Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans.
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Angelini Elsa D, Yang Jie, Balte Pallavi P, Hoffman Eric A, Manichaikul Ani W, Sun Yifei, Shen Wei, Austin John H M, Allen Norrina B, Bleecker Eugene R, Bowler Russell, Cho Michael H, Cooper Christopher S, Couper David, Dransfield Mark T, Garcia Christine Kim, Han MeiLan K, Hansel Nadia N, Hughes Emlyn, Jacobs David R, Kasela Silva, Kaufman Joel Daniel, Kim John Shinn, Lappalainen Tuuli, Lima Joao, Malinsky Daniel, Martinez Fernando J, Oelsner Elizabeth C, Ortega Victor E, Paine Robert, Post Wendy, Pottinger Tess D, Prince Martin R, Rich Stephen S, Silverman Edwin K, Smith Benjamin M, Swift Andrew J, Watson Karol E, Woodruff Prescott G, Laine Andrew F, Barr R Graham
Аннотация
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.