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Automated Coronary Artery Calcium and Quantitative Emphysema in Lung Cancer Screening: Association With Mortality, Lung Cancer Incidence, and Airflow Obstruction

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 (FEV(1)) and the discr...

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Detalles Bibliográficos
Autores principales: Balbi, Maurizio, Sabia, Federica, Ledda, Roberta E., Milanese, Gianluca, Ruggirello, Margherita, Silva, Mario, Marchianò, Alfonso V., Sverzellati, Nicola, Pastorino, Ugo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287055/
https://www.ncbi.nlm.nih.gov/pubmed/36656144
http://dx.doi.org/10.1097/RTI.0000000000000698
Descripción
Sumario: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 (FEV(1)) 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 FEV(1) 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) (P<0.05). No significant association with LC incidence was found after adjustments. Both biomarkers negatively correlated with FEV(1) (P<0.01). %LAA identified airflow obstruction with a moderate discriminative ability (AUC, 0.738). CONCLUSIONS: Automated CAC and %LAA added prognostic information to age, sex, and pack-years for predicting mortality but not LC incidence in an LC screening setting. Both biomarkers negatively correlated with FEV(1), with %LAA enabling the identification of airflow obstruction with moderate discriminative ability.