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.

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) To assess the safety of qXR at ruling out patients from entry onto the cancer pathway iii) 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

A clinical effectiveness study will be conducted in 3 NHS Greater Glasgow and Clyde sectors over a 12-month period.

Sectors will be identified and initiated into the qXR solution with a 30 day implementation period. The order in which sites will receive the qXR intervention will be determined by computer-based randomisation.

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). An economic evaluation will be conducted comparing costs and outcomes with and without the introduction of qXR. The software potentially impacts costs via two mechanisms: the identification of normal can enhance efficiency of CXR reporting; and the identification of USCs can support the prioritisation of CXRs that show signs of lung cancer, accelerating the provision of CT, which leads to faster diagnosis and treatment, and ultimately better outcomes.

Qualitative evaluation: To determine acceptability, staff interviews and patient focus groups will be carried out.

Data will be collected by an experienced qualitative researcher using a semi-structured interview guide, developed based on the key constructs of the Theoretical Framework of Acceptability. All interviews will be conducted via Zoom at a mutually agreed upon date and time and are estimated to last, on average, around 45 minutes.

To capture the NHS service user perspective, the investigators will also conduct three online focus groups with approximately 20 NHS service users.

Eligibility Criteria:
Inclusion Criteria:

* Unconsented patients ≧ 18 years old with frontal chest radiograph, acquired consecutively during usual care through the outpatient (including GP) referral pathway only, whose radiograph has not already been reported (applies to clinical effectiveness and health economic evaluation studies).
* Unconsented patients ≧ 18 years old with frontal chest radiograph, sampled from images already acquired and reported in the current or previous calendar year (applies to technical evaluation).
* Key stakeholders such as NHS service users, healthcare staff and NHS management (applies to qualitative evaluation).

Exclusion Criteria:

* Patient has requested that they are removed from the study, or has objected to the use of AI in their routine clinical care and this has been subsequently upheld by the health board (applies to clinical effectiveness study, health economic evaluation and technical evaluation).