Elsa D Angelini, Jie Yang, Pallavi P Balte, Eric A Hoffman, Ani W Manichaikul, Yifei Sun, Wei Shen, John H M Austin, Norrina B Allen, Eugene R Bleecker, Russell 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 Daniel Kaufman, John Shinn Kim, Tuuli Lappalainen, Joao Lima, Daniel Malinsky, Fernando J Martinez, Elizabeth C Oelsner, Victor E Ortega, Robert Paine, Wendy 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
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...
November 2023: Thorax