[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124595-en":3,"doc-seo-124595-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124595,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",7,"Healthcare","Automated Robust Planning for IMPT in Oropharyngeal Cancer Patients Using Machine Learning","Automated Robust Planning for intensity modulated proton therapy (IMPT) in oropharyngeal cancer patients evaluates an atlas regression forest machine learning approach for robust dose prediction and machine-deliverable plan generation. The model is trained on robust IMPT data from 88 patients and combined with robust dose mimicking optimization across 21 perturbed scenarios, then tuned via cross-validation. Results on 25 independent test patients compare robust target coverage, organ-at-risk doses, and normal tissue complication probability against manually optimized clinical plans.","University of Groningen  \nAutomated Robust Planning for IMPT in Oropharyngeal Cancer Patients Using MachineLearning  \nvan Bruggen,Ilse G;Huiskes,Merle;de Vette,Suzanne PM;Holmström,Mats;Langendijk,Johannes A;Both,Stefan;Kierkels,Roel G J;Korevaar,Erik W  \nPublished in:International Journal of Radiation Oncology,Biology,Physics  \nDOI:  \n10.1016/j.ijrobp.2022.12.004  \nIMPORTANT NOTE:You are advised to consult the publisher's version (publisher's PDF)if you wish to cite fromit.Please check the document version below.  \nDocument VersionPublisher's PDF,also known as Version of record  \nPublication date:  \n2023  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version(APA):  \nvan Bruggen,1.G.,Huiskes,M.,de Vette,S.P.M.,Holmström,M.,Langendijk,J.A.,Both,S.,Kierkels,R.  \nG.J.,&Korevaar,E.W.(2023).Automated Robust Planning for IMPT in Oropharyngeal Cancer PatientsUsing Machine Leaming.Intemational Journal of Radiation Oncology,Biology,Physics,115(5),1283-1290.https://doi.org/10.1016/j.ijrobp.2022.12.004  \nCopyright  \nOther than for strictly personal use,it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s)andor copyright holder(s),unless the work is under an open content license(like Creative Commons).  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act,indicated by the \"Taverne\"license.More information can be found on the University of Groningen website:https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details,and we will remove access to the work immediatelyand investigate your claim.  \nDownloaded from the University of Groningen/UMCG research database (Pure):http://www.rug.nl/research/portal.For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.  \nAutomated Robust Planning for IMPT in  \nOropharyngeal Cancer Patients Using MachineLearning  \nllse G.van Bruggen,MSc,*Merle Huiskes,MSc,*Suzanne P.M.de Vette,MSc,*Mats Holmström,MSc,Johannes A.Langendijk,MD,PhD,*Stefan Both,PhD,*Roel G.J.Kierkels,PhD,and Erik W.Korevaar,PhD*  \nDepartment of Radiation Oncology,University Medical Center Groningen,University of Groningen,Groningen,the Netherlands;RaySearch Laboratories,Stockholm,Sweden;and Radiotherapiegroep,Deventer,the Netherlands  \nReceived Apr 15,2022;Accepted for publication Dec 5,2022  \nConclusion:This study showed that automated MLO planning can generate robustly optimized MLO plans with quality com-parable to clinical plans in OPC patients.◎2022 Published by Elsevier Inc.  \nCorresponding author:Ilse G.van Bruggen,MSc;E-mail:i.g.van.bruggen@umcg.nl  \nIlse G.van Bruggen and Merle Huiskes contributed equally to thiswork.  \nM.Huiskes is currently at the Department of Radiation Oncology,Lei-den University Medical Centre,Leiden,the Netherlands.  \nPurpose:The aim of this study was to evaluate an automated treatment planning method for robustly optimized intensitymodulated proton therapy(IMPT)plans for oropharyngeal carcinoma patients and to compare the results with manually opti-mized robust IMPT plans.  \nMethods and Materials:An atlas regression forest-based machine learning(ML)model for dose prediction was trained onCT scans,contours,and dose distributions of robust IMPT plans of 88 oropharyngeal cancer(OPC)patients.The ML modelwas combined with a robust voxel and dose volume histogram-based dose mimicking optimization algorithm for 21 perturbedscenarios to generate a machine-deliverable plan from the predicted dose distribution.Machine learning optimization(MLO)configuration was performed using a cross-validation approach with 3×8 tuning patients and comprised of adjustments tothe mimicking optimization,to generate higher-quality MLO plans.Independent testing of the MLO algorithm was performedwith another 25 pati","cbCaivR7lJ3uVgyj","https://ap.wps.com/l/cbCaivR7lJ3uVgyj","pdf",5692143,1,9,"English","en",105,"# Introduction\n## Study purpose\n## Methods and materials\n## Results\n## Conclusion","[{\"question\":\"What was the study purpose?\",\"answer\":\"To evaluate an automated treatment planning method for robustly optimized IMPT plans for oropharyngeal carcinoma patients and compare it with manually optimized robust IMPT plans.\"},{\"question\":\"How does the machine learning model generate treatment plans?\",\"answer\":\"An atlas regression forest ML model predicts dose from CT scans, contours, and robust IMPT data, which is then combined with robust voxel- and DVH-based dose mimicking optimization across perturbed scenarios.\"},{\"question\":\"How did plan quality compare between automated MLO and clinical plans?\",\"answer\":\"MLO plans achieved comparable robust target coverage and NTCP values, with significant increases in average OAR doses reported for pharynx constrictor muscles and cervical esophagus in the MLO plans.\"}]","Automated Robust Planning for IMPT in Oropharyngeal Cancer Patients Using Machine Learning | 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was the study purpose?","Question",{"text":75,"@type":76},"To evaluate an automated treatment planning method for robustly optimized IMPT plans for oropharyngeal carcinoma patients and compare it with manually optimized robust IMPT plans.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the machine learning model generate treatment plans?",{"text":80,"@type":76},"An atlas regression forest ML model predicts dose from CT scans, contours, and robust IMPT data, which is then combined with robust voxel- and DVH-based dose mimicking optimization across perturbed scenarios.",{"name":82,"@type":73,"acceptedAnswer":83},"How did plan quality compare between automated MLO and clinical plans?",{"text":84,"@type":76},"MLO plans achieved comparable robust target coverage and NTCP values, with significant increases in average OAR doses reported for pharynx constrictor muscles and cervical esophagus in the MLO 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