Journal article
Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images
Scientific reports, Vol.11(1), pp.4916-4916
03/01/2021
DOI: 10.1038/s41598-021-84547-5
PMCID: PMC7921389
PMID: 33649381
Abstract
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.
Details
- Title: Subtitle
- Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images
- Creators
- Frank Li - University of IowaJiwoong Choi - University of IowaChunrui Zou - University of IowaJohn D Newell - University of IowaAlejandro P Comellas - University of IowaChang Hyun Lee - University of IowaHongseok Ko - Chungnam National UniversityR. Graham Barr - Columbia UniversityEugene R Bleecker - University of ArizonaChristopher B Cooper - University of California, Los AngelesFereidoun Abtin - University of California, Los AngelesIgor Barjaktarevic - University of California, Los AngelesDavid Couper - University of North Carolina at Chapel HillMeiLan Han - University of MichiganNadia N Hansel - Johns Hopkins UniversityRichard E Kanner - University of UtahRobert Paine - University of UtahElla A Kazerooni - University of MichiganFernando J Martinez - Cornell UniversityWanda O’Neal - Chapel Hill, NC USAStephen I Rennard - University of Nebraska Medical CenterBenjamin M Smith - McGill UniversityPrescott G Woodruff - University of California, San FranciscoEric A Hoffman - University of IowaChing-Long Lin - Iowa City, IA USA
- Resource Type
- Journal article
- Publication Details
- Scientific reports, Vol.11(1), pp.4916-4916
- DOI
- 10.1038/s41598-021-84547-5
- PMID
- 33649381
- PMCID
- PMC7921389
- NLM abbreviation
- Sci Rep
- ISSN
- 2045-2322
- eISSN
- 2045-2322
- Publisher
- Nature Publishing Group UK
- Grant note
- 2018001360001 / ; R01-HL130506; R01-HL112986; U01-HL114494 / ; 2017R1D1A1A09082160 / ;
- Language
- English
- Date published
- 03/01/2021
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Pulmonary, Critical Care, and Occupational Medicine; ICTS; Mechanical Engineering; Internal Medicine
- Record Identifier
- 9984195168702771
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