SWIFT LUNG - Streamlined Workflow for Investigation and Fast Tracking Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related mortality in the UK. The National Optimal Lung Cancer Pathway (NOLCP) recommends rapid triage of pulmonary nodules detected on CT imaging (within 3 days). NHS trusts consistently struggle to meet this target, which has a knock-on effect on downstream waiting times for other components of the NOLCP. A contributing factor is that referrals for triage must be manually actioned by the clinician who requested the index imaging, potentially introducing delays. Effective triage is also important. While many nodules are benign and can be monitored through surveillance imaging, incorrectly assigning a malignant nodule to interval CT imaging as part of a lung-nodule follow-up pathway delays diagnosis and risks cancer progression with potentially fatal consequences. Similarly, unnecessary investigations for benign nodules contribute to waiting lists for specialist respiratory clinics, biopsies and PET scanning. To add to this, not all lung nodules are monitored in line with British Thoracic Society guidance: failure to arrange appropriate follow-up imaging for nodules can lead to avoidable patient deaths from lung cancer as demonstrated by Oxford. Optellum Virtual Nodule Clinic (VNC), a UKCA/CE-marked (Class IIb) and FDA-approved medical device, is a potential solution. It has two components: 1) Optellum Patient Safety Net (PSN), which accurately detects pulmonary nodules from CT reports and automatically presents these on a clinical dashboard, and 2) Optellum Lung Cancer Prediction (LCP), which generates a malignancy risk prediction score, previously shown to out-perform standard-of-care clinical risk-scoring tools (Mayo and Brock models). We propose SWIFT-Lung: a prospective, stepped-wedge clinical trial across 3 NHS sites across England and Scotland (NHS Highlands, NHS Greater Glasgow and Clyde and Oxford University Hospitals). It will incorporate a retrospective technical evaluation of Optellum PSN, a multisite, prospective implementation of the Optellum VNC within existing lung cancer and lung nodule care pathways, an embedded health economic analysis and a qualitative evaluation of acceptability to patients and clinicians. The primary objective is to determine the clinical effectiveness of the Optellum VNC in reducing time-to-triage from date of index CT for patients with reported pulmonary nodules. Secondary objectives will include determining the utility, safety, technical performance, cost-effectiveness and acceptability of the AI-enabled lung nodule pathway. The total duration of the project is 36 months, including pre-implementation, implementation and post-implementation phases. Appropriate Research Ethics Committee (REC) and local Trusted Research Environment approvals will be sought. The study will be informed by public priorities through the creation of a Public Advisory Group. The results will be disseminated to researchers and clinicians in academic journals and through conference submissions, to the public in hospital press and social media releases, and to NHS decision-makers through an NHS business case. Through this study, we aim to demonstrate the potential of Optellum VNC to improve patient outcomes, reduce clinician workload and augment clinical decision-making while promoting guideline-directed lung cancer diagnosis and lung nodule follow-up. Ultimately, this study aims to demonstrate a reduction in waiting times for patients in the lung-cancer diagnostic pathway.