Theme 1 – Personalised and Adaptive Radiotherapy

Personalised and Adaptive Radiotherapy

Theme 1 is focusing on accurate detection of organ motion and patients’ outcome utilising advanced imaging and artificial intelligent in radiotherapy pathways.

Theme Leads

Dr Ann Henry (Co-Lead)

Associate Professor, Clinical Oncology

Staff Profile: Dr Ann Henry | School of Medicine | University of Leeds

Dr Bashar Al-Qaisieh (Co-Lead)

Work Package 1 – Intelligent Treatment Motion Adaption

Work package 1 is designing a neural network architecture to predict real-time 4D motion from a sequence of 2D projection images, using graph convolutional networks. This will allow prediction and compensation for organ motion during radiotherapy delivery to optimise patient outcomes by reducing normal tissue irradiation and avoiding geographical miss.

Other WP1 Researchers

Dr Zeike Taylor

Associate Professor in Mechanical Engineering

Staff profile: Dr Zeike Taylor | School of Mechanical Engineering | University of Leeds

Dr Michael Nix

Medical Physicist

Dr Mike Nix is a RadNet Principal Clinical R&D Scientist and an honorary researcher at University of Leeds, where he is leading development of innovative deep-learning methodologies for radiotherapy imaging and adaptive therapy, alongside tools for clinical confidence estimation and effective translation.  

Within Leeds Teaching Hospitals NHS Trust, he leads the development, evaluation and implementation of clinical AI technologies in Radiotherapy, in collaboration with commercial partners.

He is also a Clinical Fellow with Health Education England and NHSx, researching skills-gaps and developing educational strategy around robust and confident AI implementation for healthcare, following a year as one of the inaugural NHS Topol Fellows in Digital Health.

Dr Arezoo Zakeri

Post Doc Research Fellow

Staff profile: Dr Arezoo Zakeri | School of Computing | University of Leeds

Work Package 2 – Multifaceted Treatment Personalisation

Work package 2 has established proof-of-concept for use of distributed learning for predicting outcomes in rare cancers. This is being extended to a large consortium of centres examining outcomes after radiotherapy for anal cancer, including more complex data such as radiomics and patient reported outcomes. We are also developing deep learning methodology for predicting toxicity outcomes after pelvic radiotherapy.

Other WP2 Researchers

Dr Ane Appelt
photo of Ane Appelt

Associate Professor, Radiotherapy Physics

Staff Profile: Dr Ane Appelt | School of Medicine | University of Leeds

Dr Alex Gilbert

Honorary Consultant, Senior Clinical Trials Fellow

Staff Profile: Dr Alex Gilbert | School of Medicine | University of Leeds