Modeling dosimetric benefits from daily adaptive RT for gynecological cancer patients with and without knowledge\u2010based dose prediction"],"prefix":"10.1002","volume":"26","author":[{"given":"Rupesh","family":"Ghimire","sequence":"first","affiliation":[{"name":"Department of Radiation Medicine and Applied Sciences UC San Diego Health  La Jolla California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lance","family":"Moore","sequence":"additional","affiliation":[{"name":"Department of Radiation Medicine and Applied Sciences UC San Diego Health  La Jolla California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniela","family":"Branco","sequence":"additional","affiliation":[{"name":"Department of Radiation Medicine and Applied Sciences UC San Diego Health  La Jolla California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominique L.","family":"Rash","sequence":"additional","affiliation":[{"name":"Department of Radiation Medicine and Applied Sciences UC San Diego Health  La Jolla California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jyoti S.","family":"Mayadev","sequence":"additional","affiliation":[{"name":"Department of Radiation Medicine and Applied Sciences UC San Diego Health  La Jolla California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xenia","family":"Ray","sequence":"additional","affiliation":[{"name":"Department of Radiation Medicine and Applied Sciences UC San Diego Health  La Jolla California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"American Cancer Society.Cervical Cancer Statistics | Key Facts About Cervical Cancer. American Cancer Society. Accessed: Nov. 13 2022. [Online]. Available:https:\/\/www.cancer.org\/cancer\/cervical\u2010cancer\/about\/key\u2010statistics.html"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.CLON.2016.04.044"},{"key":"e_1_2_9_4_1","unstructured":"BeadleB JhingranA SalehpourM \u2026 M. S.\u2010I. J. of and undefined 2009.Cervix regression and motion during the course of external beam chemoradiation for cervical cancer. Elsevier Accessed: Oct. 18 2022. [Online]. Available:https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0360301608006500"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.RADONC.2010.11.015"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.IJROBP.2009.10.075"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.IJROBP.2020.08.061"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.RADONC.2008.04.016"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.IJROBP.2007.08.055"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0360\u20103016(03)00325\u20100"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.1994.12.3.510"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/42\/1\/008"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.IJROBP.2022.06.078"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/ACM2.13479"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.IJROBP.2020.02.025"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/ACM2.13783"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.PRRO.2022.05.007"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2022.101034"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1088\/2057\u20101976\/ACDF62"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/ACM2.13399"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1186\/S13014\u2010019\u20101262\u20108"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.06.033"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.EJCA.2009.07.009"},{"key":"e_1_2_9_24_1","unstructured":"HastieT QianJ K. T.\u2010C. R. Repositary and undefined 2021. An Introduction to glmnet. cloud.r\u2010project.org 2016 Accessed: Jun. 21 2023. [Online]. Available:https:\/\/cloud.r\u2010project.org\/web\/packages\/glmnet\/vignettes\/glmnet.pdf"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.18637\/JSS.V008.I15"},{"key":"e_1_2_9_26_1","unstructured":"\u201cR: Choose a model by AIC in a Stepwise Algorithm.\u201d Accessed: Mar. 24 2024. [Online]. Available:https:\/\/stat.ethz.ch\/R\u2010manual\/R\u2010devel\/library\/MASS\/html\/stepAIC.html"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/AD4A1C"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.CTRO.2023.100596"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.CLON.2024.01.005"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1186\/S13014\u2010023\u201002394\u20102\/TABLES\/3"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.PRRO.2022.10.009"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.PRRO.2022.11.003"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.ADRO.2023.101405"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.RADONC.2022.03.014"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.RADONC.2023.110064"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.ADRO.2024.101510"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.3857\/ROJ.2020.00976"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.ADRO.2022.100934"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ACCDB2"},{"key":"e_1_2_9_40_1","doi-asserted-by":"publisher","DOI":"10.3390\/CANCERS16061206"},{"key":"e_1_2_9_41_1","doi-asserted-by":"publisher","DOI":"10.1002\/ACM2.13961"},{"key":"e_1_2_9_42_1","doi-asserted-by":"publisher","DOI":"10.1148\/RYCAN.230011"},{"key":"e_1_2_9_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.JMIR.2022.12.002"},{"key":"e_1_2_9_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.RADONC.2022.02.013"},{"key":"e_1_2_9_45_1","doi-asserted-by":"publisher","DOI":"10.1002\/MP.15714"},{"key":"e_1_2_9_46_1","doi-asserted-by":"publisher","DOI":"10.3109\/0284186X.2015.1061209"},{"key":"e_1_2_9_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.CTRO.2023.100603"}],"container-title":["Journal of Applied Clinical Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.14596","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T12:36:03Z","timestamp":1748349363000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/acm2.14596"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,27]]},"references-count":46,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10.1002\/acm2.14596"],"URL":"https:\/\/doi.org\/10.1002\/acm2.14596","archive":["Portico"],"relation":{},"ISSN":["1526-9914","1526-9914"],"issn-type":[{"value":"1526-9914","type":"print"},{"value":"1526-9914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,27]]},"assertion":[{"value":"2024-05-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e14596"}}