Prof James O’Connor
University of Manchester / Cancer Research UK / The Christie
James O’Connor is Professor of Radiology at The University of Manchester, UK and a Cancer Research UK (CRUK) Advanced Clinician Scientist Fellow. He is also an Honorary Consultant in Radiology at The Christie NHS Foundation Trust, Manchester, UK, where he specialises in cross-sectional and functional imaging. He co-leads the CRUK National Cancer Imaging Translational network that links seven leading centres of cancer imaging research together in the UK.
He graduated in medicine from the University of Cambridge, UK, and University of London, UK. He completed a PhD on imaging biomarkers in cancer and is an international authority on the development and translation of MRI biomarkers radiobiology from laboratory to clinic. Current research focuses on using biomarkers to guide decision making in early phase clinical trials of drug-radiation combinations, including hypoxia modification, targeting angiogenesis and modulating tumour immunology.
Developing a new MRI method of tracking hypoxia in cancer
Oxygen deprivation (hypoxia) causes treatment resistance and poor clinical outcome in most solid tumours. Imaging methods are required to identify, quantify and track change in hypoxia, while also being cost-effective and readily available to healthcare providers. This seminar describes the emerging technology ‘Oxygen-enhanced MRI’. Current evidence that this technology detects and maps radiation- and drug-induced hypoxia modification are discussed along with future applications such as guiding adaptive radiotherapy planning.
EVEN MORE SEMINARS
Dr Qaiser Malik MSE Radiology
Radiology Improvement and Transformation
Professor Ian Marshall University of Edinburgh
Developments in neuro MRI: more information, faster
Professor David J. Lurie University of Aberdeen
Fast Field-Cycling MRI: A New Diagnostic Imaging Modality
Ernesto A. Cerdena AtlantiCare Regional Medical Center (a member of Geisinger), Atlantic City, New Jersey, USA
UPDATE on Lessons Learned in Adopting Clinical Decision Support Mechanism In Diagnostic Imaging
Xiaohong (Sharon) Gao Middlesex University
Application of deep learning techniques to medical images for early diagnosis