PenRAD deploys AI-powered chest CT for early lung cancer detection and monitoring of nodules
Peninsula Imaging Network (PenRAD) has gone live with the UK’s first commercial deployment of Qure.ai’s AI for chest CT. qCT is expected to assist radiologists with the early detection and monitoring of lung nodules.
The AI is currently processing around 1,200 chest CTs from patients each week across all four NHS trusts in the network: Royal Cornwall Hospitals NHS Trust, Royal Devon University Healthcare NHS Foundation Trust, Torbay and South Devon NHS Foundation Trust and University Hospitals Plymouth NHS Trust.
The chest CT AI was implemented with funding from the government’s £21m Artificial Intelligence Diagnostic Fund. PenRAD chose the qCT solution for its bid to the fund, in collaboration with Health Innovation Southwest and the Peninsula Cancer Alliance.
Consultant radiologist at University Hospitals Plymouth NHS Trust Dr Tej Pandher said: “Lung nodule AI assistance helps to identify and track suspicious nodules over time. The reporting radiologist can then put any such nodules into a clinical context and provide useful information for further follow-up and/or treatment.”
qCT is part of an end-to-end AI-powered lung cancer care continuum suite of solutions for chest x-ray and CT, to detect, measure and track lung nodules.
Qure.ai UK business head Surabhi Srivastava added: “qCT has been designed to provide clinical decision support in the detection, characterisation, quantification and monitoring of lung nodules. It can also pull patient images from prior examinations to provide comprehensive nodule management and support progression monitoring over time. This makes it easier for the clinician to assess the growth of nodules with standardised data for volumetric assessment and an auto-assigned malignancy risk score of pulmonary nodules using the Brock model/British Thoracic Society guidelines.”
Picture: qCT shows nodule development over two timestamps, pulling a prior image and comparing the nodule found across different time periods.
Read this report on page 25 of the the June 2025 issue of RAD Magazine.
