Radiograph Accelerated Detection and Identification of Cancer in the Lung

Lung cancer is the most common cause of cancer death in the UK yet compared to Europe it has low survival rates.The NHS aims to find 75% of cancers at an early stage as this can improve the chances of survival. To support this target, Qure.ai have developed the UK-approved qXR product, which is a software program that automatically analyses chest x-rays using artificial intelligence to identify features associated with lung cancer, indicative of other diagnoses, or that contain no abnormal features ('normal'). qXR is a class IIb medical device that can be used by radiologists to prioritise reporting based upon the presence or absence of these features. This may improve the accuracy and efficiency of reporting these images. The project includes different elements including: i) Clinical effectiveness study across 3 sectors within NHS Greater Glasgow and Clyde (NHSGGC).The primary objective is to assess the clinical effectiveness of qXR to prioritise patients that have suspected lung cancer (identified from AI analysis of a chest x-ray) for follow-on CT. Primary study outcome measure - Time to 'decision to recommend CT', or to a decision not to undertake CT for CXR acquired with USC (CXR acquired to CXR reported). Secondary objectives include: i) To assess the potential utility of qXR within the optimised lung cancer pathway in terms of the impact on both patient treatment and radiological workflow. ii) A technical evaluation utilising retrospective and prospective cohorts. The technical retrospective study will determine the performance of qXR using a sample of 1000 CXR images from all chest x-ray referral sources across all sectors (this differs from the prospective study, which only examines outpatient referred chest x-rays). iii) A health economic evaluation. Use of per patient healthcare utilisation costs to model cost benefits of qXR, including implementation of supported reporting of normal CXR. iv) A qualitative evaluation to assess acceptability and barriers to scale-up and implementation