Using Quantitative Computed Tomographic Imaging to Understand Chronic Obstructive Pulmonary Disease and Fibrotic Interstitial Lung Disease

医学 慢性阻塞性肺病 间质性肺病 放射科 疾病 计算机断层摄影 肺病 气道 肺纤维化 计算机断层摄影术 重症监护医学 病理 内科学 外科
作者
Daniela Castillo-Saldana,Cameron Hague,Harvey O. Coxson,Christopher J. Ryerson
出处
期刊:Journal of Thoracic Imaging [Lippincott Williams & Wilkins]
卷期号:35 (4): 246-254 被引量:19
标识
DOI:10.1097/rti.0000000000000440
摘要

Computed tomography (CT) is commonly used in the evaluation and management of patients with diffuse lung pathologies, including chronic obstructive pulmonary disease (COPD) and fibrotic interstitial lung disease (ILD). In clinical practice, the qualitative (visual) assessment of CT images by a radiologist provides insight into the diagnosis of diffuse lung disease, estimates disease severity, and supports the identification of complications. Quantitative CT (qCT) is an emerging technique that provides some advantages over qualitative assessment. qCT can allow early and accurate detection of emphysema and airway disease, as well as aiding the evaluation of disease burden in both COPD and ILD. This approach is starting to be used as a surrogate biomarker in clinical trials to assess response to therapy. Artificial intelligence techniques have recently been incorporated into qCT, with such rapid evolution that it is currently difficult to determine the exact role it will eventually play in evaluating patients with COPD or pulmonary fibrosis. This article reviews the current state of the art for qualitative and qCT assessment of both COPD and fibrotic ILD. Current areas of controversy and limitations of these techniques are discussed, along with the potential future role of artificial intelligence. Recommendations are provided with regard to the current use of these techniques in the management of patients with diffuse lung disease.

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