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
Department of Biomedical Engineering, Columbia University, New York, New York, USA.
LTCI, Institut Polytechnique de Paris, Telecom Paris, Palaiseau, France.
Thorax. 2023 Nov;78(11):1067-1079. doi: 10.1136/thorax-2022-219158. Epub 2023 Jun 2.
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.
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.
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 , which is implicated in mucin hypersecretion (p=1.1 ×10). 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.
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.
慢性阻塞性肺疾病(COPD)的治疗和预防进展缓慢,部分原因是亚表型有限。我们测试了对CT图像进行无监督机器学习是否能发现具有不同特征、预后和基因关联的CT肺气肿亚型。
在慢性阻塞性肺疾病研究(SPIROMICS)这一COPD病例对照研究中,对2853名参与者的CT扫描中肺气肿区域的纹理和位置进行无监督机器学习,识别出新的CT肺气肿亚型,随后进行数据简化。在基于人群的动脉粥样硬化多民族研究(MESA)肺部研究的2949名参与者中,将这些亚型与症状和生理指标进行比较,并在6658名MESA参与者中评估其预后。检查与全基因组单核苷酸多态性的关联。
该算法发现了六种可重复的(学习者间组内相关系数,0.91 - 1.00)CT肺气肿亚型。SPIROMICS中最常见的亚型,即支气管炎 - 顶端联合亚型,与慢性支气管炎、肺功能加速下降、住院、死亡、新发气流受限以及一个与粘蛋白分泌过多有关的基因变异相关(p = 1.1×10)。第二种,弥漫性亚型与体重较低、呼吸相关住院和死亡以及新发气流受限有关。第三种仅与年龄有关。第四种和第五种在视觉上类似于合并性肺纤维化肺气肿,且具有不同的症状、生理指标、预后和基因关联。第六种在视觉上类似于肺消失综合征。
对CT扫描进行大规模无监督机器学习定义了六种可重复的、常见的CT肺气肿亚型,为COPD和COPD前期的特定诊断和个性化治疗提供了途径。