Developing a risk score for pancreatic cancer diagnosis

This project on pancreatic cancer initiated in 2019, with an award from the Pancreatic Cancer Research Fund.  The aim is to use historic GP and hospital data to identify patients who are most likely to have early stage pancreatic cancer. We are using machine learning techniques to understand if there are combinations of particular health problems, illnesses, or symptoms experienced only by patients who are later diagnosed. Ultimately, improving the triage of these patients means that targeted diagnostic tests could lead to the pancreatic cancer being diagnosed earlier and treated more effectively.

Dr Woods summarises our approach

ESMO WCGIC 2020 | Machine learning techniques in pancreatic cancer screening | VJOncology

Study outputs

Our pilot study has been published in PLOS ONE.

We have also produced an infographic on the study.

Project Staff

Dr. Laura Woods

Principal Investigator
Associate Professor in Epidemiology

Dr. Ananya Malhotra

Research Fellow

Prof. Bernard Rachet

Principal Investigator
Professor of Cancer Epidemiology