Development and validation of a decision risk tool for early diagnosis of pancreatic ductal adenocarcinoma (PDAC) among new-onset diabetes in UK primary care
What is the aim of the study and why is it important?
Despite improvements in treatment and care, pancreatic cancer remains a very deadly disease. This is mainly due to patients presenting at late stages of disease when there are fewer treatment options. Patients with pancreatic cancer diagnosed at earlier stages are more likely to be treated successfully and survive longer. Unfortunately, pancreatic cancer is challenging to detect early as it mostly shows no or vague symptoms in early stages. However, many patients who develop pancreatic cancer also have been shown to have diabetes diagnosed in the months/years before a pancreatic cancer diagnosis. A new diagnosis of diabetes is therefore a potential indicator of pancreatic cancer which can be used for early detection. However as diabetes is a relatively common condition in the population, it remains impracticable to refer everyone for pancreatic cancer scans. Hence, GPs will need a more accurate tool to better identify patients who will be more likely to have pancreatic cancer for further tests.
In this study, we will perform the following
1. Develop and validate a risk-prediction tool for early diagnosis of PDAC among new-onset type-2 diabetes mellitus (T2DM) in primary care
2. Examine the scope for machine learning approaches to develop a risk prediction tool for PDAC in people with new-onset T2DM, and compare these approaches with traditional statistical modelling
How is the research being done?
This study will build a risk prediction tool to identify patients who are more likely to have or develop pancreatic cancer among those with new-onset diabetes. use information from QResearch – a database containing anonymised GP records of patients in England, taken over a period of over 30 years. This will be linked to hospital records, cancer registry and death records to search for individual new-onset diabetes patient characteristics (e.g. age, gender, lifestyle) that are associated with a later diagnosis of pancreatic cancer. We will compare the accuracy of risk prediction using computer algorithms and evaluate if a risk-based tool used in GP settings will be effective to detect pancreatic cancer at earlier stages among new-onset diabetes patients.
Dr Pui San Tan, University of Oxford and Prof Julia Hippisley-Cox, University of Oxford
Lead Applicant Organisation Name
University of Oxford
Location of research
University of Oxford
Date on which research approved
Project reference ID
Generic ethics approval reference
Are all data accessed are in anonymised form?
Brief summary of the dataset to be released (including any sensitive data)
EMIS, HES, ONS, Cancer Registry
Implications and Impact
The risk prediction tool developed in this study will provide a very useful clinical decision tool for use in primary care to better assess pancreatic cancer risk and guide further referrals/scans to aid early detection of pancreatic cancer.
Pancreatic Cancer UK
Public Benefit Statement
Dr Ashley Clift, University of Oxford
Dr Shivan Sivakumar, University of Oxford
Dr Martina Patone, University of Oxford
Mr Weiqi Liao, University of Oxford
Miss Winnie Xue Mei, University of Oxford
Dr Rachael Bashford-Rogers, University of Oxford
Prof Carol Coupland, University of Oxford & University of Nottingham
Prof Steve Pereira, University College London
Prof Richard Clifton, University of Oxford
Predicting risk of pancreatic cancer in individuals with new-onset type-2 diabetes in primary care: protocol for the development and validation of a clinical prediction model (QPancreasD)
Authors: Pui San Tan, Ashley Kieran Clift, Weiqi Liao, Martina Patone, Carol Coupland, Rachael Bashford-Rogers, Shivan Sivakumar, David Clifton, Stephen P Pereira, Julia Hippisley-Cox
Trusted Research Environment (TRE)