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Existing approaches often automate auto-segmentation and auto-planning separately, leaving unclear whether a fully end-to-end pipeline for VMAT planning in rectal cancer is feasible. Purpose: Develop and clinically evaluate a complete end-to-end process for auto-segmentation and auto-planning using only gross tumor volume contour and a CT scan. Methods combine nnU-Net auto-segmentation with RapidPlan-based VMAT auto-planning and Likert-scored physician review.",{"@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/automatic-end-to-end-vmat-treatment-planning-for-rectal-cancers/341102/",{"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/automatic-end-to-end-vmat-treatment-planning-for-rectal-cancers/341102.png","ImageObject",300,407,{"name":92,"@type":93},"Ava Thompson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the end-to-end VMAT pipeline for rectal cancer?","Question",{"text":112,"@type":113},"To create and clinically evaluate an end-to-end process that performs both auto-segmentation and auto-planning for VMAT using only the gross tumor volume contour and a CT scan as inputs.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the auto-segmentation and auto-planning components developed and tested?",{"text":117,"@type":113},"Deep learning auto-segmentation models were trained, validated, and tested using nnU-Net for multiple structures, and auto-planning used a RapidPlan knowledge-based VMAT model trained and tested on separate patient sets, followed by physician scoring.",{"name":119,"@type":110,"acceptedAnswer":120},"How were the resulting contours and plans evaluated by clinicians?",{"text":121,"@type":113},"Clinical acceptability was assessed by a gastrointestinal radiation oncologist using a five-point Likert scale for contours, and two gastrointestinal radiation oncologists scored the auto-plans with a five-point Likert scale, reporting high acceptance rates.","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},341102,1790472026,{"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":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41},1649267921044,"https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1786009248482753345","DOI: 10.1002/acm2.14259  \nRADIATION ONCOLOGY PHYSICS  \nAutomatic end-to-end VMAT treatment planning for rectal cancers  \nKai Huang1,2  Christine Chung2  Ethan B. Ludmir3  Lifei Zhang2  Constance A. Owens1,2  Jean Gumma-De La Vega2  Jack Duryea2  Yao Zhao1,2   Xinru Chen1,2  David Fuentes4  Carlos E. Cardenas5  Tina Marie Briere2  Sam Beddar2  Laurence E. Court2  Prajnan Das3  \n1The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas, USA  \n2 Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA  \n3 Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA  \n4 Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA  \n5 Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, USA  \nCorrespondence  \nLaurence E. Court, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.  \nEmail: [lecourt@mdanderson.org](lecourt@mdanderson.org)  \nLaurence E. Court and Prajnan Das are co-senior authors.  \nFunding information  \nMD Anderson Cancer Center Institutional Research Grant  \nAbstract  \nBackground: The treatment planning process from segmentation to producing a deliverable plan is time-consuming and labor-intensive. Existing solutions automate the segmentation and planning processes individually. The feasibility of combining auto-segmentation and auto-planning for volumetric modulated arc therapy (VMAT) for rectal cancers in an end-to-end process is not clear. Purpose: To create and clinically evaluate a complete end-to-end process for auto-segmentation and auto-planning of VMAT for rectal cancer requiring only the gross tumor volume contour and a CT scan as inputs.  \nMethods: Patient scans and data were retrospectively selected from our institutional records for patients treated for malignant neoplasm of the rectum. We trained, validated, and tested deep learning auto-segmentation models using nnU-Net architecture for clinical target volume (CTV), bowel bag, large bowel, small bowel, total bowel, femurs, bladder, bone marrow, and female and male genitalia. For the CTV, we identified 174 patients with clinically drawn CTVs. We used data for 18 patients for all structures other than the CTV. The structures were contoured under the guidance of and reviewed by a gastrointestinal (GI) radiation oncologist. The predicted results for CTV in 35 patients and organs at risk (OAR) in six patients were scored by the GI radiation oncologist using a five-point Likert scale. For auto-planning, a RapidPlan knowledge-based planning solution was modeled for VMAT delivery with a prescription of 25 Gy in five fractions. The model was trained and tested on 20 and 34 patients, respectively. The resulting plans were scored by two GI radiation oncologists using a fivepoint Likert scale. Finally, the end-to-end pipeline was evaluated on 16 patients, and the resulting plans were scored by two GI radiation oncologists.  \nResults: In 31 of 35 patients, CTV contours were clinically acceptable without necessary modifications. The CTV achieved a Dice similarity coefficient of 0.85 (±0.05) and 95% Hausdorff distance of 15 .25 (±5.59) mm. All OAR contours were clinically acceptable without edits, except for large and small bowel which were challenging to differentiate. However, contours for total, large, and small bowel were clinically acceptable. The two physicians accepted 100% and 91% of the auto-plans. For the end-to-end pipeline, the two physicians accepted 88% and 62% of the auto-plans.  \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 Clinica","cbCaisVcDLm8Exj2","https://ap.wps.com/l/cbCaisVcDLm8Exj2","pdf",7661142,12,"English","# Abstract\n## Background and Purpose\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"What is the main goal of the end-to-end VMAT pipeline for rectal cancer?\",\"answer\":\"To create and clinically evaluate an end-to-end process that performs both auto-segmentation and auto-planning for VMAT using only the gross tumor volume contour and a CT scan as inputs.\"},{\"question\":\"How were the auto-segmentation and auto-planning components developed and tested?\",\"answer\":\"Deep learning auto-segmentation models were trained, validated, and tested using nnU-Net for multiple structures, and auto-planning used a RapidPlan knowledge-based VMAT model trained and tested on separate patient sets, followed by physician scoring.\"},{\"question\":\"How were the resulting contours and plans evaluated by clinicians?\",\"answer\":\"Clinical acceptability was assessed by a gastrointestinal radiation oncologist using a five-point Likert scale for contours, and two gastrointestinal radiation oncologists scored the auto-plans with a five-point Likert scale, reporting high acceptance rates.\"}]","Automatic end-to-end VMAT treatment planning for rectal cancers | PDF",1790040332]