Peer-Reviewed Publication
BJR Artif Intell2026;3(1):ubag007.January 1, 2026Journal Article
Systematic prioritisation of AI-detected chest X-ray abnormalities for optimised lung cancer detection.
Rhidian R Bramley1, Anna C Sharman2, Rebecca Duerden3, Sarah Lyon1, Melissa Ryan4, Elodie Weber5, Louise Brown1,2, Matthew Evison6,7
1Greater Manchester Cancer Alliance, c/o The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom.
2Department of Radiology, Manchester University NHS Foundation Trust, Oxford Road, Manchester, M13 9WL, United Kingdom.
3Department of Radiology, Stockport NHS Foundation Trust, Stepping Hill Hospital, Poplar Grove, Stockport, SK2 7JE, United Kingdom.
4Annalise.ai , 301/100 Harris Street, Pyrmont, NSW, 2009, Australia.
5Sectra Imaging IT Solutions, Teknikringen 20, SE-583 30, Linköping, Sweden.
6Lung Cancer & Thoracic Surgery Directorate, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, M13 9WL, United Kingdom.
7Manchester Academic Health Science Centre (MAHSC), Faculty of Biology, Medicine & Health, The University of Manchester, Manchester, ManchesterUnited Kingdom.
Abstract
This paper presents a reproducible, data-driven approach for prioritisation of AI-detected chest X-ray (CXR) findings to support faster lung cancer diagnosis in the NHS. The Annalise Enterprise CXR system was deployed in shadow mode across seven acute trusts in Greater Manchester. Two cohorts were used: a retrospective cancer cohort (n = 1,282) with confirmed lung cancer and visible CXR abnormalit…
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