This study will use advanced data science techniques to develop a tool that can predict if a person has undiagnosed cancer, based on their blood test results.
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By exploring how GPs interpret and act on non-specific upper GI symptoms this project aims to optimise diagnostic strategies for upper abdominal symptoms to improve pathways to the early detection of pancreatic cancer.
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A study to demonstrate the impact of the CanTest Collaborative in strengthening the research community for early cancer diagnosis in primary care – quantitative and qualitative research techniques used in Social Network Analysis will be used to track the growth and development of the CanTest Network throughout the 5-year collaboration
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Colorectal cancer (CRC) is among the most frequent types of cancer worldwide, and a leading cause of cancer death. Patients presenting with ‘alarm’ symptoms may be referred by their general practitioner. We aim to evaluate the diagnostic accuracy and applicability of FIT in this group of patients.
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Using the VCCC Cancer Health Data Platform, this project will investigate primary care across the cancer journey, including diagnosis, during acute treatment and follow-up survivorship care.
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This project will provide new insight into the management of anaemic patients, the cancer risk for selected anaemia subtypes and the distribution of cancer-related anaemia.
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There is accumulating evidence that AI can assist clinicians to make better clinical decisions or even replace human judgement in certain areas of healthcare. In this scoping review we will focus on which modalities of AI for early detection and diagnosis of cancer are ready for possible implementation in the NHS.
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This project aims to generate evidence for optimising diagnostic strategies in patients with comorbidities experiencing possible cancer symptoms, in order to improve diagnostic timeliness.
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