Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images
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...
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Nature Portfolio
2021
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oai:doaj.org-article:c5e067c40faa40fa966e6aa8bc55c3f22021-12-02T15:53:02ZLatent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images10.1038/s41598-021-84547-52045-2322https://doaj.org/article/c5e067c40faa40fa966e6aa8bc55c3f22021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84547-5https://doaj.org/toc/2045-2322Abstract 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.Frank LiJiwoong ChoiChunrui ZouJohn D. NewellAlejandro P. ComellasChang Hyun LeeHongseok KoR. Graham BarrEugene R. BleeckerChristopher B. CooperFereidoun AbtinIgor BarjaktarevicDavid CouperMeiLan HanNadia N. HanselRichard E. KannerRobert PaineElla A. KazerooniFernando J. MartinezWanda O’NealStephen I. RennardBenjamin M. SmithPrescott G. WoodruffEric A. HoffmanChing-Long LinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021) |
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Medicine R Science Q |
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Medicine R Science Q Frank Li Jiwoong Choi Chunrui Zou John D. Newell Alejandro P. Comellas Chang Hyun Lee Hongseok Ko R. Graham Barr Eugene R. Bleecker Christopher B. Cooper Fereidoun Abtin Igor Barjaktarevic David Couper MeiLan Han Nadia N. Hansel Richard E. Kanner Robert Paine Ella A. Kazerooni Fernando J. Martinez Wanda O’Neal Stephen I. Rennard Benjamin M. Smith Prescott G. Woodruff Eric A. Hoffman Ching-Long Lin Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images |
| description |
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. |
| format |
article |
| author |
Frank Li Jiwoong Choi Chunrui Zou John D. Newell Alejandro P. Comellas Chang Hyun Lee Hongseok Ko R. Graham Barr Eugene R. Bleecker Christopher B. Cooper Fereidoun Abtin Igor Barjaktarevic David Couper MeiLan Han Nadia N. Hansel Richard E. Kanner Robert Paine Ella A. Kazerooni Fernando J. Martinez Wanda O’Neal Stephen I. Rennard Benjamin M. Smith Prescott G. Woodruff Eric A. Hoffman Ching-Long Lin |
| author_facet |
Frank Li Jiwoong Choi Chunrui Zou John D. Newell Alejandro P. Comellas Chang Hyun Lee Hongseok Ko R. Graham Barr Eugene R. Bleecker Christopher B. Cooper Fereidoun Abtin Igor Barjaktarevic David Couper MeiLan Han Nadia N. Hansel Richard E. Kanner Robert Paine Ella A. Kazerooni Fernando J. Martinez Wanda O’Neal Stephen I. Rennard Benjamin M. Smith Prescott G. Woodruff Eric A. Hoffman Ching-Long Lin |
| author_sort |
Frank Li |
| title |
Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images |
| title_short |
Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images |
| title_full |
Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images |
| title_fullStr |
Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images |
| title_full_unstemmed |
Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images |
| title_sort |
latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/c5e067c40faa40fa966e6aa8bc55c3f2 |
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