Cambridge MedAI Seminar Series

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• Raj Jena and Yizhou Wan
• Tuesday 29 October 2024, 12:00-13:00
• Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge.

If you have a question about this talk, please contact Ines Machado.

The Cancer Research UK Cambridge Centre and the Department of Radiology at Addenbrooke’s are pleased to announce a seminar series on Artificial Intelligence (AI) in Medicine, which aims to provide a comprehensive overview of the latest developments in this rapidly evolving field. As AI continues to revolutionize healthcare, we believe it is essential to explore its potential and discuss the challenges and opportunities it presents.

The seminar series will feature prominent experts in the field who will share their research and insights on a range of topics, including AI applications in disease diagnosis, drug discovery, and patient care.

Each seminar will involve two talks, followed by an interactive discussion with a light lunch from Aromi! We hope that this seminar series will be a valuable platform for researchers, practitioners and students to learn about the latest trends and explore collaborations in the exciting field of AI in Medicine.

This month’s seminar will be a joint event with the Brain Cancer Virtual Institute (BCVI), an institute within the CRUK Cambridge Centre. The BCVI has evolved from the CRUKCC Neuro-Oncology Programme and is a network of researchers and clinicians in Cambridge working to improve outcomes for brain tumour patients.

The event will be held on 29 October 2024, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom. A light lunch from Aromi will be served from 11:45.

This month will feature the following talks:

Generative AI in Radiotherapy – Gimmick or Game Changer? – Prof. Raj Jena, Professor of AI in Radiation Oncology and Honorary Consultant in Clinical Oncology, University of Cambridge Department of Oncology and Cambridge University Hospitals

Raj Jena is a Professor of AI in Radiation Oncology based at the University of Cambridge Department of Oncology and Cambridge University Hospitals. His research interests focus on clinical image processing, data science and machine learning applications. Raj is the chief Investigator for Hamlet.rt, a multi-centre radiomics study in radiation therapy open at over 12 sites over the UK and Tata medical centre in Kolkata. He is a member of the Royal College of Radiologists’ AI in Clinical Oncology (AICO) committee and is Director of the Oncology Translational Research Collaboration (O-TRC) at the National Institute for Healthcare Research. Raj enjoys working at the interface between the clinical, academic and commercial sectors. Following successful collaborations with Siemens and other imaging companies, he worked a clinical consultant to the InnerEye team at Microsoft Research. Here Raj had the opportunity to work with thought leaders in medical image analysis, and subsequently led the NHS AI lab funded OSAIRIS project, which developed the first cloud based open source imaging AI solution to be deployed at Addenbrooke’s Hospital. Raj is now applying his knowledge of machine learning and image processing to the STELLA project, an international collaboration developing a novel smart radiotherapy unit for low- and middle-income countries.

Abstract: Generative AI models have demonstrated amazing capabilities in the creation of synthetic data, but how useful can they be in discovery science? I will discuss the application of a generative machine learning model to a problem relating to modelling late effects of radiotherapy in the brain.

Utilising AI for outcome prediction in glioma surgery: challenges and opportunities – Yizhou Wan, Clinical Research Fellow and Honorary Speciality Registrar, Brain Tumour Imaging Lab, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge

Yizhou Wan is a Neurosurgery Resident with an interest in how we can use advanced imaging methods to detect tumour effects on the brain and optimise surgical and non-surgical treatments for brain cancer. His PhD studies the impact of surgery on cognition and tumour networks using advanced neuroimaging. Prior to starting doctoral training at the University of Cambridge, Yizhou studied Medicine at Imperial College London followed by Academic Foundation Training in London and Neurosurgery training in Oxford. His research is funded by a Cancer Research U.K, Clinical Research Training Fellowship and Royal College of Surgeons England Research Fellowship.

Abstract: High-grade glioma is the most common primary brain cancer. Cognitive symptoms are the commonest neurological deficits reported by patients. They have a significant impact on quality of life and reduce survival. Cognition depends on brain network functioning. Gliomas have been shown to integrate into neuronal circuits and disrupt whole-brain connectivity. Tumours interact with brain networks to disrupt cognition and promote tumour growth. My PhD studies the impact of surgery on cognition and tumour networks. By using diffusion MRI we can identify imaging markers associated with tumour-related injury and computationally model the effects of surgery on brain function. I hope to predict preoperatively the effect of surgery on postoperative cognition and survival, by treating glioma as a whole-brain disease. This will facilitate shared-decision making between surgeons and patients to formulate personalised resections which preserve maximise survival. I will also discuss the challenges involved in applying AI to clinical imaging datasets and current areas where we may be able to make progress in translating AI to the clinic and Operating room.

This is a hybrid event so you can also join via Zoom: https://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09 Meeting ID: 990 5046 7573 and Passcode: 617729

We look forward to your participation! If you are interested in getting involved and presenting your work, please email Ines Machado at im549@cam.ac.uk

For more information about this seminar series, see: https://www.integratedcancermedicine.org/research/cambridge-medai-seminar-series/

This talk is part of the Cambridge MedAI Seminar Series series.

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