Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans Article Swipe
Elsa D. Angelini
,
Jie Yang
,
Pallavi Balte
,
Eric A. Hoffman
,
Ani Manichaikul
,
Yifei Sun
,
Wei Shen
,
John H. M. Austin
,
Norrina B. Allen
,
Eugene R. Bleecker
,
Russell P. Bowler
,
Michael H. Cho
,
Christopher S. Cooper
,
David Couper
,
Mark T. Dransfield
,
Christine Kim Garcia
,
MeiLan K. Han
,
Nadia N. Hansel
,
Emlyn Hughes
,
David R. Jacobs
,
Silva Kasela
,
Joel D. Kaufman
,
John S. Kim
,
Tuuli Lappalainen
,
João A.C. Lima
,
Daniel Malinsky
,
Fernando J. Martínez
,
Elizabeth C. Oelsner
,
Victor E. Ortega
,
Robert Paine
,
Wendy S. Post
,
Tess D. Pottinger
,
Martin R. Prince
,
Stephen S. Rich
,
Edwin K. Silverman
,
Benjamin M. Smith
,
Andrew J. Swift
,
Karol E. Watson
,
Prescott G. Woodruff
,
Andrew F. Laine
,
R. Graham Barr
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1136/thorax-2022-219158
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1136/thorax-2022-219158
Background Treatment and preventative advances for chronic obstructive pulmonary disease (COPD) have been slow due, in part, to limited subphenotypes. We tested if unsupervised machine learning on CT images would discover CT emphysema subtypes with distinct characteristics, prognoses and genetic associations. Methods New CT emphysema subtypes were identified by unsupervised machine learning on only the texture and location of emphysematous regions on CT scans from 2853 participants in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS), a COPD case–control study, followed by data reduction. Subtypes were compared with symptoms and physiology among 2949 participants in the population-based Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study and with prognosis among 6658 MESA participants. Associations with genome-wide single-nucleotide-polymorphisms were examined. Results The algorithm discovered six reproducible (interlearner intraclass correlation coefficient, 0.91–1.00) CT emphysema subtypes. The most common subtype in SPIROMICS, the combined bronchitis-apical subtype, was associated with chronic bronchitis, accelerated lung function decline, hospitalisations, deaths, incident airflow limitation and a gene variant near DRD1 , which is implicated in mucin hypersecretion (p=1.1 ×10 −8 ). The second, the diffuse subtype was associated with lower weight, respiratory hospitalisations and deaths, and incident airflow limitation. The third was associated with age only. The fourth and fifth visually resembled combined pulmonary fibrosis emphysema and had distinct symptoms, physiology, prognosis and genetic associations. The sixth visually resembled vanishing lung syndrome. Conclusion Large-scale unsupervised machine learning on CT scans defined six reproducible, familiar CT emphysema subtypes that suggest paths to specific diagnosis and personalised therapies in COPD and pre-COPD.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1136/thorax-2022-219158
- OA Status
- green
- Cited By
- 31
- References
- 77
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379137326
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4379137326Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1136/thorax-2022-219158Digital Object Identifier
- Title
-
Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scansWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-02Full publication date if available
- Authors
-
