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Guest Editorial
Registration-Based Metrics of Lung
Function to Describe COPD:
The Ultimate Question of Life, the Universe, and Everything
Robert H. Brown, MD, MPH
We have a very complex organ (the lung), and on it is super-
imposed a very complicated disease, chronic obstructive
pulmonary disease (COPD). How canwe devise a simple value
that gives us greater insight into this heterogeneous disease in
this complex organ? How can we better assess COPD in
patients? More important, how can we better determine the
extent that ever-worsening COPD has on the lives of our
patients? The pulmonary component of COPD is character-
ized by airflow limitation that is not fully reversible. The airflow
limitation is usually progressive and associated with an abnor-
mal inflammatory response of the lungs to noxious particles
or gases
(1)
, the most common of which is cigarette smoke.
COPD is classically assessed by pulmonary function testing.
Spirometry is a pulmonary function test (PFT) to determine
the diagnosis and evaluate the severity of the disease. The ratio
of the post-bronchodilator forced expiratory volume in 1 sec-
ond divided by the forced vital capacity (FEV
1
/FVC) <0.70 is
the classic cut-off for a diagnosis of COPD
(1)
. As the
FEV
1
/FVC ratio decreases, the severityof the disease increases.
The generally accepted categorization of COPD severity clas-
sifies patients into four stages according to the Global Initiative
for Chronic Obstructive Lung Disease (GOLD) classification
(1)
. Can we devise a better way to assess the disease based on
the directly viewed changes in the lungs in vivo?
With the advent of computed tomographic (CT) scanning
more than four decades earlier, the ability to examine the
lung parenchyma has improved with each new generation of
faster and greater resolution CT machines. Now, one can
directly assess the parenchyma in both health and disease states
(2–25)
. Thus, we have a window into the lungs to assess the
destruction wrought by (predominantly) cigarette smoke on
the lungs that leads to COPD. Emphysema, the extent and
progression of which historically we could measure only
with the use of limited pathological specimens at autopsy,
can now be easily measured in vivo. We can assess the
amount of parenchymal destruction and compare it to the
decrease in lung function in COPD. Other methods have
been used to improve our ability to correlate the anatomic
changes seen in the lungs using high-resolution CTwith the
physiological changes of disease. Several investigators have
proposed various methods to improve our analysis of the CT
data to better correlate with the spirometric data
(26–28)
.
In the current issue of
Academic Radiology
, Bodduluri et al
(29)
use image registration to provide additional information
about lung function changes in COPD subjects. Their study
uses a combination of density and texture feature sets and
compares that to a biomechanical feature set from the regi-
stration of inspiratory and expiratory scans to evaluate the
severity of COPD, with the presumption that this method
will provide greater accuracy. They tested this through the
use of a machine-learning framework to evaluate the perform-
ance of the obtained feature sets. They found that many of
their CT-derived features were highly correlated with spiro-
metry measurements as well as a health-related quality of life
questionnaire. However, it was not clear whether these CT
features would translate into a better understanding of the
extent, progression, or response to therapy of the disease com-
pared to the information described by the spirometry and/or
the health questionnaire. Are we simply trading one value
(spirometry) for another (CT-based image features)? How
can we attain a simpler valuation that gives us greater insight
into the disease? With a very complex organ and a very
complicated disease, is it possible to derive a simple answer?
One important step in developing a better measure of lung
disease is the use of image registration of inspiratory and
expiratory scans, as presented by Bodduluri et al
(29)
. This
should lead to better quantification of the changes in the
lung with disease progression and should be lauded. The
lung is a dynamic organ in constant motion. Using image
registration in their analyses thus considers the dynamic nature
of the lung. These authors examined whether their feature sets
could determine which CT scans were from individuals with
COPD, compared to the data from spirometry and the health
questionnaire. The biomechanical feature set showed higher
correlations with FEV
1
percent predicted but not with
FEV
1
/FVC, the latter being the classic spirometric measure
of COPD. Combining all three feature sets produced the
best correlations with the spirometric measures and the
Acad Radiol 2013; 20:525–526
From the Departments of Anesthesiology, Radiology, Medicine, and
Environmental Health Sciences, Johns Hopkins University, Johns Hopkins
Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205.
