[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118801-en":3,"doc-seo-118801-105":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},118801,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data","Dynamic susceptibility contrast (DSC-) MRI enables perfusion estimation by tracking the passage of a gadolinium-based contrast agent and the resulting T2/T2* signal changes over time. This study evaluates qualitative review (QR) for paediatric normal brain data quality and builds an automated alternative using machine learning. Signal–time course quality metrics (SDNR, RMSE, FWHM, PSR) and QR labels train classifiers, with thresholds selected from QR outcomes to optimize performance.","BJR [https://doi.org/10.1259/bjr.20201465](https://doi.org/10.1259/bjr.20201465)  \n\n| \u003Cbr>Received:\u003Cbr>21 December 2020\u003Cbr>Accepted:\u003Cbr>24 January 2023\u003Cbr>Published online:\u003Cbr>16 February 2023\u003Cbr>© 2023 The Authors. Published by the British Institute of Radiology under the terms of the Creative Commons Attribution 4.0 Unported License [http://creativecommons.org/l](http://creativecommons.org/l)icenses/by/4 .0/, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.\u003Cbr>Cite this article as:\u003Cbr>Powell SJ, Withey SB, Sun Y, Grist JT, Novak J, MacPherson L, et al. Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data. Br J Radiol (2023) 10.1259/bjr.20201465. |  |\n| --- | --- |\n| FULL PAPER\u003Cbr>Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data\u003Cbr>1,2STEPHEN J. POWELL, 2,3,4STEPHANIE B. WITHEY, 2,5YU SUN, 2JAMES T. GRIST, 2,3,6JAN NOVAK,\u003Cbr>7LESLEY MACPHERSON, 8LAURENCE ABERNETHY, 9BARRY PIZER, 10RICHARD GRUNDY, 10,11,12PAUL S. MORGAN, 10,13TIM JASPAN, 14SIMON BAILEY, 15DIPAYAN MITRA, 16DOROTHEE P. AUER, 8SHIVARAM AVULA,\u003Cbr>2,3,17THEODOROS N. ARVANITIS and 2,3ANDREW PEET\u003Cbr>1Physical Sciences for Health CDT, University of Birmingham, Birmingham, United Kingdom\u003Cbr>2 Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom\u003Cbr>3Department of Oncology, Birmingham Children’s Hospital, Birmingham, United Kingdom\u003Cbr>4RRPPS, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom\u003Cbr>5School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China\u003Cbr>6Department of Psychology, Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom\u003Cbr>7Radiology, Birmingham Children’s Hospital, Birmingham, United Kingdom\u003Cbr>8Radiology, Alder Hey Children’s NHS Foundation Trust, Liverpool, United Kingdom\u003Cbr>9Oncology, Alder Hey Children’s NHS Foundation Trust, Liverpool, United Kingdom\u003Cbr>10The Children’s Brain Tumour Research Centre, University of Nottingham, Nottingham, United Kingdom 11Medical Physics, Nottingham University Hospitals, Nottingham, United Kingdom\u003Cbr>12NIHR Nottingham Biomedical Research Centre, Nottingham, United Kingdom\u003Cbr>13Radiology, Nottingham University Hospitals, Nottingham, United Kingdom\u003Cbr>14Sir James Spence Institute of Child Health, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom\u003Cbr>15Neuroradiology, The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom\u003Cbr>16Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom\u003Cbr>17 Institute of Digital Healthcare, WMG, University of Warwick, Coventry, United Kingdom\u003Cbr>Address correspondence to: Professor Andrew Peet\u003Cbr>E-mail: [a.peet@bham.ac.uk](a.peet@bham.ac.uk) |  |\n| Objective: Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. Methods: 1027 signal–time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen’s κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal–time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier.\u003Cbr>Results: Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for S","cbCaijMIj1ZJlrHK","https://ap.wps.com/l/cbCaijMIj1ZJlrHK","pdf",1655409,1,13,"English","en",105,"# Objective\n# Methods\n# Results\n# Conclusion\n# Advances in knowledge\n# Introduction","[{\"question\":\"What is the goal of this study on DSC-MRI data quality?\",\"answer\":\"To assess how well qualitative review (QR) determines DSC- MRI data quality in paediatric normal brain and to develop a machine-learning-based automated alternative.\"},{\"question\":\"Which signal–time course measures were used to train the classifiers?\",\"answer\":\"SDNR, RMSE, FWHM, and percentage signal recovery (PSR) were computed for the signal–time courses and used alongside QR results to train classifiers.\"},{\"question\":\"How did the best machine learning classifier perform?\",\"answer\":\"The random forest classifier performed best, achieving sensitivity 0.94, specificity 0.83, precision 0.93, classification error 9.3%, and area under the curve 0.89 at the 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is the goal of this study on DSC-MRI data quality?","Question",{"text":75,"@type":76},"To assess how well qualitative review (QR) determines DSC- MRI data quality in paediatric normal brain and to develop a machine-learning-based automated alternative.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which signal–time course measures were used to train the classifiers?",{"text":80,"@type":76},"SDNR, RMSE, FWHM, and percentage signal recovery (PSR) were computed for the signal–time courses and used alongside QR results to train classifiers.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the best machine learning classifier perform?",{"text":84,"@type":76},"The random forest classifier performed best, achieving sensitivity 0.94, specificity 0.83, precision 0.93, classification error 9.3%, and area under the curve 0.89 at the selected 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