Elsa D. Angelini, Jie Yang, Pallavi Balte, Eric A. Hoffman, Ani Manichaikul, Yifei Sun, Wei Shen, John H. M. Austin, Norrina B. Allen, Eugene R. Bleecker, Russell P. Bowler, Michael H. Cho, Christopher S. Cooper, David Couper, Mark T. Dransfield, Christine Kim Garcia, MeiLan K. Han, Nadia N. Hansel, Emlyn Hughes, David R. Jacobs, Silva Kasela, Joel D. Kaufman, John S. Kim, Tuuli Lappalainen, João A.C. Lima, Daniel Malinsky, Fernando J. Martínez, Elizabeth C. Oelsner, Victor E. Ortega, Robert Paine, Wendy S. Post, Tess D. Pottinger, Martin R. Prince, Stephen S. Rich, Edwin K. Silverman, Benjamin M. Smith, Andrew J. Swift, Karol E. Watson, Prescott G. Woodruff, Andrew F. Laine, R. Graham BarrList of authors in order
- Landing page
-
https://doi.org/10.1136/thorax-2022-219158Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://pmc.ncbi.nlm.nih.gov/articles/PMC10592007/pdf/nihms-1928370.pdfDirect OA link when available
- Concepts
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Medicine, COPD, Chronic bronchitis, Lung, Population, Internal medicine, Bronchitis, Pathology, Environmental healthTop concepts (fields/topics) attached by OpenAlex
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31Total citation count in OpenAlex
- Citations by year (recent)
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2025: 10, 2024: 18, 2023: 3Per-year citation counts (last 5 years)
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77Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.only | 53 |
| abstract_inverted_index.slow | 13 |
| abstract_inverted_index.that | 240 |
| abstract_inverted_index.were | 46, 87, 117 |
| abstract_inverted_index.with | 34, 89, 107, 114, 145, 181, 196 |
| abstract_inverted_index.×10 | 171 |
| abstract_inverted_index.−8 | 172 |
| abstract_inverted_index.Study | 76, 100, 105 |
| abstract_inverted_index.among | 93, 109 |
| abstract_inverted_index.fifth | 202 |
| abstract_inverted_index.lower | 182 |
| abstract_inverted_index.mucin | 168 |
| abstract_inverted_index.only. | 198 |
| abstract_inverted_index.part, | 16 |
| abstract_inverted_index.paths | 242 |
| abstract_inverted_index.scans | 63, 232 |
| abstract_inverted_index.sixth | 219 |
| abstract_inverted_index.third | 193 |
| abstract_inverted_index.which | 164 |
| abstract_inverted_index.would | 29 |
| abstract_inverted_index.(COPD) | 10 |
| abstract_inverted_index.(MESA) | 103 |
| abstract_inverted_index.(p=1.1 | 170 |
| abstract_inverted_index.common | 135 |
| abstract_inverted_index.fourth | 200 |
| abstract_inverted_index.images | 28 |
| abstract_inverted_index.study, | 81 |
| abstract_inverted_index.tested | 21 |
| abstract_inverted_index.Methods | 41 |
| abstract_inverted_index.Outcome | 72 |
| abstract_inverted_index.Results | 119 |
| abstract_inverted_index.airflow | 155, 190 |
| abstract_inverted_index.chronic | 6, 146 |
| abstract_inverted_index.deaths, | 153, 187 |
| abstract_inverted_index.defined | 233 |
| abstract_inverted_index.diffuse | 177 |
| abstract_inverted_index.disease | 9 |
| abstract_inverted_index.genetic | 39, 216 |
| abstract_inverted_index.limited | 18 |
| abstract_inverted_index.machine | 24, 50, 228 |
| abstract_inverted_index.regions | 60 |
| abstract_inverted_index.second, | 175 |
| abstract_inverted_index.subtype | 136, 178 |
| abstract_inverted_index.suggest | 241 |
| abstract_inverted_index.texture | 55 |
| abstract_inverted_index.variant | 160 |
| abstract_inverted_index.weight, | 183 |
| abstract_inverted_index.Measures | 73 |
| abstract_inverted_index.Subtypes | 86 |
| abstract_inverted_index.advances | 4 |
| abstract_inverted_index.combined | 140, 205 |
| abstract_inverted_index.compared | 88 |
| abstract_inverted_index.decline, | 151 |
| abstract_inverted_index.discover | 30 |
| abstract_inverted_index.distinct | 35, 211 |
| abstract_inverted_index.familiar | 236 |
| abstract_inverted_index.fibrosis | 207 |
| abstract_inverted_index.followed | 82 |