Received February 8, 2013; accepted February 8, 2013. Address corre-
spondence to: R.H.B. e-mail:
ª
AUR, 2013
525
BROWN
Academic Radiology, Vol 20, No 5, May 2013
health-related quality of life questionnaire. However, surpris-
ingly, the combination of all three did not have the highest
area under the curve on the receiver operating characteristic
analysis.
Another aspect of these investigators’ study was the use of
a machine-learning framework to evaluate the performance
of the obtained biomechanical feature set compared to
density- and texture-based feature sets, with the goal of using
their feature sets to determine the presence and severity of
disease (COPD vs non-COPD) (GOLD stages 0–4). They
showed that the biomechanical features were more effective
in recognizing COPD severity than the density and texture
feature sets.
This work has all the makings of a method that would com-
bine all the complicated variables into a simple measure, but
we are not quite there yet. We never quite get a sense of which
feature set is most important, or what is the best combination
of feature sets, in discriminating disease from nondisease.
As many of us have heard our children utter the phrase, ‘‘Are
we there yet?’’ we all want to know whether we have reached
the end of our journeywith the discoveryof a better method of
imaging lung disease in general and COPD specifically.
Unfortunately, we are not quite there yet. We are making
advancements, and Bodduluri et al
(29)
have moved us in
the right direction.
We continue to look for a simple outcome variable that will
tell us the extent and burden of disease in our patients in
general and, more specifically, in those patients with COPD.
Sometimes, it is simply a matter of asking the right question.
From Douglas Adams’ book,
Hitchhiker’s Guide to the Galaxy
(
30
), the supercomputer ‘‘Deep Thought’’ was built to answer
the question of life, the universe, and everything. The answer
turned out to be 42. The UltimateQuestion to that answer was
unknown, so an even bigger supercomputer was needed to
determine the right question. Maybe we simply need to ask
the right question. For the ultimate quantification of a lung
disease such as COPD, each study gets us closer to the right
question. Then, and perhaps only then, will we be able to
define and use simple metrics to truly optimize patient
diagnosis and treatment.
6. Diaz AA, Valim C, Yamashiro T, et al. Airway count and emphysema
assessed by chest CT imaging predicts clinical outcome in smokers.
Chest 2012; 138:880–887.
7. Estepar RS, Washko GG, Silverman EK, et al. Accurate airway wall
estimation using phase congruency. Med Image Comput Comput Assist
Interv 2006; 9:125–134.
8. Foreman MG, Zhang L, Murphy J, et al. Early-onset chronic obstructive
pulmonary disease is associated with female sex, maternal factors, and
African American race in the COPDGene Study. Am J Respir Crit Care
Med 2011; 184:414–420.
9. Gevenois PA, de Maertelaer V, De Vuyst P, et al. Comparison of computed
density and macroscopic morphometry in pulmonary emphysema. Am J
Respir Crit Care Med 1995; 152:653–657.
10. Gould GA, MacNee W, McLean A, et al. CT measurements of lung density
in life can quantitate distal airspace enlargement–an essential defining
feature of human emphysema. Am Rev Respir Dis 1988; 137:380–392.
11. Gould GA, Redpath AT, Ryan M, et al. Lung CT density correlates with
measurements of airflow limitation and the diffusing capacity. Eur Respir
J 1991; 4:141–146.
12. Han MK, Agusti A, Calverley PM, et al. Chronic obstructive pulmonary
disease phenotypes: the future of COPD. Am J Respir Crit Care Med
2010; 182:598–604.
13. Han MK, Kazerooni EA, Lynch DA, et al. Chronic obstructive pulmonary
disease exacerbations in the COPDGene Study: associated radiologic
phenotypes. Radiology 2011; 261:274–282.
14. Hersh CP, Washko GR, Jacobson FL, et al. Interobserver variability in the
determination of upper lobe-predominant emphysema. Chest 2007; 131:
424–431.
15. KimWJ, Silverman EK, Hoffman E, et al. CT metrics of airway disease and
emphysema in severe COPD. Chest 2009; 136:396–404.
