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计算机辅助诊断(CAD)在64排多层螺旋计算机断层扫描检测肺结节中的作用

Role of Computer Aided Diagnosis (CAD) in the detection of pulmonary nodules on 64 row multi detector computed tomography.

作者信息

Prakashini K, Babu Satish, Rajgopal K V, Kokila K Raja

机构信息

Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal University, Manipal, Udupi, Karnataka, India.

Consultant Radiologist, Jansons Health (P) Ltd., Erode, Tamil Nadu, India.

出版信息

Lung India. 2016 Jul-Aug;33(4):391-7. doi: 10.4103/0970-2113.184872.

Abstract

AIMS AND OBJECTIVES

To determine the overall performance of an existing CAD algorithm with thin-section computed tomography (CT) in the detection of pulmonary nodules and to evaluate detection sensitivity at a varying range of nodule density, size, and location.

MATERIALS AND METHODS

A cross-sectional prospective study was conducted on 20 patients with 322 suspected nodules who underwent diagnostic chest imaging using 64-row multi-detector CT. The examinations were evaluated on reconstructed images of 1.4 mm thickness and 0.7 mm interval. Detection of pulmonary nodules, initially by a radiologist of 2 years experience (RAD) and later by CAD lung nodule software was assessed. Then, CAD nodule candidates were accepted or rejected accordingly. Detected nodules were classified based on their size, density, and location. The performance of the RAD and CAD system was compared with the gold standard that is true nodules confirmed by consensus of senior RAD and CAD together. The overall sensitivity and false-positive (FP) rate of CAD software was calculated.

OBSERVATIONS AND RESULTS

Of the 322 suspected nodules, 221 were classified as true nodules on the consensus of senior RAD and CAD together. Of the true nodules, the RAD detected 206 (93.2%) and 202 (91.4%) by the CAD. CAD and RAD together picked up more number of nodules than either CAD or RAD alone. Overall sensitivity for nodule detection with the CAD program was 91.4%, and FP detection per patient was 5.5%. The CAD showed comparatively higher sensitivity for nodules of size 4-10 mm (93.4%) and nodules in hilar (100%) and central (96.5%) location when compared to RAD's performance.

CONCLUSION

CAD performance was high in detecting pulmonary nodules including the small size and low-density nodules. CAD even with relatively high FP rate, assists and improves RAD's performance as a second reader, especially for nodules located in the central and hilar region and for small nodules by saving RADs time.

摘要

目的

确定现有的计算机辅助检测(CAD)算法结合薄层计算机断层扫描(CT)在检测肺结节方面的整体性能,并评估在不同结节密度、大小和位置范围内的检测敏感性。

材料与方法

对20例有322个疑似结节的患者进行了横断面前瞻性研究,这些患者接受了64排多探测器CT胸部诊断成像。检查在厚度为1.4mm、层间距为0.7mm的重建图像上进行评估。首先由一位有2年经验的放射科医生(RAD)检测肺结节,随后使用CAD肺结节软件进行检测评估。然后,相应地接受或拒绝CAD检测出的结节候选对象。根据检测出的结节的大小、密度和位置进行分类。将RAD和CAD系统的性能与由资深RAD和CAD共同达成共识确认的真实结节这一金标准进行比较。计算CAD软件的总体敏感性和假阳性(FP)率。

观察结果

在322个疑似结节中,经资深RAD和CAD共同达成共识,221个被分类为真实结节。在这些真实结节中,RAD检测出206个(93.2%),CAD检测出202个(91.4%)。CAD和RAD共同检测出的结节数量比单独的CAD或RAD都要多。使用CAD程序检测结节的总体敏感性为91.4%,每位患者的FP检测率为5.5%。与RAD的表现相比,CAD对4 - 10mm大小的结节(93.4%)以及肺门(100%)和中央(96.5%)位置的结节显示出相对较高的敏感性。

结论

CAD在检测肺结节方面表现出色,包括小尺寸和低密度结节。CAD即使假阳性率相对较高,但作为第二阅片者有助于并提高RAD的性能,特别是对于位于中央和肺门区域的结节以及小结节,还能节省RAD的时间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3481/4948226/93eb0edd0b3d/LI-33-391-g001.jpg

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