Radiograph Accelerated Detection and Identification of Cancer in the Lung

NCT06044454 | Active Not Recruiting DMC

• 2 other identifiers: INGN23RM02823/NW/0211
• Study Type: observational
• Target: 60,000
• Locations: 1 country
• Sites: 4

Brief Summary

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

Conditions

Lung Cancer

Outcome Measures

Primary Outcomes (1)

• Time to 'decision to recommend CT', or to a decision not to undertake CT for CXR acquired with USC (CXR acquired to CXR reported)

  Time to 'decision to recommend CT', or to a decision not to undertake CT for CXR acquired with USC (CXR acquired to CXR reported)

  through study completion, an average of 1 year

Secondary Outcomes (9)

• Time from acquisition to reporting of all CXRs

  through study completion, an average of 1 year

• Time to diagnosis of lung cancer

  through study completion, an average of 1 year

• Time to treatment initiation lung cancer

  through study completion, an average of 1 year

• Number of hospital visits during screening pathway

  through study completion, an average of 1 year

• Hospitalisation within 6 and 12 months CXR acquisition

  through study completion, an average of 1 year

Study Arms (1)

Service deployment

Intervention Chest X-ray received - care team (standard of care) CT scan - care team (standard of care)

Other: qXR

Interventions

qXR OTHER

a software product that uses artificial intelligence to triage, prioritise, and (for tuberculosis only) diagnose based upon identified abnormalities within the CXR.

Service deployment

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Service deployment within a single health board (NHS Greater Glasgow and Clyde), within the following sectors and hospitals: Clyde Sector * The Royal Alexandra Hospital * Inverclyde Royal Hospital * Vale of Leven Hospital South Sector * Queen Elizabeth University Hospital * New Victoria Hospital * Gartnavel General Hospital * West Glasgow Ambulatory Care Hospital North Sector * Glasgow Royal Infirmary * Stobhill Hospital At each participating site, the investigators will identify all patients referred through the outpatient pathway (including GP-referrals). This will include those with suspected lung cancer referred for CXR.

You may qualify if:

• 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).

You may not qualify if:

• 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).

Sponsors & Collaborators

• NHS Greater Glasgow and Clyde: lead
• Qure.ai Technologies Pvt. Ltd: collaborator

Study Sites (4)

Glasgow Royal Infirmary (North Sector)

Glasgow, United Kingdom

Location

NHS Greater Glasgow and Clyde

Glasgow, United Kingdom

Location

Queen Elizabeth University Hosp (South Sector)

Glasgow, United Kingdom

Location

The Royal Alexandra Hospital (Clyde Sector)

Paisley, United Kingdom

Location

Related Publications (1)

• Duncan SF, McConnachie A, Blackwood J, Stobo DB, Maclay JD, Wu O, Germeni E, Robert D, Bilgili B, Kumar S, Hall M, Lowe DJ. Radiograph accelerated detection and identification of cancer in the lung (RADICAL): a mixed methods study to assess the clinical effectiveness and acceptability of Qure.ai artificial intelligence software to prioritise chest X-ray (CXR) interpretation. BMJ Open. 2024 Sep 20;14(9):e081062. doi: 10.1136/bmjopen-2023-081062.

  PMID: 39306349 DERIVED

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Officials

• David Lowe

  NHS Greater Glasgow and Clyde Board HQ

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: ECOLOGIC OR COMMUNITY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 29, 2023
• First Posted: September 21, 2023
• Study Start: December 4, 2023
• Primary Completion: November 30, 2025
• Study Completion: November 30, 2025
• Last Updated: September 19, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will not share

Locations

GB (4)