The Integration and Analysis of Data using Artificial Intelligence to Improve Patient Outcomes with Thoracic Diseases (DART)

We have assembled a team from academia, industry, charity and the NHS to integrate data from diagnostic technologies in novel ways using artificial intelligence algorithms. This will enable the earlier diagnosis of lung cancer for increased patient survival and large time and cost savings to the NHS. Lung cancer is the biggest cause of cancer death in the UK and worldwide, with £307M/year cost to the NHS England. Earlier diagnosis is critical for increasing survival, however the current diagnostic pathway is flawed. Randomised controlled trials show that screening programmes can reduce mortality by 20-26%, and detect co-morbid disease and has led to the establishment of a new £70M NHS England lung cancer screening programme, launching this year and running for four years. There are at present no commercially available tools that are proven to improve patient care compared to the current screening guidelines. To address this need, we will use our established data infrastructure to collect and transfer clinical data, CT scans, digitised images of stained tissue sections (digital pathology) and blood-derived data from the consented participants of the lung cancer screening programme to our secure data 'lake' based at the University of Oxford. For the first time, we will integrate these diverse data types using our Artificial Intelligence algorithms to enable further and improved characterisation of disease, than is possible by a radiologist alone. By linking the additional information available at diagnosis to outcome data, we will also be able to refine the lung cancer treatment guidelines. We will also link to data from primary care to better define risk in the general population. With this approach, we aim to (1) more accurately diagnose lung cancer with enhanced prognostic information; (2) reduce the occurrence of harmful invasive procedures in the diagnostic pathway; (3) improve patient selection for lung cancer screening and reduce costs; (4) improve assessment of risks from co-morbidities; (5) generate and store a large amount of data that can be used for future research. We will define a new set of standards for lung cancer diagnosis that will improve patient care and generate large cost and time savings to the NHS, and also help patients with lung cancer elsewhere in the world.