KEY TAKEAWAYS
- The study aimed to evaluate the effectiveness of Qure.ai’s ‘qXR’ software in prioritizing chest X-rays suspected of lung cancer for quicker reporting.
- The study could revolutionize lung cancer diagnosis, accelerating X-ray identification for earlier treatment and better survival.
Lung cancer diagnosis relies heavily on chest X-rays (CXRs). However, with a rising demand for CXRs and a shortage of radiologists, timely interpretation poses a significant challenge. This delay can impact survival rates, making early detection and treatment crucial. Artificial intelligence (AI) offers a potential solution by assisting radiologists in prioritizing urgent cases.
Sean F Duncan and the team aimed to investigate the use of Qure.ai’s ‘qXR’ software in expediting the reporting of suspicious CXRs within NHS Greater Glasgow & Clyde.
The primary objective is to assess the clinical effectiveness of qXR in prioritizing patients with suspected lung cancer on CXR for follow-up CT. Secondary objectives include evaluating the utility, safety, technical performance, health economics, and acceptability of the intervention.
The RADICAL study employs a mixed-methods approach, incorporating a stepped-wedge cluster-randomized design. The study will encompass 24 months, including 12 months for data collection and 12 months for follow-up. All standard care CXRs from outpatient and primary care requests will be securely relayed to qXR for analysis.
The software will flag images with potential cancer indicators as ‘Urgent Suspicion of Cancer,’ prompting prioritized review by a radiologist within the established reporting workflow.
The study’s findings are expected to be disseminated through presentations at relevant conferences focusing on artificial intelligence, radiology, and respiratory health. Additionally, the results will be published in peer-reviewed journals and an interim report will be prepared for the Scottish Government.
Positive results could have significant implications for healthcare systems globally, potentially leading to earlier interventions and better outcomes for patients with lung cancer.
This study is co-funded by The Scottish Government Detect Cancer Earlier (DCE) Programme and Qure.ai.
Source: https://pubmed.ncbi.nlm.nih.gov/39306349/
Duncan SF, McConnachie A, Blackwood J, et al. (2024). “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;14(9):e081062. Published 2024 Sep 20. doi:10.1136/bmjopen-2023-081062
