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. SPIROMICS and MESA data are available to the scientific community as described in the Acknowledgements section and on the study websites.

中文翻译：

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通过 CT 扫描的无监督机器学习定义的肺气肿亚型


背景 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 CT 图像上的无监督机器学习是否会发现具有独特特征、预后和遗传关联的 CT 肺气肿亚型。方法 通过无监督机器学习，仅根据 COPD 病例对照研究（SPIROMICS）中 2853 名参与者的 CT 扫描中肺气肿区域的纹理和位置来识别新的 CT 肺气肿亚型，然后提供数据减少。将亚型与基于人群的动脉粥样硬化多种族研究 (MESA) 肺研究的 2949 名参与者的症状和生理学以及 6658 名 MESA 参与者的预后进行了比较。检查了与全基因组单核苷酸多态性的关联。结果 该算法发现了六种可重复的（学习者组内相关系数，0.91-1.00）CT 肺气肿亚型。 SPIROMICS 中最常见的亚型，即支气管炎-心尖联合亚型，与慢性支气管炎、肺功能加速衰退、住院、死亡、事件气流受限以及 DRD1 附近的基因变异相关，该基因变异与粘蛋白分泌过多有关 (p=1.1 × 10−8).其次，弥漫性亚型与体重减轻、呼吸系统住院和死亡以及事件气流受限相关。第三个仅与年龄有关。第四和第五例在视觉上类似于合并性肺纤维化肺气肿，并且具有不同的症状、生理学、预后和遗传关联。第六个在视觉上类似于肺消失综合症。 结论 CT 扫描上的大规模无监督机器学习定义了六种可重复的、熟悉的 CT 肺气肿亚型，为 COPD 和慢性阻塞性肺病前期的具体诊断和个性化治疗提供了途径。 SPIROMICS 和 MESA 数据可供科学界使用，如致谢部分和研究网站上所述。