Hasenstab Kyle A, Yuan Nancy, Retson Tara, Conrad Douglas J, Kligerman Seth, Lynch David A, Hsiao Albert
Department of Radiology (K.A.H., N.Y., T.R., S.K., A.H.) and Department of Medicine (D.J.C.), University of California San Diego, 9452 Medical Center Dr, La Jolla, CA 92037; Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.).
Radiol Cardiothorac Imaging. 2021 Apr 8;3(2):e200477. doi: 10.1148/ryct.2021200477. eCollection 2021 Apr.
To develop a deep learning-based algorithm to stage the severity of chronic obstructive pulmonary disease (COPD) through quantification of emphysema and air trapping on CT images and to assess the ability of the proposed stages to prognosticate 5-year progression and mortality.
In this retrospective study, an algorithm using co-registration and lung segmentation was developed in-house to automate quantification of emphysema and air trapping from inspiratory and expiratory CT images. The algorithm was then tested in a separate group of 8951 patients from the COPD Genetic Epidemiology study (date range, 2007-2017). With measurements of emphysema and air trapping, bivariable thresholds were determined to define CT stages of severity (mild, moderate, severe, and very severe) and were evaluated for their ability to prognosticate disease progression and mortality using logistic regression and Cox regression.
On the basis of CT stages, the odds of disease progression were greatest among patients with very severe disease (odds ratio [OR], 2.67; 95% CI: 2.02, 3.53; < .001) and were elevated in patients with moderate disease (OR, 1.50; 95% CI: 1.22, 1.84; = .001). The hazard ratio of mortality for very severe disease at CT was 2.23 times the normal ratio (95% CI: 1.93, 2.58; < .001). When combined with Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging, patients with GOLD stage 2 disease had the greatest odds of disease progression when the CT stage was severe (OR, 4.48; 95% CI: 3.18, 6.31; < .001) or very severe (OR, 4.72; 95% CI: 3.13, 7.13; < .001).
Automated CT algorithms can facilitate staging of COPD severity, have diagnostic performance comparable with that of spirometric GOLD staging, and provide further prognostic value when used in conjunction with GOLD staging.© RSNA, 2021See also commentary by Kalra and Ebrahimian in this issue.
开发一种基于深度学习的算法，通过对CT图像上的肺气肿和气体潴留进行量化来对慢性阻塞性肺疾病（COPD）的严重程度进行分期，并评估所提出的分期对预测5年疾病进展和死亡率的能力。
在这项回顾性研究中，内部开发了一种使用配准和肺分割的算法，以自动量化吸气和呼气CT图像上的肺气肿和气体潴留。然后在来自COPD遗传流行病学研究的另一组8951例患者（日期范围为2007 - 2017年）中对该算法进行测试。通过测量肺气肿和气体潴留，确定双变量阈值以定义严重程度的CT分期（轻度、中度、重度和极重度），并使用逻辑回归和Cox回归评估其预测疾病进展和死亡率的能力。
基于CT分期，极重度疾病患者的疾病进展几率最高（比值比[OR]，2.67；95%可信区间：2.02，3.53；P <.001），中度疾病患者的进展几率也有所升高（OR，1.50；95%可信区间：1.22，1.84；P =.001）。CT上极重度疾病的死亡率风险比是正常比值的2.23倍（95%可信区间：1.93，2.58；P <.001）。当与慢性阻塞性肺疾病全球倡议（GOLD）分期相结合时，CT分期为重度（OR，4.48；95%可信区间：3.18，6.31；P <.001）或极重度（OR，4.72；95%可信区间：3.13，7.13；P <.001）时，GOLD 2期疾病患者的疾病进展几率最高。
自动化CT算法可促进COPD严重程度的分期，其诊断性能与肺功能GOLD分期相当，并且与GOLD分期联合使用时可提供进一步的预后价值。©RSNA，2021另见本期Kalra和Ebrahimian的评论。