16. McDonough JE, Yuan R, Suzuki M, et al. Small-airway obstruction and
emphysema in chronic obstructive pulmonary disease. N Engl J Med
2011; 365:1567–1575.
17. Ross JC, Estepar RS, Diaz A, et al. Lung extraction, lobe segmentation and
hierarchical region assessment for quantitative analysis on high resolution
computed tomography images. Med Image Comput Comput Assist Interv
2009; 12:690–698.
18. Ross JC, San Jose Estepar R, Kindlmann G, et al. Automatic lung lobe
segmentation using particles, thin plate splines, and maximum a posteriori
estimation. Med Image Comput Comput Assist Interv 2010; 13:163–171.
19. San Jose Estepar R, Reilly JJ, Silverman EK, et al. Three-dimensional
airway measurements and algorithms. Proc Am Thorac Soc 2008; 5:
905–909.
20. Washko GR, Dransfield MT, Estepar RS, et al. Airway wall attenuation:
a biomarker of airway disease in subjects with COPD. J Appl Physiol
2009; 107:185–191.
21. Washko GR, Hunninghake GM, Fernandez IE, et al. Lung volumes and
emphysema in smokers with interstitial lung abnormalities. N Engl J Med
2011; 364:897–906.
22. Washko GR, Lynch DA, Matsuoka S, et al. Identification of early interstitial
lung disease in smokers from the COPDGene Study. Acad Radiol 2010;
17:48–53.
23. Yamashiro T, Matsuoka S, Bartholmai BJ, et al. Collapsibility of lung
volume by paired inspiratory and expiratory CT scans: correlations with
lung function and mean lung density. Acad Radiol 2010; 17:489–495.
24. Yamashiro T, Matsuoka S, Estepar RS, et al. Kurtosis and skewness of
density histograms on inspiratory and expiratory CT scans in smokers.
COPD 2011; 8:13–20.
25. Yamashiro T, Matsuoka S, Estepar RS, et al. Quantitative airway assess-
ment on computed tomography in patients with alpha1-antitrypsin
deficiency. COPD 2009; 6:468–477.
26. Sorensen L, Nielsen M, Lo P, et al. Texture-based analysis of COPD:
a data-driven approach. IEEE Trans Med Imaging 2012; 31:70–78.
27. Uppaluri R, Hoffman EA, Sonka M, et al. Computer recognition of regional
lung disease patterns. Am J Respir Crit Care Med 1999; 160:648–654.
28. Uppaluri R, Hoffman EA, Sonka M, et al. Interstitial lung disease:
a quantitative study using the adaptive multiple feature method. Am J
Respir Crit Care Med 1999; 159:519–525.
29. Bodduluri S, Newell J, Hoffman A, et al. Registration based lung
mechanical analysis of chronic obstructive pulmonary disease (COPD)
using a supervised machine learning framework. Acad Radiol 2013;
20:527–536.
30. Adams D. The Hitchhiker’s Guide to the Galaxy. New York City, NY:
Random House Publishing Group, 1995.
REFERENCES
1. Rabe KF, Hurd S, Anzueto A, et al. Global strategy for the diagnosis,
management, and prevention of chronic obstructive pulmonary disease:
GOLD executive summary. Am J Respir Crit Care Med 2007; 176:
532–555.
2. Barr RG, Berkowitz EA, Bigazzi F, et al. A combined pulmonary-radiology
workshop for visual evaluation of COPD: study design, chest CT findings
and concordance with quantitative evaluation. COPD 2012; 9:151–159.
3. Diaz AA, Bartholmai B, San Jose Estepar R, et al. Relationship of
emphysema and airway disease assessed by CT to exercise capacity in
COPD. Respir Med 2010; 104:1145–1151.
4. Diaz AA, Come CE, Ross JC, et al. Association between airway caliber
changes with lung inflation and emphysema assessed by volumetric CT
scan in subjects with COPD. Chest 2012; 141:736–744.
5. Diaz AA, Han MK, Come CE, et al. The effect of emphysema on computed
tomographic measures of airway dimensions in smokers. Chest. 2012 Oct
526
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