Community of Practice: Causal Modelling of Early-Onset Cancer (Part 2)

On behalf of the Cancer Research UK Scotland Centre and the Causality in Healthcare AI Hub, I’m pleased to invite you to join a Community of Practice focused on:

Causal Modelling of Early-Onset Cancer

A two-part workshop event will bring together the cancer research, epidemiology, data science, and modelling communities to explore how causal AI methods can help us understand early-onset cancers.

The Challenge

Although we have access to extensive national healthcare data tracking cancer journeys since the 1970s, it’s challenging to capture and model individual exposures to key determinants—such as deprivation, life-style, education, ethnicity, and environmental exposures—at critical periods before diagnosis (e.g. in childhood and adolescence.)

While links between socio-economic status, smoking, obesity, and cancer mortality are established, early-life influences remain less understood. The workshop will address challenges in integrating diverse data sources with healthcare records and developing models to clarify each factor’s role in cancer risk.

We have a strong interest in colorectal cancer and exploring dietary risk factors, however longitudinal nutrition data is scarce. Building such data resources could also open new avenues for studying cancer and emerging health risks like micro- and nano-plastics, as vectors for forever chemicals and environmental toxins.

Cancer Research UK Scotland Centre

The CRUK Scotland Centre was established in 2022 and has been funded by Cancer Research UK for five years. It is a partnership between the Universities of Edinburgh and Glasgow, the MRC Human Genetics Unit, the Cancer Research UK Scotland Institute, NHS Lothian and NHS Greater Glasgow and Clyde. Our aim is to translate innovative discovery science into patient benefit. Our research focuses on cancers that profoundly affect people in Scotland (Colorectal, Hepatobiliary, Pancreatic Cancer and Mesothelioma), and cancers in which we have a growing expertise (Brain Tumours and Gynaecological Cancers). We aim to establish translational pipelines that take scientific discoveries, into pre-clinical and clinical research with long-term goal of clinical implementation. Our research is supported by an additional theme on Data, which, in collaboration with Public Health Scotland, harnesses Scotland’s extensive clinical data to provide a comprehensive view of patients' journeys while integrating clinical data with pre-clinical model data using machine learning and deep learning for comparative, multi-omic, cross-species analyses.

CHAI Hub

CHAI is the Causality in Healthcare AI Hub that unites an international consortium of universities, industry partners, government bodies, and regulatory entities to develop cutting-edge causal AI innovations to enhance patient care and outcomes.

We are working to develop an explainable causal AI platform specifically addressing unique challenges from healthcare across prevention, diagnosis, and treatment. We believe that to move AI forward, we need to build models incorporating both theory and observation. This will create models that are more transportable, explainable, fair, and of more direct relevance for decision support.

The CHAI Hub co-develops research with clinical experts, policy makers, and patients to address complex and heterogenous data structures, nuanced real-world problems, and a rapid pathway to societal and economic impact.

Know what’s Happening Next — before everyone else does.

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Host or Publisher School of Engineering