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This study investigates symptom clusters (SCs) in prostate cancer patients, how SCs relate to HRQoL, and which factors predict SC membership. SCs are derived from patient-reported outcomes using the EORTC QLQC30 and pelvic symptom questionnaires, with machine learning models trained on 80% of data and validated on 20%.",{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/using-machine-learning-to-predict-patient-reported-symptom-clusters-in-prostate-cancer-patients-receiving-radiotherapy-research-article/442389/",{"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/using-machine-learning-to-predict-patient-reported-symptom-clusters-in-prostate-cancer-patients-receiving-radiotherapy-research-article/442389.png","ImageObject",300,407,{"name":92,"@type":93},"Blitz","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-03","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main purpose of this study?","Question",{"text":112,"@type":113},"To examine symptom clusters in prostate cancer patients, assess associations between clusters and HRQoL, and identify predictors of which clusters patients belong to.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were symptom clusters identified?",{"text":117,"@type":113},"Symptom clusters were derived from patient-reported outcomes collected with the EORTC Core Quality of Life questionnaire (EORTC QLQC30) and pelvic symptom questionnaires, using machine learning techniques.",{"name":119,"@type":110,"acceptedAnswer":120},"Which time points and clusters were analyzed in the results?",{"text":121,"@type":113},"Data were analyzed before radiotherapy (T0), end of radiotherapy (T1), 12 months (T2), and 24 months (T3), with clusters identified and compared across these points.","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},442389,1790741568,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"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":145},24464137899374,"https://us-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Rammant etal. Health and Quality of Life Outcomes (2026) 24:3  \n[https://doi.org/10.1186/s12955-025-02460-1](https://doi.org/10.1186/s12955-025-02460-1)  \nHealth and Quality of Life Outcomes  \nRESEARCH Open Access  \nUsing machine learning to predict patient- reported symptom clusters in prostate cancer patients receiving radiotherapy  \nElke Rammant 1,28*, Emile Deman2, Valérie Fonteyne 1,3, Lindsay Poppe 1, Renée Bultijnck 1,3, Piet Dirix4, Gert De Meerleer5, Karin Haustermans5, Ann Van Hecke6,7, Miguel E. Aguado-Barrera8,9, Barbara Avuzzi 10,  \nDavid Azria 11, Jenny Chang-Claude 12,13, Barbara N. Chiorda 10, Ananya Choudhury 14, Patricia Calvo-Crespo8,15,  \nDirk De Ruysscher 16, Antonio Gómez-Caamaño8,15, Philipp Heumann 13, Ashley M. Hopkins 17, Kerstie Johnson 18, Maarten Lambrecht5, Alan Mcwilliam 13, Bradley D. Menz 17, Filip Poelaert 19, Tiziana Rancati20, Kato Rans5, Tim Rattay21, Barry S. Rosenstein22, Petra Seibold 12, Jane Shortall 13, Elena Sperk23, Nora Sundahl24, Christopher J. Talbot21, Ana Vega8,9,25, Peter Vermeulen26, Adam Webb27, Catharine M. L. West 14, Liv Veldeman 1,3 and Sofie Van Hoecke2 on behalf of REQUITE consortium  \nAbstract  \nPurpose/objective Prostate cancer (PC) survivors frequently experience multiple co-occurring symptoms that adversely affect health-related quality of life (HRQoL) . Identifying symptom clusters (SCs) may help to improve symptom management and patient care. The aim of this study is to investigate (1) SCs in PC patients,(2) associations of SCs with HRQoL, and (3) predictors of SCs.  \nMaterial/methods We used data from an international, multi-centre, prospective cohort study (REQUITE) . SCs were identified from patient-reported outcomes collected with the EORTC Core Quality of Life questionnaire (EORTC QLQC30) and pelvic symptom questionnaires. Machine learning techniques identified SCs, associations with HRQoL and SCs predictors. The dataset was divided into training (80%) and validation (20%) cohorts.  \nResults Data were analysed from 1538 (before radiotherapy (T0)), 1490 (end of radiotherapy (T1)), 1322 (12-months (T2)), and 1219 (24-months (T3)) patients. SCs identified at T0: SC1 (gastro-intestinal), SC2 (fatigue, urinary, emotional and cognitive functioning), and SC3 (pain, physical, role, and social functioning) . SCs changed at T1: SC1 (gastrointestinal symptoms), SC2 (fatigue, urinary problems, insomnia), SC3 (social and role functioning), and SC4 (pain, bowel problems, physical, emotional, and cognitive functioning) . At T2, symptoms returned to baseline clusters. SCs including ‘fatigue’ or ‘urinary symptoms’ were most frequent across time-points. At T0, T2 and T3, HRQoL was best predicted by clusters 2 and 3 (35–45% explained variance) . At T1, cluster 4 was the best predictor (52% explained variance) . Planned radiotherapy target volume, prostate specific antigen (PSA) at pre-diagnostic biopsy, age and alcohol consumption were the best predictors of SC2 at T1 and SC3 and fatigue-dyspnoea atT3 .  \n*Correspondence:  \nElke Rammant [El ke.rammant@ugent.be](El ke.rammant@ugent.be)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by ","cbCaid3DJsxqn5LA","https://ap.wps.com/l/cbCaid3DJsxqn5LA","pdf",2204664,11,"English","# Abstract\n## Purpose/objective\n## Material/methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What is the main purpose of this study?\",\"answer\":\"To examine symptom clusters in prostate cancer patients, assess associations between clusters and HRQoL, and identify predictors of which clusters patients belong to.\"},{\"question\":\"How were symptom clusters identified?\",\"answer\":\"Symptom clusters were derived from patient-reported outcomes collected with the EORTC Core Quality of Life questionnaire (EORTC QLQC30) and pelvic symptom questionnaires, using machine learning techniques.\"},{\"question\":\"Which time points and clusters were analyzed in the results?\",\"answer\":\"Data were analyzed before radiotherapy (T0), end of radiotherapy (T1), 12 months (T2), and 24 months (T3), with clusters identified and compared across these points.\"}]","Using machine learning to predict patient-reported symptom clusters in prostate cancer patients receiving radiotherapy - Research Article | PDF",1790700131,28]