Improving Radiologist Detection of Lung Nodules with CAD

描述（由申请人提供）： 肺癌是美国癌症相关死亡的主要原因。原发性和转移性肺癌最常见的表现为肺结节，这是很容易可视化放射线。CT扫描是目前可用于检测肺结节的最灵敏的非侵入性手段，但其灵敏度有限且观察者间变异性高，特别是对于较小的结节。多探测器行CT（MDCT）的最新发展允许以前所未有的三维空间分辨率对肺部进行成像，在单个不到10秒的屏气内，该分辨率比单排CT系统高10倍。对于放射科医生来说，利用MDCT数据的更高空间分辨率来改善肺结节检测，他们必须克服两个关键挑战-（1）对由肺部的高分辨率MDCT扫描产生的300-600个图像的时间有效解释，以及（2）在检查1 mm厚的CT切片时提高结节检测灵敏度而不丧失特异性，其中，与厚截面采集相比，由于肺结节和血管的外观的更大相似性，所以区分肺结节和血管更加困难。因此，该提案的重点是开发一种优化的方法来检测肺癌的CT。我们的具体目标是： 1.目的：研究一种从肺部CT数据中自动检测肺结节的方法。 2.确定在对CT图像进行初步放射科医师评估后，考虑计算机辅助检测（CAD）结果时，放射科医师检测疑似肺结节患者的灵敏度和观察者间一致性的改善。我们将通过对从美国不同地区的两个医疗中心获得的肺结节CT扫描进行培训来优化我们的CAD系统，我们将证明CAD可以像第二个放射科医生一样有效地提高放射科医生在CT扫描上检测肺结节的能力，而不会大幅增加假阳性检测。这项工作完成后，我们将使放射科医生能够更好地利用MDCT扫描仪提供的改进数据，并大大提高他们在CT扫描中检测肺结节的能力，从而有助于改善肺癌患者的管理和护理。

DESCRIPTION (provided by applicant): Lung cancer is the leading cause of cancer-related deaths in the United States. Both primary and metastatic lung cancer most commonly manifest as pulmonary nodules, which are readily visualized radiographically. CT scanning is currently the most sensitive non-invasive means available for detecting pulmonary nodules, but has suffered from limited sensitivity and high interobserver variability, particularly of smaller nodules. The recent development of multi-detector-row CT (MDCT) allows imaging of the lungs with unprecedented three-dimensional spatial resolution, up to 10 times greater than single-row CT systems within a single less than 10 second breathhold. For radiologists to harness the higher spatial resolution of MDCT data to improve lung nodule detection, they must overcome two key challenges - (1) time efficient interpretation of the 300-600 images that result from high-resolution MDCT scans of the lungs and (2) improve nodule detection sensitivity without losing specificity when examining 1-mm thick CT sections, where lung nodule and blood vessel discrimination is more difficult due to the greater similarity of their appearance when compared to thick-section acquisitions. The focus of this proposal, therefore, is to develop an optimized approach toward the detection of lung cancer with CT. Our specific aims are: 1. To develop an automatic technique for detecting pulmonary nodules from lung CT data. 2. To determine the improvement in radiologist sensitivity and interobserver agreement for the detection of pulmonary nodules in patients suspected of having them when computer-aided detection (CAD) results are considered following initial radiologist assessment of CT images. We will optimize our CAD system by training on CT scans of pulmonary nodules obtained from two medical centers in different regions of the United States, and we will show that CAD can be as effective as a second radiologist in improving a radiologist's ability to detect pulmonary nodules on CT scans without substantially increasing falsely positive detections. Upon completion of this work, we will have enabled radiologists to take better advantage of the improved data available from MDCT scanners and substantially improve their ability to detect pulmonary nodules on CT scans, and thereby contribute to improvements in the management and care of patients with lung cancer.