Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study

Abstract

Background Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective, requires training, and is affected by variability that may limit sensitivity to longitudinal change. Purpose To evaluate the clinical and imaging significance of increasing emphysema severity as graded by a deep learning algorithm on sequential CT scans in cigarette smokers. Materials and Methods A secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study participants was performed and included baseline and 5-year follow-up CT scans from 2007 to 2017. Emphysema was classified automatically according to the Fleischner emphysema grading system at baseline and 5-year follow-up using a deep learning model. Baseline and change in clinical and imaging parameters at 5-year follow-up were compared in participants whose emphysema progressed versus those who did not. Kaplan-Meier analysis and multivariable Cox regression were used to assess the relationship between emphysema score progression and mortality. Results A total of 5056 participants (mean age, 60 years ± 9 [SD]; 2566 men) were evaluated. At 5-year follow-up, 1293 of the 5056 participants (26%) had emphysema progression according to the Fleischner grading system. This group demonstrated progressive airflow obstruction (forced expiratory volume in 1 second [percent predicted]: -3.4 vs -1.8), a greater decline in 6-minute walk distance (-177 m vs -124 m), and greater progression in quantitative emphysema extent (adjusted lung density: -1.4 g/L vs 0.5 g/L; percentage of lung voxels with CT attenuation less than -950 HU: 0.6 vs 0.2) than those with nonprogressive emphysema (P < .001 for each). Multivariable Cox regression analysis showed a higher mortality rate in the group with emphysema progression, with an estimated hazard ratio of 1.5 (95% CI: 1.2, 1.8; P < .001). Conclusion An increase in Fleischner emphysema grade on sequential CT scans using an automated deep learning algorithm was associated with increased functional impairment and increased risk of mortality. ClinicalTrials.gov registration no. NCT00608764 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Grenier in this issue.

Graphical abstract

Figure 1:

Flowchart of study population. DL = deep learning.

Figure 2:

Inspiratory axial noncontrast CT scans obtained at baseline and 5-year-follow-up in two participants demonstrate emphysema progression according to the deep learning automated method. (A) Baseline scan shows mild emphysema in a 49-year-old man. (B) Image obtained at 5-year follow-up shows progression to moderate emphysema. Forced expiratory volume in 1 second (FEV₁) decreased by 677 mL. (C) Baseline scan shows moderate emphysema in a 62-year-old man. (D) Image obtained at 5-year follow-up shows progression to confluent emphysema. FEV₁ decreased by 502 mL.

Figure 3:

Inspiratory axial noncontrast CT scans obtained at (A) baseline and (B) 5-year follow-up in a 73-year-old woman. There was no change in emphysema grade according to the deep learning automated method. Both baseline and 5-year follow-up scans show mild emphysema. Forced expiratory volume in 1 second did not change at follow-up.

Figure 4:

Kaplan-Meier plot shows the relationship between deep learning emphysema grade progression and survival. Lower survival is associated with emphysema progression in 5026 participants included in mortality analysis.

Figure 5:

Kaplan-Meier plot shows survival according to emphysema grade assessed at 5-year follow-up chest CT. Adv. = advanced.