Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and the traditional variables extracted from computed tomography (CT) images may not be sufficient to describe all the topological features of lung tissues in COPD patients. We employed an unsupervised three-dimensional (3D) con...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Scientific reports Ročník 11; číslo 1; s. 4916 - 15
Hlavní autoři: Li, Frank, Choi, Jiwoong, Zou, Chunrui, Newell, John D., Comellas, Alejandro P., Lee, Chang Hyun, Ko, Hongseok, Barr, R. Graham, Bleecker, Eugene R., Cooper, Christopher B., Abtin, Fereidoun, Barjaktarevic, Igor, Couper, David, Han, MeiLan, Hansel, Nadia N., Kanner, Richard E., Paine, Robert, Kazerooni, Ella A., Martinez, Fernando J., O’Neal, Wanda, Rennard, Stephen I., Smith, Benjamin M., Woodruff, Prescott G., Hoffman, Eric A., Lin, Ching-Long
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 01.03.2021
Nature Publishing Group
Nature Portfolio
Témata:
639/166/985
692/699/1785/4037
Adult
Aged
Case-Control Studies
Chronic obstructive pulmonary disease
Cohort Studies
Computed tomography
Deep learning
Emphysema
Factor analysis
Female
Humanities and Social Sciences
Humans
Lung - diagnostic imaging
Lung - pathology
Lung diseases
Male
Middle Aged
multidisciplinary
Obstructive lung disease
Phenotypes
Pulmonary Disease, Chronic Obstructive - diagnostic imaging
Respiratory tract
Science
Science (multidisciplinary)
Smokers
Subpopulations
Tomography
Tomography, X-Ray Computed - methods
ISSN:2045-2322, 2045-2322
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and the traditional variables extracted from computed tomography (CT) images may not be sufficient to describe all the topological features of lung tissues in COPD patients. We employed an unsupervised three-dimensional (3D) convolutional autoencoder (CAE)-feature constructor (FC) deep learning network to learn from CT data and derive tissue pattern-clusters jointly. We then applied exploratory factor analysis (EFA) to discover the unobserved latent traits (factors) among pattern-clusters. CT images at total lung capacity (TLC) and residual volume (RV) of 541 former smokers and 59 healthy non-smokers from the cohort of the SubPopulations and Intermediate Outcome Measures in the COPD Study (SPIROMICS) were analyzed. TLC and RV images were registered to calculate the Jacobian (determinant) values for all the voxels in TLC images. 3D Regions of interest (ROIs) with two data channels of CT intensity and Jacobian value were randomly extracted from training images and were fed to the 3D CAE-FC model. 80 pattern-clusters and 7 factors were identified. Factor scores computed for individual subjects were able to predict spirometry-measured pulmonary functions. Two factors which correlated with various emphysema subtypes, parametric response mapping (PRM) metrics, airway variants, and airway tree to lung volume ratio were discriminants of patients across all severity stages. Our findings suggest the potential of developing factor-based surrogate markers for new COPD phenotypes.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-84547-5