Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study

Jie Yang; Elsa D. Angelini; Pallavi P. Balte; Eric A. Hoffman; John H. M. Austin; Benjamin M. Smith; R. Graham Barr; Andrew F. Laine

doi:10.1109/TMI.2021.3094660

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Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study

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Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study

Jie Yang, Elsa D. Angelini, Pallavi P. Balte, Eric A. Hoffman, John H. M. Austin, Benjamin M. Smith, R. Graham Barr and Andrew F. Laine

IEEE transactions on medical imaging, Vol.40(12), pp.3652-3662

12/01/2021

DOI: 10.1109/TMI.2021.3094660

PMCID: PMC8715521

PMID: 34224349

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Abstract

Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography (CT) opens the way to new definitions of emphysema subtypes and eliminates the need of thorough manual labeling. However, CT-based emphysema subtypes have been limited to texture-based patterns without considering spatial location. In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtype candidates. Exploiting two cohorts of full-lung CT scans from the MESA COPD (n = 317) and EMCAP (n = 22) studies, we first show that our spatial mapping enables population-wide study of emphysema spatial location. We then evaluate the characteristics of the sLTPs discovered on MESA COPD, and show that they are reproducible, able to encode standard emphysema subtypes, and associated with physiological symptoms.

Computer Science

Computer Science, Interdisciplinary Applications

Engineering

Engineering, Biomedical

Engineering, Electrical & Electronic

Imaging Science & Photographic Technology

Life Sciences & Biomedicine

Radiology, Nuclear Medicine & Medical Imaging

Science & Technology

Technology

Details

Title: Subtitle: Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study
Creators: Jie Yang - Columbia University
Elsa D. Angelini - Imperial College London
Pallavi P. Balte - Columbia University Medical Center
Eric A. Hoffman - University of Iowa
John H. M. Austin - Columbia University Medical Center
Benjamin M. Smith - Columbia University Medical Center
R. Graham Barr - Columbia University Medical Center
Andrew F. Laine - Columbia University
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.40(12), pp.3652-3662
DOI: 10.1109/TMI.2021.3094660
PMID: 34224349
PMCID: PMC8715521
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers
Number of pages: 11
Grant note: RO1-HL-112986 / NIH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA R01-HL121270; R01-HL077612; RC1-HL100543; R01-HL093081; N01-HC095159; N01-HC-95169; UL1-RR-024156; UL1-RR-025005 / NIH/National Heart, Lung, and Blood Institute (NHLBI); United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI)
Language: English
Date published: 12/01/2021
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
Record Identifier: 9984318797602771

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