Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value

• Main Authors: Jordan Chamberlin, Madison R. Kocher, Jeffrey Waltz, Madalyn Snoddy, Natalie F. C. Stringer, Joseph Stephenson, Pooyan Sahbaee, Puneet Sharma, Saikiran Rapaka, U. Joseph Schoepf, Andres F. Abadia, Jonathan Sperl, Phillip Hoelzer, Megan Mercer, Nayana Somayaji, Gilberto Aquino, Jeremy R. Burt
• Format: Article
• Language: English
• Published: BMC 2021-03-01
• Series: BMC Medicine
• Subjects: Convolutional neural networks; Deep learning; Artificial intelligence; Lung cancer screening; Coronary artery disease; Cardiothoracic imaging
• Online Access: https://doi.org/10.1186/s12916-021-01928-3

Abstract Background Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT. Methods A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen’s kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis. Results Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen’s kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942). Conclusion We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.