Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, USA.
Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
Int J Chron Obstruct Pulmon Dis. 2022 Apr 26;17:919-930. doi: 10.2147/COPD.S334592. eCollection 2022.
Chronic obstructive pulmonary disease (COPD) is heterogenous in its clinical manifestations and disease progression. Patients often have disease courses that are difficult to predict with readily available data, such as lung function testing. The ability to better classify COPD into well-defined groups will allow researchers and clinicians to tailor novel therapies, monitor their effects, and improve patient-centered outcomes. Different modalities of assessing these COPD phenotypes are actively being studied, and an area of great promise includes the use of quantitative computed tomography (QCT) techniques focused on key features such as airway anatomy, lung density, and vascular morphology. Over the last few decades, companies around the world have commercialized automated CT software packages that have proven immensely useful in these endeavors. This article reviews the key features of several commercial platforms, including the technologies they are based on, the metrics they can generate, and their clinical correlations and applications. While such tools are increasingly being used in research and clinical settings, they have yet to be consistently adopted for diagnostic work-up and treatment planning, and their full potential remains to be explored.
慢性阻塞性肺疾病（COPD）在临床表现和疾病进展方面具有异质性。患者的疾病过程往往难以用现有数据（如肺功能测试）预测。更好地将 COPD 分为明确的组别，将使研究人员和临床医生能够为患者量身定制新型疗法，监测其疗效，并改善以患者为中心的结果。目前正在积极研究评估这些 COPD 表型的不同方式，一个非常有前途的领域包括使用定量计算机断层扫描（QCT）技术，重点关注气道解剖、肺密度和血管形态等关键特征。在过去几十年中，全球各地的公司已经将自动化 CT 软件包商业化，这些软件包在这些努力中被证明非常有用。本文综述了几个商业平台的关键特征，包括它们所基于的技术、可以生成的指标，以及它们的临床相关性和应用。尽管这些工具在研究和临床环境中越来越多地被使用，但它们尚未被一致地用于诊断和治疗计划，其全部潜力仍有待探索。
