[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-337933-105":59,"doc-detail-337933-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","evaluating-automatically-generated-normal-tissue-contours-for-safe-use-in-head-and-neck-and-cervical-cancer-treatment-planning","Evaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning","","Volumetric-modulated arc therapy (VMAT) for head and neck and cervical cancers requires accurate contouring, yet generating organs at risk and optimizing plans can be time-consuming. An automated deep-learning–based Radiation Planning Assistant (RPA) was used to create autoplans for retrospectively collected patients, generating autocontours for comparison to physician-drawn contours treated as ground truth. A two one-sided tests framework assessed whether normal-tissue dose equivalence held within a clinically chosen margin, and dosimetric criteria pass rates were quantified. Results supported dosimetric equivalence for common DVH metrics and generally preserved clinical dose tolerances.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/evaluating-automatically-generated-normal-tissue-contours-for-safe-use-in-head-and-neck-and-cervical-cancer-treatment-planning/337933/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/evaluating-automatically-generated-normal-tissue-contours-for-safe-use-in-head-and-neck-and-cervical-cancer-treatment-planning/337933.png","ImageObject",300,407,{"name":92,"@type":93},"Aria","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in VMAT planning?","Question",{"text":112,"@type":113},"Creating contours and optimizing VMAT plans for head and neck and cervical cancers is time-consuming. The study evaluates whether an automated tool can generate high-quality normal tissue contours safely.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were automated contours evaluated against physician-drawn contours?",{"text":117,"@type":113},"Autoplans were retrospectively generated using the Radiation Planning Assistant (RPA). Autocontours were compared to clinic physician-drawn contours used as ground truth by evaluating normal-tissue dose equivalence with a two one-sided tests (TOST) approach and clinically selected margins.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the main findings for head and neck and cervical plans?",{"text":121,"@type":113},"Most structures met established dosimetric criteria for both automated and manual contours. Automated contours were equivalent to ground truth for a majority of common DVH metrics, and differences generally did not change whether clinical dose tolerances passed or failed.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},337933,1790134931,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46},2336464648322,"https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488","DOI: 10.1002/acm2.14338  \nRADIATION ONCOLOGY PHYSICS  \nEvaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning  \nRaphael Douglas1 Beth M. Beadle2   \n Adenike Olanrewaju1  Raymond Mumme1  Laurence Edward Court1  \nLifei Zhang1  \n1 Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA  \n2 Department of Radiation Oncology, Stanford University, Stanford, California, USA  \nCorrespondence  \nRaphael Douglas, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA.  \nEmail: [rjdouglas3@mdanderson.org](rjdouglas3@mdanderson.org)  \nAbstract  \nPurpose: Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool.  \nMethods: For patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep-learning and RapidPlan models developed in-house. The autoplans were, then, applied to the original, physician-drawn contours, which were used as a ground truth (GT) to compare with the autocontours (RPA) . Using a “two one-sided tests” (TOST) procedure, we evaluated whether the autocontour normal tissue dose was equivalent to that of the ground truth by a margin, δ, that we determined based on clinical judgement. We also calculated the number of plans that met established clinically accepted dosimetric criteria.  \nResults: For HN plans, 91 .8% and 91 .7% of structures met dosimetric criteria for automatic and manual contours, respectively; for cervical plans, 95.6% and 95.7% of structures met dosimetric criteria for automatic and manual contours, respectively. Autocontours were equivalent to the ground truth for 71% and 75% of common DVH metrics for the HN and cervix, respectively.  \nConclusions: This study shows that dosimetrically equivalent normal tissue contours can be created for HN and cervical cancers using deep learning techniques. In general, differences between the contours did not affect the passing or failing of clinical dose tolerances.  \nKEYWORDS  \nauto-contour evaluation  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2024 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.  \nJAppl Clin Med Phys. 2024;25:e14338 .  \n[https://doi.org/10.1002/acm2.14338](https://doi.org/10.1002/acm2.14338)  \n[wileyonlinelibrary.com/journal/acm2](wileyonlinelibrary.com/journal/acm2)  \n1  \nof 8  \n1  INTRODUCTION  \nRadiotherapy is a crucial modality for treatment of head and neck (HN) and cervical cancers. Intensitymodulated radiotherapy and volumetric-modulated arc therapy (VMAT) are accepted treatment methods for these cancers but are subject to inter-and intra-planner variability.1–4 These variations can lead to poor clinical outcomes for patients treated with suboptimal plans.3,5 Knowledge-based planning has been shown to reduce variability,1,2,4 while also creating high-quality treatment plans.6–8  \nThe Radiation Planning Assistant (RPA) is a webbased automated treatment planning tool being developed to generate consistent, high-quality contours and treatment plans. The development of the RPA has been previously described.7,9–21 The RPA uses deep learning and knowledge-based planning","cbCailrPsFQtF7QF","https://ap.wps.com/l/cbCailrPsFQtF7QF","pdf",994381,"English","# Abstract\n# Purpose\n# Methods\n## Patient data\n## RPA workflow\n# Results\n# Conclusions\n# Keywords","[{\"question\":\"What problem does the study address in VMAT planning?\",\"answer\":\"Creating contours and optimizing VMAT plans for head and neck and cervical cancers is time-consuming. The study evaluates whether an automated tool can generate high-quality normal tissue contours safely.\"},{\"question\":\"How were automated contours evaluated against physician-drawn contours?\",\"answer\":\"Autoplans were retrospectively generated using the Radiation Planning Assistant (RPA). Autocontours were compared to clinic physician-drawn contours used as ground truth by evaluating normal-tissue dose equivalence with a two one-sided tests (TOST) approach and clinically selected margins.\"},{\"question\":\"What were the main findings for head and neck and cervical plans?\",\"answer\":\"Most structures met established dosimetric criteria for both automated and manual contours. Automated contours were equivalent to ground truth for a majority of common DVH metrics, and differences generally did not change whether clinical dose tolerances passed or failed.\"}]","Evaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning | PDF",1790021236]