Speaker
Description
Professor Marianne Aznar is Professor of Radiation Oncology Physics at the University of Manchester and Honorary medical physicist at the Christie NHS Foundation Trust. She trained as a clinical physicist in Canada and Denmark and joined the Radiotherapy-Related Research Group in 2017.
Her research focuses on the side-effects of cancer treatment, especially for patient groups who can expect a long life after their original cancer treatments (diagnoses include, e.g., Hodgkin Lymphoma, breast cancer and childhood cancer), and more recently, the application of artificial intelligence to radiotherapy practice. Her current projects include BRAINatomy, looking at cognition in survivors of childhood brain tumours (https://www.mcrc.manchester.ac.uk/brainatomy-2/), and SMILE which looking at facial deformation and dental toxicities in children treated with Proton Beam Therapy (https://www.childrenscancerresearchfund.co.uk/pioneering-research-for-children-with-head-neck-cancers/). Internationally, she is involved with the European Society of Radiotherapy and Oncology (ESTRO) where she serves as an elected board member and a teaching faculty member. She also represents radiotherapy professionals on the Cardio-Oncology Council of the European Society of Cardiology and contributed to the recent international guidelines for cardio-oncology.
Professor Aznar has published extensively in the field of medical physics and radiotherapy and serves as the physics editor for Radiotherapy and Oncology.
Summary
Treatment outcome models turn dose distributions into predictions of side-effects or tumour control but most models are never adopted in clinical practice. This talk offers early-career physicists a practical orientation to building credible outcome models, that are both clinically useful and trustworthy.
We will review “good practice” in modelling research: posing a clear clinical question, characterising the exposure carefully, and appreciating that the outcome itself is often the hardest part. Many endpoints that matter (e.g. cognitive change, late toxicity, quality of life) are ill-defined, subjective, or difficult to measure reliably, and a model inherits every weakness in how they were captured, so awareness of the clinical context matters as much as the mathematics. I will present two examples from our current work in paediatric radiotherapy: BRAINatomy (cognitive and endocrine function) and SMILE (dento-facial effects). We will also discuss new approaches as alternatives to traditional DVH-based dose-response analysis, e.g. voxel-wise analysis which can move beyond mean-organ-dose metrics to localise where dose drives an effect.
This talk will centre on interpretability, reproducibility, and honest reporting of uncertainty. The aim is not a comprehensive methods course but enough grounding to approach outcome modelling critically, and to tell good practice from bad in the literature.