Early Detection of Emphysema Progression

Gorbunova, Vladlena; Jacobs, Sander S. A. M.; Lo, Pechin; Dirksen, Asger; Nielsen, Mads; Bab–Hadiashar, Alireza; Bruijne, Marleen de

doi:10.1007/978-3-642-15745-5_24

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…To date, groups who evaluate the prognostic value of registered data sets typically use statistically based metrics, such as the mean of the Jacobian, differences in HU or dissimilarity measures based on the histograms of the CT images where information from the measure is pooled throughout the lung into a single outcome measure which forfeits the spatial information that is inherent in the CT images 9,10,14,27 .…”

Section: Discussionmentioning

confidence: 99%

Computed tomography–based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression

Galbán

Han

Boes

et al. 2012

Nat Med

668

584

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Chronic obstructive pulmonary disease (COPD) is increasingly being recognized as a highly heterogeneous disorder, composed of varying pathobiology. Accurate detection of COPD subtypes by image biomarkers are urgently needed to enable individualized treatment thus improving patient outcome. We adapted the Parametric Response Map (PRM), a voxel-wise image analysis technique, for assessing COPD phenotype. We analyzed whole lung CT scans of 194 COPD individuals acquired at inspiration and expiration from the COPDGene Study. PRM identified the extent of functional small airways disease (fSAD) and emphysema as well as provided CT-based evidence that supports the concept that fSAD precedes emphysema with increasing COPD severity. PRM is a versatile imaging biomarker capable of diagnosing disease extent and phenotype, while providing detailed spatial information of disease distribution and location. PRMs ability to differentiate between specific COPD phenotypes will allow for more accurate diagnosis of individual patients complementing standard clinical techniques.

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Section: Discussionmentioning

confidence: 99%

Computed tomography–based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression

Galbán

Han

Boes

et al. 2012

Nat Med

668

584

View full text Add to dashboard Cite

show abstract

“…Image registration can be employed to match inspiratory scans obtained serially to assess emphysema progression (45,46). Gorbunova and colleagues (47) used image registration in a small study of 27 patients with severe emphysema to match serially acquired images at baseline, and at 3, 12, 21, and 24-30 months, and found that measurement of local disease progression could be consistently demonstrated. Image registration-based algorithms have been proposed for the measurement of local progression of emphysema that is minimally affected by changes in lung volume between baseline and follow-up (48,49).…”

Section: Emphysemamentioning

confidence: 99%

Recent Advances in Computed Tomography Imaging in Chronic Obstructive Pulmonary Disease

et al. 2018

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Lung imaging is increasingly being used to diagnose, quantify, and phenotype chronic obstructive pulmonary disease (COPD). Although spirometry is the gold standard for the diagnosis of COPD and for severity staging, the role of computed tomography (CT) imaging has expanded in both clinical practice and research. COPD is a heterogeneous disease with considerable variability in clinical features, radiographic disease, progression, and outcomes. Recent studies have examined the utility of CT imaging in enhancing diagnostic certainty, improving phenotyping, predicting disease progression and prognostication, selecting patients for intervention, and also in furthering our understanding of the complex pathophysiology of this disease. Multiple CT metrics show promise for use as imaging biomarkers in COPD.

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“…This compared corresponding regions of interest between baseline and follow-up CT scans, instead of direct voxel-to-voxel density comparison. 75 Histogram-based features were obtained from random samples of corresponding regions in the lung and were compared using L1-norm and Kullback–Leibler divergence dissimilarity indices. The use of image matching for disease progression is heavily reliant on the comparison of CT densities between corresponding locations of baseline and follow-up scans.…”

Section: Applications In Copdmentioning

confidence: 99%

Computed Tomography Image Matching in Chronic Obstructive Pulmonary Disease

Bodduluri

Bhatt

Reinhardt

2016

Crit Rev Biomed Eng

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Chronic obstructive pulmonary disease (COPD), characterized by progressive airflow obstruction due to the combined effects of emphysema and small airways disease, is associated with high morbidity and mortality. The complex link between emphysema and airways disease is associated with significant heterogeneity in clinical presentation. Spirometry is the current gold standard for diagnosis and stratification of the severity of airflow obstruction in COPD. Although spirometry is simple to use, it does not enable the separation of emphysema from airways disease. Computed tomography (CT), on the other hand, provides the anatomic localization of disease and has been increasingly used to phenotype COPD. The majority of current CT measures are extracted from a single-volume CT scan and although useful to characterize emphysema and airways disease, they do not link structural and functional abnormalities. Alternatively, CT image matching combines information from both inspiratory and expiratory CT scans, thus enabling determination of functional changes such as regional ventilation and mechanical properties of the lung. In this review, we discuss recent applications of CT image matching that provide clinically meaningful information beyond spirometry and single-volume CT scan measures.

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Early Detection of Emphysema Progression

Cited by 7 publications

References 9 publications

Computed tomography–based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression

Computed tomography–based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression

Recent Advances in Computed Tomography Imaging in Chronic Obstructive Pulmonary Disease

Computed Tomography Image Matching in Chronic Obstructive Pulmonary Disease

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