Abstract
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.
Keywords
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References
- 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.BMC Med. 2021; 19: 55
- Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT.Med Phys. 2020; 47: 5048-5060
- Leveraging the coronary calcium scan beyond the coronary calcium score.Eur Radiol. 2018; 28: 3082-3087
- Automatic segmentation and quantification of the cardiac structures from non-contrast-enhanced cardiac CT scans.Phys Med Biol. 2017; 62: 3798-3813
- Measurement of left atrial volume by 2D and 3D non-contrast computed tomography compared with cardiac magnetic resonance imaging.J Cardiovasc Comput Tomogr. 2018; 12: 316-319
- Left atrial volume quantification using coronary calcium score scan: feasibility, reliability and reproducibility analysis of a standardized approach.Int J Cardiol Heart Vasc. 2019; 23: 100351
- Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.PLoS One. 2020; 15e0232573
- Segmentation and visualization of left atrium through a unified deep learning framework.Int J Comput Assist Radiol Surg. 2020; 15: 589-600
- Evaluation of left atrial function by multidetector computed tomography before left atrial radiofrequency-catheter ablation: comparison of a manual and automated 3D volume segmentation method.Eur J Radiol. 2010; 75: e141-e146
- Identification of high-risk left ventricular hypertrophy on calcium scoring cardiac computed tomography scans: validation in the DHS.Circ Cardiovasc Imaging. 2020; 13e009678
- Left atrial size and function: role in prognosis.J Am Coll Cardiol. 2014; 63: 493-505
- Association of computed tomography-derived left atrial size with major cardiovascular events in the general population: the Heinz Nixdorf Recall Study.Int J Cardiol. 2014; 174: 318-323
- Noncoronary measures enhance the predictive value of cardiac CT above traditional risk factors and CAC score in the general population.JACC Cardiovasc Imaging. 2016; 9: 1177-1185
- Left atrial enlargement increases the risk of major adverse cardiac events independent of coronary vasodilator capacity.Eur J Nucl Med Mol Imag. 2015; 42: 1551-1561
- A Probabilistic U-Net for Segmentation of Ambiguous Images Proceedings of the 32nd International Conference on Neural Information Processing Systems.2018
- Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging.Eur Heart J Cardiovasc Imaging. 2015; 16: 233-270
- Multimodality cardiac imaging for the assessment of left atrial function and the association with atrial Arrhythmias.Circ Cardiovasc Imaging. 2016; 9
- Deep learning for quantification of epicardial and thoracic adipose tissue from non-contrast CT.IEEE Trans Med Imag. 2018; 37: 1835-1846
- Automated aortic calcium scoring on low-dose chest computed tomography.Med Phys. 2010; 37: 714-723
Article info
Publication history
Published online: December 16, 2021
Accepted:
December 13,
2021
Received in revised form:
November 16,
2021
Received:
June 15,
2021
Identification
Copyright
© 2021 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.