| abstract_inverted_index.function | 150 |
| abstract_inverted_index.incident | 154, 189 |
| abstract_inverted_index.learning | 25, 51, 229 |
| abstract_inverted_index.location | 57 |
| abstract_inverted_index.specific | 244 |
| abstract_inverted_index.subtype, | 142 |
| abstract_inverted_index.subtypes | 33, 45, 239 |
| abstract_inverted_index.symptoms | 90 |
| abstract_inverted_index.visually | 203, 220 |
| abstract_inverted_index.Treatment | 1 |
| abstract_inverted_index.algorithm | 121 |
| abstract_inverted_index.diagnosis | 245 |
| abstract_inverted_index.emphysema | 32, 44, 131, 208, 238 |
| abstract_inverted_index.examined. | 118 |
| abstract_inverted_index.pre-COPD. | 252 |
| abstract_inverted_index.prognoses | 37 |
| abstract_inverted_index.prognosis | 108, 214 |
| abstract_inverted_index.pulmonary | 8, 206 |
| abstract_inverted_index.resembled | 204, 221 |
| abstract_inverted_index.subtypes. | 132 |
| abstract_inverted_index.symptoms, | 212 |
| abstract_inverted_index.syndrome. | 224 |
| abstract_inverted_index.therapies | 248 |
| abstract_inverted_index.vanishing | 222 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Conclusion | 225 |
| abstract_inverted_index.SPIROMICS, | 138 |
| abstract_inverted_index.associated | 144, 180, 195 |
| abstract_inverted_index.discovered | 122 |
| abstract_inverted_index.identified | 47 |
| abstract_inverted_index.implicated | 166 |
| abstract_inverted_index.intraclass | 126 |
| abstract_inverted_index.limitation | 156 |
| abstract_inverted_index.physiology | 92 |
| abstract_inverted_index.reduction. | 85 |
| abstract_inverted_index.Large-scale | 226 |
| abstract_inverted_index.accelerated | 148 |
| abstract_inverted_index.bronchitis, | 147 |
| abstract_inverted_index.correlation | 127 |
| abstract_inverted_index.genome-wide | 115 |
| abstract_inverted_index.limitation. | 191 |
| abstract_inverted_index.obstructive | 7 |
| abstract_inverted_index.physiology, | 213 |
| abstract_inverted_index.respiratory | 184 |
| abstract_inverted_index.(SPIROMICS), | 77 |
| abstract_inverted_index.0.91–1.00) | 129 |
| abstract_inverted_index.Associations | 113 |
| abstract_inverted_index.Intermediate | 71 |
| abstract_inverted_index.Multi-Ethnic | 99 |
| abstract_inverted_index.coefficient, | 128 |
| abstract_inverted_index.participants | 66, 95 |
| abstract_inverted_index.personalised | 247 |
| abstract_inverted_index.preventative | 3 |
| abstract_inverted_index.reproducible | 124 |
| abstract_inverted_index.unsupervised | 23, 49, 227 |
| abstract_inverted_index.(interlearner | 125 |
| abstract_inverted_index.associations. | 40, 217 |
| abstract_inverted_index.emphysematous | 59 |
| abstract_inverted_index.participants. | 112 |
| abstract_inverted_index.reproducible, | 235 |
| abstract_inverted_index.Subpopulations | 69 |
| abstract_inverted_index.case–control | 80 |
| abstract_inverted_index.hypersecretion | 169 |
| abstract_inverted_index.subphenotypes. | 19 |
| abstract_inverted_index.Atherosclerosis | 102 |
| abstract_inverted_index.characteristics, | 36 |
| abstract_inverted_index.hospitalisations | 185 |
| abstract_inverted_index.population-based | 98 |
| abstract_inverted_index.bronchitis-apical | 141 |
| abstract_inverted_index.hospitalisations, | 152 |
| abstract_inverted_index.single-nucleotide-polymorphisms | 116 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5070233463 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 41 |
| corresponding_institution_ids | https://openalex.org/I2799503643 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.800000011920929 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.98400908 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |