Department of Electrical and Computer Engineering, The Ohio State University College of Engineering, Columbus, OH; Department of Radiology, The Ohio State University College of Medicine, Columbus, OH.
Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, The Ohio State University College of Medicine, Columbus, OH.
Chest. 2012 Dec;142(6):1589-1597. doi: 10.1378/chest.11-2027.
Chest CT scans are commonly used to clinically assess disease severity in patients presenting with pulmonary sarcoidosis. Despite their ability to reliably detect subtle changes in lung disease, the utility of chest CT scans for guiding therapy is limited by the fact that image interpretation by radiologists is qualitative and highly variable. We sought to create a computerized CT image analysis tool that would provide quantitative and clinically relevant information.
We established that a two-point correlation analysis approach reduced the background signal attendant to normal lung structures, such as blood vessels, airways, and lymphatics while highlighting diseased tissue. This approach was applied to multiple lung fields to generate an overall lung texture score (LTS) representing the quantity of diseased lung parenchyma. Using deidentified lung CT scan and pulmonary function test (PFT) data from The Ohio State University Medical Center's Information Warehouse, we analyzed 71 consecutive CT scans from patients with sarcoidosis for whom simultaneous matching PFTs were available to determine whether the LTS correlated with standard PFT results.
We found a high correlation between LTS and FVC, total lung capacity, and diffusing capacity of the lung for carbon monoxide (P < .0001 for all comparisons). Moreover, LTS was equivalent to PFTs for the detection of active lung disease. The image analysis protocol was conducted quickly (< 1 min per study) on a standard laptop computer connected to a publicly available National Institutes of Health ImageJ toolkit.
The two-point image analysis tool is highly practical and appears to reliably assess lung disease severity. We predict that this tool will be useful for clinical and research applications.
胸部 CT 扫描常用于评估肺结节病患者的疾病严重程度。尽管它们能够可靠地检测到肺部疾病的细微变化，但由于放射科医生的图像解释是定性的且高度可变，因此胸部 CT 扫描在指导治疗方面的作用有限。我们试图创建一种计算机 CT 图像分析工具，该工具将提供定量和临床相关的信息。
我们确定了一种两点相关分析方法，该方法降低了伴随正常肺结构（如血管、气道和淋巴管）的背景信号，同时突出了病变组织。该方法应用于多个肺区，生成代表病变肺实质量的整体肺纹理评分（LTS）。我们利用俄亥俄州立大学医疗中心信息仓库的匿名肺 CT 扫描和肺功能测试（PFT）数据，分析了 71 例来自结节病患者的连续 CT 扫描，这些患者同时有匹配的 PFT 数据。目的是确定 LTS 是否与标准 PFT 结果相关。
我们发现 LTS 与 FVC、总肺活量和一氧化碳弥散量之间存在高度相关性（所有比较的 P <.0001）。此外，LTS 与 PFT 一样可用于检测活动性肺病。图像分析协议在连接到公共 NIH ImageJ 工具包的标准笔记本电脑上快速（每项研究 < 1 分钟）进行。
两点图像分析工具非常实用，似乎能够可靠地评估肺部疾病的严重程度。我们预测，该工具将在临床和研究应用中非常有用。
