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Yan Chen

Professor of Digital Screening, Faculty of Medicine & Health Sciences


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    Nottingham City Hospital
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    NG5 1PB
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Professor Chen was appointed to the Chair of Digital Screening and is the head of the Digital Cancer Screening Research Group at University of Nottingham, having previously been the Director of the Applied Vision Research Centre at Loughborough University.

Professor Chen has led the development of the world-first national digital assessment and training scheme in radiology (PERFORMS) for breast screening readers which is fully embedded within the UK's National Breast Screening Programme for more than 34 years in order to help to ensure the quality of radiology reporting and improve readers' imaging interpretation skills ( This platform is endorsed by the Royal College of Radiologists, comprises a mandated activity for radiologists' annual appraisal and is also accredited by the European accreditation Council for Continuing Medical Education.

Due to its success, her research has been extended in the UK lung cancer screening programme (PERFECTS) which is the first assessment and training platform that aims to ensure appropriate interpretation of lung CT scans in order for radiologists to benefit patient outcome and streamline clinician workload ( It is the external quality assurance for lung screening programme and self-assessment and training to screening readers. The scheme addresses the need for continuous improvement in radiology reporting ability which is vital for any successful screening programme.

Expertise Summary

Professor Chen has received multiple large research grant awards as PI/Co-I, working closely with national and international organisations, including Horizon 2020, WHO, Innovate Research UK, National Institute for Health Research, and NHS England. Her research interests are in medical imaging, covering cancer screening, early cancer detection, and diagnostic accuracy in CT, breast mammography and tomosynthesis, contrast mammography, and digital pathology specialities. She's specifically interested in quality assurance of health professionals and Artificial Intelligence (AI) programmes that interpret medical images, as well as using eye tracking technology and developing AI applications to aid health professionals' training.

Yan's current research projects are:

  • PERFORMS (Personal Performance in Mammographic Screening): UK National mandated Breast Cancer Screening External Quality Assurance scheme. UK Breast Screening Programme.
  • IMPROVE: a specialist training scheme for radiographers who undergo breast screening training.
  • PERFECTS (PERFormance Evaluation for CT Screening): the first national External Quality Assurance scheme for lung cancer imaging assessment. The scheme is used to ensure appropriate interpretation of lung scans which benefits patient outcome.
  • DART (The Integration and Analysis of Data using Artificial Intelligence to Improve Patient Outcomes with Thoracic Diseases): an AI project aims to develop integrated diagnostics that will enable the earlier diagnosis of lung cancer for increased patient survival and large time and cost savings to the NHS.
  • MyPeBS (My Personal Breast Screening): is a major ambitious European initiative. This unique international clinical study compares a personalised risk-based screening strategy (based on the individual women's risk of developing breast cancer) to standard screening among 85,000 women aged 40 to 70 in 6 countries: Belgium, France, Israel, Italy, United Kingdom and Spain.
  • PROSPECTS (Prospective Randomised Trial of Digital Breast Tomosynthesis): A randomised contrail trial involving 100,000 female volunteers to compare the use of traditional 2D mammograms with the new 3D breast imaging technology. The trial will measure the efficacy and cost-effectiveness of the two technologies.

Professor Chen is also working on AI evaluation and benchmarking to ensure that AI can be safely implemented into the clinical setting to aid cancer detection, particularly in the screening setting. She advocates risk stratified approaches to screening programmes and is currently involved with the MyPEBS clinical trial, as well as screening technology improvement, such as the potential uptake of breast tomosynthesis in screening, part of the PROSPECTS trial.

She has held various positions, including Honorary Member of Royal College of Radiologists, RSNA Machine-Learning Committee Member, Chair of SPIE Medical Imaging, Associate Editor for British Journal of Radiology and Journal of Medical Imaging.

Research Summary

I have built an internationally recognised online teaching and training resource called 'PERFORMS' for breast clinicians, radiologists and radiographers which helps participants to improve their… read more

Recent Publications

Current Research

I have built an internationally recognised online teaching and training resource called 'PERFORMS' for breast clinicians, radiologists and radiographers which helps participants to improve their mammographic imaging interpretation skills and remain up to date in their specialities. I design the new PERFORMS test sets every year, selecting challenging breast screening cases that can provide participants with information about their individual strengths and weaknesses in reading performance, identify under-performing outliers and accommodate further tailored training to improve their performance. The cancer detection performance evidence from this has then been adopted as the model of service delivery (Appendix 4).

I am developing an online teaching and training module called 'PERFECTS', a new and ground-breaking platform that provides teaching and learning to all radiologists in the UK who interpret CT scans in order for them to benefit patient outcome and streamline clinician workload.

I have also contributed to the development of the 'IMPROVE' scheme, a special training case set designed for technologists and advanced practitioner radiographers who undergo breast screening training. As individuals undergo such training, they examine the same test set both early in their training and towards the end of their training. Differences in performance between the first and second occasion help to give insight into aspects of individual participants' improved skills as they have progressed through their mammographic education. This module, which is continually modified to keep it contemporary in terms of content and relevance, remains innovative in that it attracts and trains individuals from all UK screening programme national training centres.

Following my move to UoN I have worked very closely with UoN Technology Transfer Office (TTO) and Nottingham Technology Venture (NTV). I gained NTV business plan approval for an international medical imaging assessment MedTech spin-out company supported by UoN TTO. The approval has now converted to a private limited spin- out company, PERFORMS Assessment Ltd.

School of Medicine

University of Nottingham
Medical School
Nottingham, NG7 2UH

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