Cui Sijia, Shu Zhenyu, Ma Yanqing, Lin Yi, Wang Haochu, Cao Hanbo, Liu Jing, Gong Xiangyang
Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
Hangzhou Medical College, Institute of Artificial Intelligence and Remote Imaging, Hangzhou, China.
Front Med (Lausanne). 2022 Sep 13;9:944294. doi: 10.3389/fmed.2022.944294. eCollection 2022.
The common respiratory abnormality, small airway dysfunction (fSAD), is easily neglected. Its prognostic factors, prevalence, and risk factors are unclear. This study aimed to explore the early detection of fSAD using radiomic analysis of computed tomography (CT) images to predict fSAD progress. The patients were divided into fSAD and non-fSAD groups and divided randomly into a training group ( = 190) and a validation group ( = 82) at a 7:3 ratio. Lung kit software was used for automatic delineation of regions of interest (ROI) on chest CT images. The most valuable imaging features were selected and a radiomic score was established for risk assessment. Multivariate logistic regression analysis showed that age, radiomic score, smoking, and history of asthma were significant predictors of fSAD ( < 0.05). Results suggested that the radiomic nomogram model provides clinicians with useful data and could represent a reliable reference to form fSAD clinical treatment strategies.
常见的呼吸异常，即小气道功能障碍（fSAD），很容易被忽视。其预后因素、患病率和危险因素尚不清楚。本研究旨在利用计算机断层扫描（CT）图像的放射组学分析来探索fSAD的早期检测，以预测fSAD的进展。将患者分为fSAD组和非fSAD组，并以7:3的比例随机分为训练组（n = 190）和验证组（n = 82）。使用肺部套件软件在胸部CT图像上自动勾勒感兴趣区域（ROI）。选择最有价值的影像特征并建立放射组学评分用于风险评估。多因素逻辑回归分析显示，年龄、放射组学评分、吸烟和哮喘病史是fSAD的显著预测因素（P < 0.05）。结果表明，放射组学列线图模型为临床医生提供了有用的数据，并且可以作为制定fSAD临床治疗策略的可靠参考。
