Rationale and Objectives
To compare an artificial intelligence (AI)-based prototype and subjective grading
for predicting disease severity in patients with emphysema.
Methods
Our IRB approved HIPAA-compliant study included 113 adults (71±8 years; 47 females,
66 males) who had both non-contrast chest CT and pulmonary function tests performed
within a span of 2 months. The disease severity was classified based on the forced
expiratory volume in 1 second (FEV1 as % of predicted) into mild, moderate, and severe.
2 thoracic radiologists (RA), blinded to the clinical and AI results, graded severity
of emphysema on a 5-point scale suggested by the Fleischner Society for each lobe.
The whole lung scores were derived from the summation of lobar scores. Thin-section
CT images were processed with the AI-Rad Companion Chest prototype (Siemens Healthineers)
to quantify low attenuation areas (LAA < - 950 HU) in whole lung and each lobe separately.
Bronchial abnormality was assessed by both radiologists and a fully automated software
(Philips Healthcare).
Results
Both AI (AUC of 0.77; 95% CI: 0.68 – 0.85) and RA (AUC: 0.76, 95% CI: 0.65 – 0.84)
emphysema quantification could differentiate mild, moderate, and severe disease based
on FEV1. There was a strong positive correlation between AI and RA (r = 0.72 – 0.80;
p <0.001). The combination of emphysema and bronchial abnormality quantification from
radiologists’ and AI assessment could differentiate between different severities with
AUC of 0.80 – 0.82 and 0.87, respectively.
Conclusion
The assessed AI-prototypes can predict the disease severity in patients with emphysema
with the same predictive value as the radiologists.
Key Words
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REFERENCES
Centers for Disease Control and Prevention. Chronic Obstructive Pulmonary Disease (COPD) Data and Statistics. Available at: https://www.cdc.gov/copd/data.html (Accessed on January 12, 2021).
COPD: Facts, Statistics, and You. Available at: https://www.healthline.com/health/copd/facts-statistics-infographic (Accessed on January 12, 2021).
- Chronic obstructive pulmonary disease: lobe-based visual assessment of volumetric CT by using standard images—comparison with quantitative CT and pulmonary function test in the COPDGene study.Radiology. 2013; 266: 626-635
- Beyond FEV1 in COPD: a review of patient-reported outcomes and their measurement.Int J Chron Obstruct Pulmon Dis. 2012; 7: 697
- Present and future utility of computed tomography scanning in the assessment and management of COPD.Eur Respir J. 2016; 48: 216-228
- Relationship of CT-quantified emphysema, small airways disease and bronchial wall dimensions with physiological, inflammatory and infective measures in COPD.Respir Res. 2018; 19: 1-11
- Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease: the GOLD science committee report 2019.Eur Respir J. 2019; 53: 1900164
- GOLD 2021 guidelines for COPD—what's new and why.Adv Respir Med. 2021; 89: 344-346
- Chronic obstructive pulmonary disease: diagnostic considerations.Am Fam Physician. 2006; 73: 669-676
- Fleischner society visual emphysema ct patterns help predict progression of emphysema in current and former smokers: results from the COPD gene study.Radiology. 2021; 298: 441-449
- High-resolution CT quantification of bronchiectasis: clinical and functional correlation.Radiology. 2002; 225: 663-672
- Fleischner society visual emphysema CT patterns help predict progression of emphysema in current and former smokers: results from the COPD gene study.Radiology. 2021; 298: 441-449
- Unraveling the interplay of image formation, data representation and learning in CT-based COPD phenotyping automation: the need for a meta-strategy.Med Phys. 2021; 48: 5179-5191
- Artificial intelligence-based fully automated per lobe segmentation and emphysema-quantification based on chest computed tomography compared with global initiative for chronic obstructive lung disease severity of smokers.J Thorac Imaging. 2020; 35: S28-S34
- DCT-MIL: deep CNN transferred multiple instance learning for COPD identification using CT images.Phys Med Biol. 2020; 65145011
- Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT.Lancet Digit Health. 2020; 2: e259-e267
- Automated CT staging of chronic obstructive pulmonary disease severity for predicting disease progression and mortality with a deep learning convolutional neural network.Radiology: Cardiothoracic Imaging. 2021; 3e200477
- A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects.Sci Rep. 2021; 11: 1-2
- Genetic epidemiology of COPD (COPD Gene) investigators. deep learning enables automatic classification of emphysema pattern at CT.Radiology. 2020; 294: 434-444
- Size-specific optimization of CT protocols based on minimum detectability.Med physics. 2017; 44: 1301-1311
- Effect of obesity on ability to lower exposure for detection of low-attenuation liver lesions.J Appl Clin Med Phys. 2021; 22: 138-144
- Prediction of pulmonary function in patients with chronic obstructive pulmonary disease: correlation with quantitative CT parameters.Korean J Radiol. 2019; 20: 683-692
FDA Cleared AI Algorithms. Available at: https://models.acrdsi.org/. (Accessed on July 9, 2021).
Article info
Publication history
Published online: October 14, 2021
Accepted:
September 6,
2021
Received in revised form:
September 3,
2021
Received:
July 21,
2021
Footnotes
Funding: This work has not received any findings.
Identification
Copyright
© 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
