[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125559-en":3,"doc-seo-125559-105":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},125559,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine-learning with region-level radiomic and dosimetric features for predicting radiotherapy-induced rectal toxicities in prostate cancer patients","Study builds machine-learning models to predict radiation-induced rectal toxicities across three clinical endpoints and evaluates whether radiomic features from radiotherapy planning CT combined with dosimetric features improve performance. Region-level radiomic and dosimetric descriptors are computed by segmenting the rectal wall into regions and sections. A cohort of 183 VoxTox patients is split into training and test sets, with highly correlated features removed via four selection methods and models trained with multiple classifiers and ensemble learning. Results show moderate AUCs, with ensemble radiomic-dosimetric modeling improving prediction.","University of Dundee  \nMachine-learning with region-level radiomic and dosimetric features for predicting radiotherapy-induced rectal toxicities in prostate cancer patients  \nYang, Zhuolin; Noble, David J. ; Shelley, Leila; Berger, Thomas; Jena, Raj; McLaren, Duncan B.  \nPublished in:  \nRadiotherapy and Oncology  \nDOI:  \n10.1016/j.radonc.2023.109593  \nPublication date:  \n2023  \nLicence: CC BY  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in Discovery Research Portal  \nCitation for published version (APA):  \nYang, Z. , Noble, D. J. , Shelley, L. , Berger, T. , Jena, R. , McLaren, D. B. , Burnet, N. G. , & Nailon, W. H. (2023) . Machine-learning with region-level radiomic and dosimetric features for predicting radiotherapy-induced rectal toxicities in prostate cancer patients. Radiotherapy and Oncology, 183 , Article 109593.  \n[https://doi.org/10.1016/j.radonc.2023.109593](https://doi.org/10.1016/j.radonc.2023.109593)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in Discovery Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \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.  \nDownload date: 25. Sep. 2025  \nOriginal Article  \nMachine-learning with region-level radiomic and dosimetric features for predicting radiotherapy-induced rectal toxicities in prostate cancer  \npatients  \nZhuolin Yang a,b, ⇑, David J. Noble c,d, Leila Shelley a, Thomas Berger a, Raj Jena e, Duncan B. McLaren c,d, Neil G. Burnet f, William H. Nailon a,b,g  \na Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU; b School of Engineering, The University of Edinburgh, The King’s Buildings, Mayfield Road, Edinburgh EH9 3JL; c Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh; dDepartment of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU; e The University of Cambridge, Department of Oncology, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ; f The Christie NHS Foundation Trust, Manchester M20 4BX; and g School of Science and Engineering, The University of Dundee, Dundee DD1 4HN, UK  \n\n| a r t i c l e i n f o | a b s t r a c t\u003Cbr>Background and purpose: This study aims to build machine learning models to predict radiation-induced rectal toxicities for three clinical endpoints and explore whether the inclusion of radiomic features calculated on radiotherapy planning computerised tomography (CT) scans combined with dosimetric features can enhance the prediction performance.\u003Cbr>Materials and methods: 183 patients recruited to the VoxTox study (UK-CRN-ID-13716) were included. Toxicity scores were prospectively collected after 2 years with grade 􀀁 1 proctitis, haemorrhage (CTCAEv4.03); and gastrointestinal (GI) toxicity (RTOG) recorded as the endpoints of interest. The rectal wall on each slice was divided into 4 regions according to the centroid, and all slices were divided into 4 sections to calculate region-level radiomic and dosimetric features. The patients were split into a training set (75%, N = 137) and a test set (25%, N = 46). Highly correlated features were removed using four featureselection methods. Individual radiomic or dosimetric or combined (radiomic + dosimetric) features were subsequently classified using three machine learning classifiers to explore their association with these radiation-induced rectal toxicities.\u003Cbr>Results: The test set area under the curve (AUC) values were 0.549, 0.741 and 0.669 for proctitis, haemorrhage and GI toxicity prediction using radiomic combined with dosimetric","cbCailiVhaRxc9U6","https://ap.wps.com/l/cbCailiVhaRxc9U6","pdf",1075374,1,7,"English","en",105,"# Background and purpose\n# Materials and methods\n## Dataset and endpoints\n## Feature extraction and selection\n## Model training\n# Results\n# Conclusions","[{\"question\":\"What problem does the study address?\",\"answer\":\"It aims to predict radiation-induced rectal toxicities in prostate cancer patients for three clinical endpoints and to test whether combining region-level radiomic and dosimetric features improves predictive performance.\"},{\"question\":\"How were the radiomic and dosimetric features computed?\",\"answer\":\"The rectal wall on each planning CT slice was divided into regions based on the centroid, and all slices were divided into sections to calculate region-level radiomic and dosimetric features.\"},{\"question\":\"What performance did the models achieve?\",\"answer\":\"Test-set AUC values were reported for proctitis, haemorrhage, and GI toxicity, and the ensembled radiomic–dosimetric model reached an AUC of 0.747 for haemorrhage.\"}]","Machine-learning with region-level radiomic and dosimetric features for predicting radiotherapy-induced rectal toxicities in prostate cancer patients | 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problem does the study address?","Question",{"text":75,"@type":76},"It aims to predict radiation-induced rectal toxicities in prostate cancer patients for three clinical endpoints and to test whether combining region-level radiomic and dosimetric features improves predictive performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the radiomic and dosimetric features computed?",{"text":80,"@type":76},"The rectal wall on each planning CT slice was divided into regions based on the centroid, and all slices were divided into sections to calculate region-level radiomic and dosimetric features.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance did the models achieve?",{"text":84,"@type":76},"Test-set AUC values were reported for proctitis, haemorrhage, and GI toxicity, and the ensembled radiomic–dosimetric model reached an AUC of 0.747 for 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