[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125673-en":3,"doc-seo-125673-105":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},125673,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",7,"Healthcare","Evaluation treatment planning system for oropharyngeal cancer patient using machine learning","Oropharyngeal cancer (OPC) is treated with radiotherapy where accurate planning affects dose distribution and normal tissue risk. This research evaluates the performance of an OPC VMAT machine-learning model against clinical treatment plans by comparing dosimetric parameters and normal tissue complication probabilities. The ML plans are tuned to match or exceed clinical photon plan quality and to identify an appropriate strategic planning scheme for OPC. Five patient cases are used with a RayStation VMAT model (development 11B) trained across different modalities using specified PTV targets and constraints.","University of Groningen  \nEvaluation treatment planning system for oropharyngeal cancer patient using machine learning  \nGlayl, Ahmed Ghanim; Salem, Karrar Hazim; Noori, Harith Muthanna; Abdul-Zahra, Dalael  \nSaad; Shareef Abdalhussien, Naeem; Alkhafaji, Mohammed Ayad Published in:  \nApplied Radiation and Isotopes  \nDOI:  \n10.1016/j.apradiso.2023.110785  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2023  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nGlayl, A. G. , Salem, K. H. , Noori, H. M. , Abdul-Zahra, D. S. , Shareef Abdalhussien, N. , & Alkhafaji, M. A.(2023) . Evaluation treatment planning system for oropharyngeal cancer patient using machine learning.  \nApplied Radiation and Isotopes, 199, Article 110785. [https://doi.org/10.1016/j.apradiso.2023.110785](https://doi.org/10.1016/j.apradiso.2023.110785)  \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 the author(s) and/or 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](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 immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 04-08-2026  \nApplied Radiation and Isotopes 199 (2023) 110785  \nContents lists available at ScienceDirect  \nApplied Radiation and Isotopes  \njournal [homepage:](homepage: www.elsevier.com/locate/apradiso)[ www.elsevier.com/locate/apradiso](homepage: www.elsevier.com/locate/apradiso)  \n|  |  |\n| --- | --- |\n| Evaluation treatment planning system for oropharyngeal cancer patient  |  |\n| using machine learning |  |\n| Ahmed Ghanim Glayla, Karrar Hazim Salem b, Harith Muthanna Nooric, |  |\n| Dalael Saad Abdul-Zahrad, *, Naeem Shareef Abdalhussiene, Mohammed Ayad Alkhafajif |  |\n| a Department of Radiation Oncology, University Medical Center Groningen, Netherlands |  |\n| b Pharmacy Department, Al-Mustaqbal University College, 51001, Hillah, Babil, Iraq |  |\n| c Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkiye |  |\n| d Department of Medical Physics, Hilla University College, Babylon, Iraq |  |\n| e Shahid-Beheshti University of Medical Sciences, Tehran, Iran |  |\n| f National University of Science and Technology, Dhi Qar, Iraq |  |\n| A R T I C L E I N F O\u003Cbr>Keywords:\u003Cbr>Oropharyngeal cancer (OPC) Radiotherapy (RT)\u003Cbr>Machine learning (ML) | A B S T R A C T\u003Cbr>Oropharyngeal cancer (OPC) comprises a group of various malignant tumours that grow in the throat, larynx, mouth, sinuses, and nose.\u003Cbr>The research aims: to investigate the performance of the OPC VMAT model by comparison to clinical plans in terms of dosimetric parameters and normal tissue complication probabilities.\u003Cbr>Purpose: Tune the model which at least matches the performance of clinical created photon treatment plans and analyse and fin","cbCaideTglGTxXIL","https://ap.wps.com/l/cbCaideTglGTxXIL","pdf",5415553,1,9,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does the study address in OPC radiotherapy planning?\",\"answer\":\"The study addresses how to evaluate and tune a machine-learning VMAT treatment planning approach for oropharyngeal cancer by comparing it with clinical plans.\"},{\"question\":\"How are the machine-learning plans compared to clinical reference plans?\",\"answer\":\"The ML plans are compared to reference clinical plans using dose constraints and target coverage, including dosimetric parameters and normal tissue complication probabilities.\"},{\"question\":\"What planning approach and dose prescription are used for OPC in the study?\",\"answer\":\"A VMAT oropharynx model is used with a prescribed dose of 70 Gy delivered as 2 Gy per fraction, employing PTVs derived for primary and secondary targets.\"}]","Evaluation treatment planning system for oropharyngeal 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