Expanding the role of radiographers in reporting suspected lung cancer: a cost-effectiveness analysis using a decision tree model

Introduction To assess whether an enhanced role for radiographers in reporting lung cancer chest radiographs is cost-effective. Methods Costs and outcomes of chest radiograph reporting by reporting radiographer or by a radiologist were compared using a decision tree model. The model followed patients from an initial chest radiographs for suspected lung cancer to the provision of cancer care in positive cases. Sensitivity and specificity of reporting for radiographers and radiologists were derived from a recent trial. Treatment costs and quality adjusted life expectancy were estimated over five years for those diagnosed. Deterministic and probabilistic sensitivity analyses were used to test the robustness of inference to parameter uncertainty. Results For 1000 simulated patients, radiographer reporting decreased detection costs by £8500 and detected 10.3 more cases at initial presentation. After including treatment costs and outcomes, radiographer reporting remained cheaper than radiologist reporting and resulted in 1.4 additional QALYs per 1000 screened patients. Probabilistic analysis indicated a 98% likelihood that radiographer reporting is cheaper and more effective than radiologist reporting after inclusion of treatment costs and outcomes. Conclusion Radiographer reporting is a cost-effective alternative to radiologist reporting in lung cancer diagnosis. Further work is needed to support the adoption of radiographer's reporting pathway in diagnosis of lung cancer suspected patients

PROTEUS Study: A Prospective Randomized Controlled Trial Evaluating the Use of Artificial Intelligence in Stress Echocardiography.

BACKGROUND: Stress echocardiography (SE) is one of the most commonly used diagnostic imaging tests for coronary artery disease (CAD) but requires clinicians to visually assess scans to identify patients who may benefit from invasive investigation and treatment. EchoGo Pro provides an automated interpretation of SE based on artificial intelligence (AI) image analysis. In reader studies, use of EchoGo Pro when making clinical decisions improves diagnostic accuracy and confidence. Prospective evaluation in real world practice is now important to understand the impact of EchoGo Pro on the patient pathway and outcome. METHODS: PROTEUS is a randomized, multicenter, 2-armed, noninferiority study aiming to recruit 2,500 participants from National Health Service (NHS) hospitals in the UK referred to SE clinics for investigation of suspected CAD. All participants will undergo a stress echocardiogram protocol as per local hospital policy. Participants will be randomized 1:1 to a control group, representing current practice, or an intervention group, in which clinicians will receive an AI image analysis report (EchoGo Pro, Ultromics Ltd, Oxford, UK) to use during image interpretation, indicating the likelihood of severe CAD. The primary outcome will be appropriateness of clinician decision to refer for coronary angiography. Secondary outcomes will assess other health impacts including appropriate use of other clinical management approaches, impact on variability in decision making, patient and clinician qualitative experience and a health economic analysis. DISCUSSION: This will be the first study to assess the impact of introducing an AI medical diagnostic aid into the standard care pathway of patients with suspected CAD being investigated with SE. TRIAL REGISTRATION: Clinicaltrials.gov registration number NCT05028179, registered on 31 August 2021; ISRCTN: ISRCTN15113915; IRAS ref: 293515; REC ref: 21/NW/0199