DOLCE - using Artificial Intelligence to diagnose lung cancer

DOLCE: Determining the Impact of Optellum’s LCP Artificial Intelligence Solution on Service Utilisation, Health Economics and Patient Outcomes

About this study

Lung cancer is the deadliest type of cancer. In the UK, 47,000 are newly diagnosed each year with over 35,000 people dying from it each year. The low survival rate for lung cancer is because it is often detected late (partly due to a lack of symptoms), when the chances of curing it are considerably lower.

At present, the primary way to diagnose lung cancer earlier is to follow up on small ‘spots’ or lung nodules found on computed tomography (CT) scans (a type of imaging used for looking inside the body) that have been undertaken for other conditions (so called “incidental” findings). In most patients, these are harmless. However, in a very few, they indicate the early stages of cancer.

The study is investigating a new software programme to help analyse ‘lung nodules’, which are sometimes found on CT scan. The aims of the study is to determine whether the Lung Cancer Prediction generated by the software can improve measures of clinical utility, patient outcomes and cost effectiveness versus standard of care and also determine whether it can help the NHS avoid unnecessary procedures and imaging, increase limited capacity, and save money.

Who to contact for more information

Professor David Baldwin - Consultant Respiratory Physician, University of Nottingham

Sophie Hayes - Research & Innovation at Nottingham University Hospitals

Where this study is taking place

  • Nottingham University Hospitals NHS Trust
  • King's College Hospital NHS Foundation Trust
  • St. George’s University Hospitals NHS Foundation Trust
  • University Hospitals of Derby And Burton NHS Foundation Trust
  • University Hospitals of Leicester NHS Trust
  •  Royal Free NHS Foundation Trust
  • Leeds Teaching Hospital NHS Trust
  • The Royal Marsden NHS Foundation Trust
  • University College London Hospital NHS Foundation Trust
  • Oxford University Hospitals NHS Foundation Trust