Chen Jingwen, Cao Rong, Jiao Shengyin, Dong Yunpeng, Wang Zilong, Zhu Hua, Luo Qian, Zhang Lei, Wang Han, Yin Xiaorui
Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of R&D, VoxelCloud, Shanghai, China.
Quant Imaging Med Surg. 2023 Oct 1;13(10):6929-6941. doi: 10.21037/qims-22-1297. Epub 2023 Sep 18.
Computer-aided diagnosis (CAD) systems can help reduce radiologists' workload. This study assessed the value of a CAD system for the detection of lung nodules on chest computed tomography (CT) images.
The study retrospectively analyzed the CT images of patients who underwent routine health checkups between August 2019 and November 2019 at 3 hospitals in China. All images were first assessed by 2 radiologists manually in a blinded manner, which was followed by assessment with the CAD system. The location and classification of the lung nodules were determined. The final diagnosis was made by a panel of experts, including 2 associate chief radiologists and 1 chief radiologist at the radiology department. The sensitivity for nodule detection and false-positive nodules per case were calculated.
A total of 1,002 CT images were included in the study, and the process was completed for 999 images. The sensitivity of the CAD system and manual detection was 90.19% and 49.88% (P<0.001), respectively. Similar sensitivity was observed between manual detection and the CAD system in lung nodules >15 mm (P=0.08). The false-positive nodules per case for the CAD system were 0.30±0.84 and those for manual detection were 0.24±0.68 (P=0.12). The sensitivity of the CAD system was higher than that of the radiologists, but the increase in the false-positive rate was only slight.
In addition to reducing the workload for medical professionals, a CAD system developed using a deep-learning model was highly effective and accurate in detecting lung nodules and did not demonstrate a meaningfully higher the false-positive rate.
计算机辅助诊断（CAD）系统有助于减轻放射科医生的工作量。本研究评估了一种CAD系统在胸部计算机断层扫描（CT）图像上检测肺结节的价值。
本研究回顾性分析了2019年8月至2019年11月在中国3家医院接受常规健康检查的患者的CT图像。所有图像首先由2名放射科医生以盲法进行人工评估，然后使用CAD系统进行评估。确定肺结节的位置和分类。最终诊断由一个专家小组做出，包括放射科的2名副主任医师和1名主任医师。计算结节检测的敏感性和每例的假阳性结节数。
本研究共纳入1002幅CT图像，999幅图像完成了评估过程。CAD系统和人工检测的敏感性分别为90.19%和49.88%（P<0.001）。在直径>15mm的肺结节中，人工检测和CAD系统的敏感性相似（P=0.08）。CAD系统每例的假阳性结节数为0.30±0.84，人工检测为0.24±0.68（P=0.12）。CAD系统的敏感性高于放射科医生，但假阳性率的增加仅轻微。
使用深度学习模型开发的CAD系统除了可以减轻医学专业人员的工作量外，在检测肺结节方面高效且准确，并且没有显示出明显更高的假阳性率。
