Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Acad Radiol. 2021 May;28(5):647-654. doi: 10.1016/j.acra.2020.03.015. Epub 2020 Apr 15.
To evaluate the spatial patterns of missed lung nodules in a real-life routine screening environment.
In a screening institute, 4,822 consecutive adults underwent chest CT, and each image set was independently interpreted by two radiologists in three steps: (1) independently interpreted without computer-assisted detection (CAD) software, (2) independently referred to the CAD results, (3) determined by the consensus of the two radiologists. The locations of nodules and the detection performance data were semi-automatically collected using a CAD server integrated into the reporting system. Fisher's exact test was employed for evaluating findings in different lung divisions. Probability maps were drawn to illustrate the spatial distribution of radiologists' missed nodules.
Radiologists significantly tended to miss lung nodules in the bilateral hilar divisions (p < 0.01). Some radiologists had their own spatial pattern of missed lung nodules.
Radiologists tend to miss lung nodules present in the hilar regions significantly more often than in the rest of the lung.
评估在真实常规筛查环境下肺部结节漏诊的空间分布模式。
在一家筛查机构中，连续对 4822 名成年人进行了胸部 CT 检查，每个图像集由两名放射科医生分三步独立进行解读：（1）独立解读，不使用计算机辅助检测（CAD）软件；（2）参考 CAD 结果独立解读；（3）由两名放射科医生达成共识决定。使用集成在报告系统中的 CAD 服务器半自动采集结节的位置和检测性能数据。采用 Fisher 精确检验评估不同肺区的检查结果。绘制概率图以说明放射科医生漏诊结节的空间分布。
放射科医生明显倾向于漏诊双侧肺门区的结节（p < 0.01）。一些放射科医生有其自己的肺部结节漏诊的空间分布模式。
放射科医生明显更倾向于漏诊肺门区域的肺部结节，而不是其他区域的肺部结节。
