Computer aided detection (CAD) and scoring of lung nodules in a Scottish lung cancer screening programme

Aims and objectives: Lung cancer is the most common cancer worldwide with 1.6 million deaths worldwide in 20121. Several large American institutions have recommended lung cancer screening with low dose CT chest in specific population groups2,3,4. If lung cancer screening was implemented in Scotland there would be a very large increase in requirement for chest CT scans. These would all need reported, increasing radiological workload and cost. We postulated that combining the use of computer aided detection (CAD) and analysis could safely exclude lung cancer in a large number of scans obviating the need for radiological review with associated resource savings. The hypothesis was that CAD would have a 100% negative predictive value for lung cancer screening. Methods and materials: The initial screening Chest CT scans from Glasgow patients enrolled within the prospective ECLS study5 were sent for Lung-RADs analysis by Diagnostic Image Analysis Group Department of Radiology and Nuclear Medicine, Radboud University Medical Center. Computer aided detection was used to identify nodules and then categorise them into one of four groups (CAD 1 - 4) based upon probability of malignancy taking into account lesion morphology, size, location and texture. Independently and blind to the CAD results the scans were reported by two experienced consultant respiratory radiologists and assigned a similar grading category. The patient were then followed up with interval CT scans as per the ECLS study protocol. Results: CAD categorisation had a statistically significant correlation with the radiologist categorisation (p<0.001). None of the 113 (36%) patients within the CAD 1 group (no nodule identified) were found to have lung nodule(s). Conclusion: CAD and risk stratification is useful in excluding lung cancer on thoracic CT within the screening population. CAD followed by radiology review of suspicious cases is the cost effective and would lead to a 30% reduction in screening costs.