Automated Coronary Artery Calcium and Quantitative Emphysema in Lung Cancer Screening

Abstract

Purpose: To assess automated coronary artery calcium (CAC) and quantitative emphysema (percentage of low attenuation areas [%LAA]) for predicting mortality and lung cancer (LC) incidence in LC screening. To explore correlations between %LAA, CAC, and forced expiratory value in 1 second (FEV₁) and the discriminative ability of %LAA for airflow obstruction. Materials and Methods: Baseline low-dose computed tomography scans of the BioMILD trial were analyzed using an artificial intelligence software. Univariate and multivariate analyses were performed to estimate the predictive value of %LAA and CAC. Harrell C-statistic and time-dependent area under the curve (AUC) were reported for 3 nested models (Model_survey: age, sex, pack-years; Model_survey-LDCT: Model_survey plus %LAA plus CAC; Model_final: Model_survey-LDCT plus selected confounders). The correlations between %LAA, CAC, and FEV₁ and the discriminative ability of %LAA for airflow obstruction were tested using the Pearson correlation coefficient and AUC-receiver operating characteristic curve, respectively. Results: A total of 4098 volunteers were enrolled. %LAA and CAC independently predicted 6-year all-cause (Model_final hazard ratio [HR], 1.14 per %LAA interquartile range [IQR] increase [95% CI, 1.05-1.23], 2.13 for CAC ≥400 [95% CI, 1.36-3.28]), noncancer (Model_final HR, 1.25 per %LAA IQR increase [95% CI, 1.11-1.37], 3.22 for CAC ≥400 [95%CI, 1.62-6.39]), and cardiovascular (Model_final HR, 1.25 per %LAA IQR increase [95% CI, 1.00-1.46], 4.66 for CAC ≥400, [95% CI, 1.80-12.58]) mortality, with an increase in concordance probability in Model_survey-LDCT compared with Model_survey (P1 (P1, with %LAA enabling the identification of airflow obstruction with moderate discriminative ability.