Rubin Geoffrey D, Lyo John K, Paik David S, Sherbondy Anthony J, Chow Lawrence C, Leung Ann N, Mindelzun Robert, Schraedley-Desmond Pamela K, Zinck Steven E, Naidich David P, Napel Sandy
Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, S-072, Stanford, CA 94305-5105, USA.
Radiology. 2005 Jan;234(1):274-83. doi: 10.1148/radiol.2341040589. Epub 2004 Nov 10.
To compare the performance of radiologists and of a computer-aided detection (CAD) algorithm for pulmonary nodule detection on thin-section thoracic computed tomographic (CT) scans.
The study was approved by the institutional review board. The requirement of informed consent was waived. Twenty outpatients (age range, 15-91 years; mean, 64 years) were examined with chest CT (multi-detector row scanner, four detector rows, 1.25-mm section thickness, and 0.6-mm interval) for pulmonary nodules. Three radiologists independently analyzed CT scans, recorded the locus of each nodule candidate, and assigned each a confidence score. A CAD algorithm with parameters chosen by using cross validation was applied to the 20 scans. The reference standard was established by two experienced thoracic radiologists in consensus, with blind review of all nodule candidates and free search for additional nodules at a dedicated workstation for three-dimensional image analysis. True-positive (TP) and false-positive (FP) results and confidence levels were used to generate free-response receiver operating characteristic (ROC) plots. Double-reading performance was determined on the basis of TP detections by either reader.
The 20 scans showed 195 noncalcified nodules with a diameter of 3 mm or more (reference reading). Area under the alternative free-response ROC curve was 0.54, 0.48, 0.55, and 0.36 for CAD and readers 1-3, respectively. Differences between reader 3 and CAD and between readers 2 and 3 were significant (P < .05); those between CAD and readers 1 and 2 were not significant. Mean sensitivity for individual readings was 50% (range, 41%-60%); double reading resulted in increase to 63% (range, 56%-67%). With CAD used at a threshold allowing only three FP detections per CT scan, mean sensitivity was increased to 76% (range, 73%-78%). CAD complemented individual readers by detecting additional nodules more effectively than did a second reader; CAD-reader weighted kappa values were significantly lower than reader-reader weighted kappa values (Wilcoxon rank sum test, P < .05).
With CAD used at a level allowing only three FP detections per CT scan, sensitivity was substantially higher than with conventional double reading.
比较放射科医生与计算机辅助检测（CAD）算法在胸部薄层计算机断层扫描（CT）上检测肺结节的性能。
本研究经机构审查委员会批准，无需知情同意。对20名门诊患者（年龄范围15 - 91岁，平均64岁）进行胸部CT检查（多层螺旋扫描仪，4排探测器，层厚1.25 mm，层间距0.6 mm）以检测肺结节。三位放射科医生独立分析CT扫描图像，记录每个结节候选位置，并给出置信度评分。将通过交叉验证选择参数的CAD算法应用于这20份扫描图像。由两位经验丰富的胸部放射科医生达成共识建立参考标准，对所有结节候选进行盲法审查，并在专用的三维图像分析工作站上自由搜索其他结节。真阳性（TP）和假阳性（FP）结果及置信度用于生成自由响应式接收者操作特征（ROC）曲线。基于任一阅片者的TP检测确定双读性能。
20份扫描图像显示195个直径3 mm或更大的非钙化结节（参考阅片）。CAD及阅片者1 - 3的替代自由响应ROC曲线下面积分别为0.54、0.48、0.55和0.36。阅片者3与CAD之间以及阅片者2与阅片者3之间的差异具有统计学意义（P < 0.05）；CAD与阅片者1和阅片者2之间的差异无统计学意义。单次阅片的平均敏感度为50%（范围41% - 60%）；双读使敏感度提高到63%（范围56% - 67%）。当CAD阈值设定为每次CT扫描仅允许3次FP检测时，平均敏感度提高到76%（范围73% - 78%）。CAD比第二位阅片者更有效地检测出额外结节，从而补充了单个阅片者的工作；CAD - 阅片者加权kappa值显著低于阅片者 - 阅片者加权kappa值（Wilcoxon秩和检验，P < 0.05）。
当CAD阈值设定为每次CT扫描仅允许3次FP检测时，其敏感度显著高于传统双读。
