定量胸部计算机断层扫描和机器学习用于长期COVID中小气管疾病的亚表型化
Rodrigo Caruso Chate1,2, Carlos Roberto Ribeiro Carvalho3, Marcio Valente Yamada Sawamura1
1Department of Radiology.
Journal of thoracic imaging
|October 16, 2025
概括
机器学习在长期COVID患者中确定了四种不同的成像表型,包括与特定肺功能异常相关的小呼吸道疾病 (SAD) 集群. 这种方法有助于了解COVID后的呼吸道并发症.
科学领域:
- 肺部医学 肺部医学
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 长期COVID,是SARS-CoV-2感染后的疾病,可能导致持续的呼吸系统问题.
- 定量CT (QCT) 和机器学习 (ML) 为分析肺部成像提供了先进的工具.
- 小呼吸道疾病 (SAD) 是一种已知的并发症,但其在COVID后患者中的成像表型需要进一步调查.
研究的目的:
- 整合定量CT (QCT) 和机器学习 (ML) 来识别住院后的COVID-19幸存者的成像表型.
- 专门调查小呼吸道疾病 (SAD) 现型及其与肺功能测试 (肺结核学) 的相关性.
主要方法:
- 对257名成年COVID-19幸存者的回顾性分析,他们在住院6-12个月后接受了体积吸气和呼吸胸部CT和胸膜扫描.
- 使用AI-Rad伴侣胸部CT提取了定量CT (QCT) 参数.
- 使用QCT参数的等级聚类来识别不同的患者表型.
主要成果:
- 确定了四种不同的成像表型:"SAD" (14%),"中间" (39%),"年轻纤维化" (31%),和"老纤维化" (16%).
- SAD 集群的残留体积 (RV) / 肺总容量 (TLC) 比率较高,FEF25-75/强迫生命能力 (FVC) 比率较低.
- 较旧的纤维菌群体显示TLC和FVC的最低值,而较年轻的纤维菌群体具有较低的RV/TLC比率和更高的FEF25-75.
结论:
- 整合QCT和ML成功地在长期COVID患者中确定了不同的成像表型.
- 鉴定出一种独特的SAD表型,与特定的肺功能异常密切相关.
- 这种方法为在长期COVID的背景下表征肺部异常提供了一种新的方法.
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