慢性阻塞性肺疾病影像组学研究进展  

The Progress in Radiomics of Chronic Obstructive Pulmonary Disease

作　　者：杨立恒[1] 张莹[1] 卢芳 梁硕[2] 李月川[1] 李毅[1] YANG Liheng;ZHANG Ying;LU Fang;LIANG Shuo;LI Yuechuan;LI Yi(Department of Respiratory and Critical Care Medicine,Tianjin Chest Hospital,Tianjin 300222,China;Department of Radiology,Tianjin Chest Hospital,Tianjin 300222,China)

机构地区：[1]天津市胸科医院呼吸与危重症医学科,天津300222 [2]天津市胸科医院影像科,天津300222

出　　处：《医学综述》2025年第2期208-213,共6页Medical Recapitulate

基　　金：天津市医学重点学科(专科)建设项目(TJYXZDXK-049A);天津市卫生健康科技项目(TJWJ2024ZD007)。

摘　　要：慢性阻塞性肺疾病(COPD)是呼吸系统常见疾病,肺功能检查是评估疾病严重程度的最常用方法,但其并不能充分评估大气道和小气道对疾病进展的影响,以及肺泡破坏的程度。近年来,胸部CT检查已成为评估COPD的重要手段之一。目前基于胸部CT的影像组学研究快速发展,各种人工智能及机器学习的影像组学方法不断涌现,通过提取影像组学特征,使用机器学习工具进行分析,揭示这些影像组学特征与临床转归之间的联系,有助于制订COPD治疗策略,评估疗效和预后,为COPD的治疗及早期诊断提供更多依据。Chronic obstructive pulmonary disease(COPD)is a common disease of the respiratory system.Lung function examination is the most common method to assess the severity of the disease,but it does not fully assess the impact of atmospheric and small airway on the progression of the disease,and the extent of alveolar damage.In recent years,chest CT examination has become one of the important means to evaluating COPD.At present,radiomics research based on chest CT is developing rapidly,and various artificial intelligence and machine learning radiomics methods are constantly emerging.By extracting imaging features and using machine learning tools for analysis,uncovering the association between these radiomics features and clinical endpoints can help develop the treatment strategies for COPD,evaluate the efficacy and prognosis,and provide more basis for the treatment and early diagnosis of COPD.

关 键 词：慢性阻塞性肺疾病 肺气肿 胸部CT 影像组学 定量CT 

分 类 号：R563[医药卫生—呼吸系统]