Systematic Prioritisation of AI-Detected Chest X-ray Abnormalities for Optimised Lung Cancer Detection
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Objective
This study aimed to establish a reproducible method for selecting AI-detected chest X-ray (CXR) abnormalities prioritised for urgent reporting to support faster lung cancer diagnosis. By selecting findings informed by cancer prevalence and clinical significance, we sought to maximise detection while maintaining a high negative predictive value (NPV).
Materials and Methods
Two cohorts of CXRs were evaluated: (1) a retrospective cohort of patients with confirmed lung cancer and abnormal CXRs, and (2) a prospective cohort of primary care referred CXRs from seven Greater Manchester trusts, with the AI system in shadow mode. The AI triage system (Annalise Enterprise CXR) evaluated the relative prevalence of 124 abnormalities, and prioritisation strategies were assessed using sensitivity, specificity, positive predictive value (PPV), and NPV.
Results
A total of 1,282 lung cancer patients were included in cohort 1. In cohort 2, the AI system processed 13,802 CXRs. Sensitivity was 95.87% (94.77%-96.97%) in cohort 1, and specificity was 79.11% (78.43%-79.79%) in cohort 2, with an NPV of 99.95%.
Conclusion
This study presents a systematic, reproducible method for prioritising AI-detected CXR abnormalities, balancing high sensitivity and NPV while minimising low-risk prioritisation. This approach provides a data-driven alternative to traditional methods relying solely on clinical judgement.
