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
Iris Asllani Brighton and Sussex Medical School
Multimodal Functional Imaging of the Diseased Brain
Dr Phil Koczan NHS England
Assessing the application of Artificial Intelligence and its use in the NHS
Paul Bentley Imperial College London
How can imaging-AI help stroke management?
Gary Royle University College London
Novel imaging and sensor systems for radiotherapy
Simon Walker-Samuel University College London
Using medical imaging with machine learning to develop efficient tools for diagnosing cancer