Prospective Study of Low- and Standard-dose Chest CT for Pulmonary Nodule Detection: A Comparison of Image Quality, Size Measurements and Radiation Exposure

Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software

AIM

To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD).

MATERIALS AND METHODS

A chest phantom with artificial pulmonary nodules was first scanned using the routine protocol and the ULD protocol (3.28 versus 0.18 mSv) to compare the image quality and to test the acceptability of the ULD CT protocol. Next, 147 lung-screening patients were enrolled prospectively, undergoing an additional ULD CT immediately after their routine CT examination for clinical validation. Images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), the AIIR, and were imported to the CAD software for preliminary nodule detection. Subjective image quality on the phantom was scored using a five-point scale and compared using the Mann-Whitney U-test. Nodule detection using CAD was evaluated for ULD HIR and AIIR images using the routine dose image as reference.

RESULTS

Higher image quality was scored for AIIR than for FBP and HIR at ULD (p<0.001). As reported by CAD, 107 patients were presented with more than five nodules on routine dose images and were chosen to represent the challenging cases at an early stage of pulmonary disease. Among such, the performance of nodule detection by CAD on ULD HIR and AIIR images was 75.2% and 92.2% of the routine dose image, respectively.

CONCLUSION

Combined with AIIR, it was feasible to use an ULD CT protocol with 95% dose reduction for CAD-based screening of pulmonary nodules.

Impact of CT screening in lung cancer: Scientific evidence and literature review

The treatment of lung cancer has improved significantly in recent years however, lung cancer remains as a leading cause of cancer-related mortality worldwide. Lung cancer screening has been explored, over the past several decades, as a means of reducing lung cancer mortality, to identify asymptomatic disease when it is potentially curable. The National Lung Screening Trial (NLST) established that low-dose computed tomography (LDCT) scans of the chest can be instrumental in reducing lung cancer mortality but the criteria for screening implemented in this trial may not be equitably sensitive across racial and sex subpopulations. Furthermore, the high false detection rate reported in this trial has raised concerns regarding overdiagnosis with LDCT alone. The aim of this review is to summarize the history of lung cancer screening trials, limitations of lung cancer screening, the impact of alternative risk prediction models in reducing disparities, and the use of biomarkers in conjunction with imaging to improve diagnostic authenticity.