支气管扩张
医学
囊性纤维化
放射性武器
金标准(测试)
空气滞留
模式
疾病
重症监护医学
肺病
医学物理学
肺
肺功能测试
放射科
病理
内科学
社会科学
社会学
作者
Katie J Bayfield,Oliver Weinheimer,Christie Boyton,Rachel Fitzpatrick,Anna Middleton,Brendan Kennedy,Anneliese Blaxland,Geshani Jayasuriya,Neil Caplain,Hana Issa,Robert Goetti,Mark O. Wielpütz,Lifeng Yu,Craig J. Galbán,Terry E. Robinson,Brian J. Bartholmai,Dominic A. Fitzgerald,Hiran Selvadurai,Paul D. Robinson
出处
期刊:The European respiratory journal
[European Respiratory Society]
日期:2023-06-29
卷期号:62 (1): 2300286-2300286
被引量:9
标识
DOI:10.1183/13993003.00286-2023
摘要
Optimal characterisation of early structural abnormalities has been highlighted as critical to future efforts to detect and prevent cystic fibrosis (CF) lung disease progression [1]. CT is currently the gold standard, detecting structural disease at an earlier stage than x-ray or lung function testing, with predictive value for subsequent progression and later FEV1 decline [1]. Compared to other emerging radiological modalities for early disease detection, such as MRI, CT is more widely accessible and MRI at present is not able to directly quantify the extent of bronchiectasis due to its limitations in spatial resolution. Current US CF foundation clinical guidelines state that CT should be "considered every 2–3 years, using the lowest possible radiation dose". Early lung disease is identified on CT by air trapping, increase in airway wall thickness and airway diameter, and potentially irreversible bronchiectasis [2]. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: The authors have nothing to disclose.
科研通智能强力驱动
Strongly Powered by AbleSci AI