Long-Term Prognostic Implications of Thoracic Aortic Calcification on CT Using Artificial Intelligence-Based Quantification in a Screening Population: A Two-Center Study.

Abstract

Background: The importance of including thoracic aortic calcification (TAC), in addition to coronary artery calcification (CAC), in prognostic assessments has been difficult to determine, partly due to greater challenge in performing standardized TAC assessments. Objective: To evaluate long-term prognostic implications of TAC assessed using artificial intelligence (AI)-based quantification on routine chest CT in a screening population. Methods: This retrospective study included 7404 asymptomatic individuals (median age, 53.9 years; 5875 male, 1529 female) who underwent nongated noncontrast chest CT as part of a national general health screening program at one of two centers from January 2007 to December 2014. A commercial AI program quantified TAC and CAC using Agatston scores, which were stratified into categories. Radiologists manually quantified TAC and CAC in 2567 examinations. The role of AI-based TAC categories in predicting major adverse cardiovascular events (MACE) and all-cause mortality (ACM), independent of AI-based CAC categories as well as clinical and laboratory variables, was assessed by multivariable Cox proportional hazards models using data from both centers and C-statistics from prognostic models developed and tested using center 1 and center 2 data, respectively. Results: AI-based and manual quantification showed excellent agreement for TAC and CAC (concordance correlation coefficient: 0.967 and 0.895, respectively). Median observation periods were 7.5 years for MACE (383 events in 5343 individuals) and 11.0 years for ACM (292 events in 7404 individuals). Adjusting for AI-based CAC categories along with clinical and laboratory variables, risk for MACE was not independently associated with any AI-based TAC category; risk of ACM was independently associated with AI-based TAC score of 1001-3000 (HR=2.14, p=.02), but not other AI-based TAC categories. In testing prognostic models, addition of AI-based TAC categories did not improve model fit relative to models containing clinical variables, laboratory variables, and AI-based CAC categories for MACE (C-index from 0.760 to 0.760, p=.81) or ACM (C-index from 0.823 to 0.830, p=.32). Conclusion: The addition of TAC to models containing CAC provided limited improvement in risk prediction in an asymptomatic screening population undergoing. Clinical Impact: AI-based quantification provides a standardized approach for better understanding the potential role of TAC as a predictive imaging biomarker.