Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes.

Journal Article (Journal Article)

BACKGROUND: Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. However, a large amount of nonpulmonary data from these scans remains unassessed. We aimed to validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine NCCT and evaluate prediction of cardiovascular outcomes. METHODS: We retrospectively evaluated 273 patients (median age 69 years, 55.5% male) who underwent LDCT for lung cancer screening. LA volumes were quantified by three expert cardiothoracic radiologists and a prototype AI algorithm. LA volumes were then indexed to the body surface area (BSA). Expert and AI LA volume index (LAVi) were compared and used to predict cardiovascular outcomes within five years. Logistic regression with appropriate univariate statistics were used for modelling outcomes. RESULTS: There was excellent correlation between AI and expert results with an LAV intraclass correlation of 0.950 (0.936-0.960). Bland-Altman plot demonstrated the AI underestimated LAVi by a mean 5.86 ​mL/m2. AI-LAVi was associated with new-onset atrial fibrillation (AUC 0.86; OR 1.12, 95% CI 1.08-1.18, p ​< ​0.001), HF hospitalization (AUC 0.90; OR 1.07, 95% CI 1.04-1.13, p ​< ​0.001), and MACCE (AUC 0.68; OR 1.04, 95% CI 1.01-1.07, p ​= ​0.01). CONCLUSION: This novel deep learning algorithm for automated measurement of LA volume on lung cancer screening scans had excellent agreement with manual quantification. AI-LAVi is significantly associated with increased risk of new-onset atrial fibrillation, HF hospitalization, and major adverse cardiac and cerebrovascular events within 5 years.

Full Text

Duke Authors

Cited Authors

  • Aquino, GJ; Chamberlin, J; Mercer, M; Kocher, M; Kabakus, I; Akkaya, S; Fiegel, M; Brady, S; Leaphart, N; Dippre, A; Giovagnoli, V; Yacoub, B; Jacob, A; Gulsun, MA; Sahbaee, P; Sharma, P; Waltz, J; Schoepf, UJ; Baruah, D; Emrich, T; Zimmerman, S; Field, ME; Agha, AM; Burt, JR

Published Date

  • 2022

Published In

Volume / Issue

  • 16 / 3

Start / End Page

  • 245 - 253

PubMed ID

  • 34969636

Electronic International Standard Serial Number (EISSN)

  • 1876-861X

Digital Object Identifier (DOI)

  • 10.1016/j.jcct.2021.12.005


  • eng

Conference Location

  • United States