[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127525-en":3,"doc-seo-127525-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127525,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning for Predicting Clinician Approval of Treatment Plans for Left-Sided Whole Breast Radiation Therapy","Purpose: Investigate the ability of machine learning (ML) models to predict clinician approval of treatment plans for left-sided whole breast radiotherapy. Focus centers on developing predictive approaches that can support clinical decision-making by anticipating clinician evaluation prior to final plan endorsement. The document provides publication metadata, authorship, licensing, and citation details within Advances in Radiation Oncology, with an early “journal pre-proof” disclosure and associated identifiers.","EUR Research Information Portal  \nMachine Learning for Predicting Clinician Evaluation of Treatment Plans for Left-Sided Whole Breast Radiation Therapy  \nPublished in:  \nAdvances in Radiation Oncology  \nPublication status and date:  \nPublished: 01/09/2023  \nDOI (link to publisher):  \n10.1016/j.adro.2023.101228  \nDocument Version  \nVersion created as part of publication process; publisher's layout; not normally made publicly available  \nDocument License/Available under:  \nCC BY-NC-ND  \nCitation for the published version (APA):  \nFiandra, C. , Cattani, F. , Leonardi, M. C. , Comi, S. , Zara, S. , Rossi, L. , Jereczek-Fossa, B. A. , Fariselli, P. , Ricardi, U. , & Heijmen, B. (2023) . Machine Learning for Predicting Clinician Evaluation of Treatment Plans for Left-Sided Whole Breast Radiation Therapy. Advances in Radiation Oncology, 8(5), Article 101228. [https://doi.org/10.1016/j.adro.2023.101228](https://doi.org/10.1016/j.adro.2023.101228)  \nLink to publication on the EUR Research Information Portal  \nTerms and Conditions of Use  \nExcept as permitted by the applicable copyright law, you may not reproduce or make this material available to any third party without the prior written permission from the copyright holder(s) . Copyright law allows the following uses of this material without prior permission:  \n• you may download, save and print a copy of this material for your personal use only;  \n• you may share the EUR portal link to this material.  \nIn case the material is published with an open access license (e.g. a Creative Commons (CC) license), other uses may be allowed. Please check the terms and conditions of the specific license.  \nTake-down policy  \nIf you believe that this material infringes your copyright and/or any other intellectual property rights, you may request its removal by contacting us at the following email address: [openaccess.library@eur.nl. Please](openaccess.library@eur.nl. Please) provide us with all the relevant information, including the reasons why you believe any of your rights have been infringed. In case of a legitimate complaint, we will make the material inaccessible and/or remove it from the website.  \nJournal Pre-proof  \nMachine learning for predicting clinician approval of treatment plans for left-sided whole breast radiotherapy.  \nC. Fiandra PhD , F. Cattani MSc , M.C. Leonardi MD ,  \nS. Comi MSc , S. Zara MSc , L. Rossi PhD ,  \nB.A. Jereczek-Fossa MD , P. Fariselli PhD , U. Ricardi MD ,  \nB. Heijmen PhD  \nPII: S2452-1094(23)00057-X  \nDOI: [https://doi.org/10.1016/j.adro.2023.101228](https://doi.org/10.1016/j.adro.2023.101228)  \nReference: ADRO 101228  \nTo appear in: Advances in Radiation Oncology  \nReceived date: 19 January 2023  \nAccepted date: 15 March 2023  \nPlease cite this article as: C. Fiandra PhD , F. Cattani MSc , M.C. Leonardi MD , S. Comi MSc , S. Zara MSc , L. Rossi PhD , B.A. Jereczek-Fossa MD , P. Fariselli PhD , U. Ricardi MD , B. Heijmen PhD , Machine learning for predicting clinician approval of treatment plans for left-sided whole breast radiotherapy. , Advances in Radiation Oncology (2023), doi:  \n[https://doi.org/10.1016/j.adro.2023.101228](https://doi.org/10.1016/j.adro.2023.101228)  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2023 The Author(s) . Published by Elsevier Inc. on behalf of American Society for Radiation Oncology.  \nThis is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/l](http://creativecommons.org/l","cbCaih78S0kyx7DU","https://ap.wps.com/l/cbCaih78S0kyx7DU","pdf",1148578,2,1,17,"English","en",105,"# EUR Research Information Portal\n## Publication information\n## License and terms of use\n## Take-down policy\n## Journal pre-proof details\n## Citation and bibliographic identifiers\n## Title, running title, authors, and correspondence","[{\"question\":\"What does the study aim to predict for left-sided whole breast radiotherapy?\",\"answer\":\"It aims to predict clinician approval of treatment plans for left-sided whole breast radiotherapy using machine learning models.\"},{\"question\":\"Where is the article published and how is it labeled in the portal?\",\"answer\":\"It is published in Advances in Radiation Oncology and is marked as a “Journal Pre-proof” with an early 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