GEP-NETs are among the most diagnostically elusive tumours in oncology. Patients typically wait nearly five years before receiving a confirmed diagnosis – and during that time, disease can progress from lower to higher grade, with five-year survival rates ranging from approximately 86% for low-grade disease to as low as 25% for high-grade disease. A significant proportion of patients may never be recognised in routine care at all. This case study asks whether we could find the hidden patients and find them earlier.
What this case study covers
In a collaboration with a leading global pharmaceutical company, Volv Global applied its proprietary machine learning methodology to approximately 24 million de-identified UK primary care records. The work set out to establish whether a meaningful undiagnosed GEP-NET population could be detected in routine data – and what that population looks like.
The case study documents the methodology, the results, and what they mean for pharmaceutical teams working in this disease area.
Key findings at a glance
Download the case study
The full case study – including methodology, cohort construction, model performance, results, and implications for medical affairs, clinical development, and commercial strategy – is available to download below.
Of interest to
- Pharmaceutical: Clinical Development
- Pharmaceutical: Medical Affairs
- Pharmaceutical: Value and Access / HEOR
- Pharmaceutical: Commercial Excellence
- Clinicians and specialist centres in gastroenterology and oncology
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