DeepLN：一种基于人工智能的肺癌筛查自动化系统。
DeepLN: an artificial intelligence-based automated system for lung cancer screening.
作者信息
Guo Jixiang, Wang Chengdi, Xu Xiuyuan, Shao Jun, Yang Lan, Gan Yuncui, Yi Zhang, Li Weimin
机构信息
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China.
Department of Respiratory and Critical Care Medicine, West China School/West China Hospital, Sichuan University, Chengdu, China.
出版信息
Ann Transl Med. 2020 Sep;8(18):1126. doi: 10.21037/atm-20-4461.
BACKGROUND
Lung cancer causes more deaths worldwide than any other cancer. For early-stage patients, low-dose computed tomography (LDCT) of the chest is considered to be an effective screening measure for reducing the risk of mortality. The accuracy and efficiency of cancer screening would be enhanced by an intelligent and automated system that meets or surpasses the diagnostic capabilities of human experts.
METHODS
Based on the artificial intelligence (AI) technique, i.e., deep neural network (DNN), we designed a framework for lung cancer screening. First, a semi-automated annotation strategy was used to label the images for training. Then, the DNN-based models for the detection of lung nodules (LNs) and benign or malignancy classification were proposed to identify lung cancer from LDCT images. Finally, the constructed DNN-based LN detection and identification system was named as DeepLN and confirmed using a large-scale dataset.
RESULTS
A dataset of multi-resolution LDCT images was constructed and annotated by a multidisciplinary group and used to train and evaluate the proposed models. The sensitivity of LN detection was 96.5% and 89.6% in a thin section subset [the free-response receiver operating characteristic (FROC) is 0.716] and a thick section subset (the FROC is 0.699), respectively. With an accuracy of 92.46%±0.20%, a specificity of 95.93%±0.47%, and a precision of 90.46%±0.93%, an ensemble result of benign or malignancy identification demonstrated a very good performance. Three retrospective clinical comparisons of the DeepLN system with human experts showed a high detection accuracy of 99.02%.
CONCLUSIONS
In this study, we presented an AI-based system with the potential to improve the performance and work efficiency of radiologists in lung cancer screening. The effectiveness of the proposed system was verified through retrospective clinical evaluation. Thus, the future application of this system is expected to help patients and society.
背景
在全球范围内，肺癌导致的死亡人数比其他任何癌症都多。对于早期患者，胸部低剂量计算机断层扫描（LDCT）被认为是降低死亡风险的有效筛查手段。一个能够达到或超越人类专家诊断能力的智能自动化系统将提高癌症筛查的准确性和效率。
方法
基于人工智能（AI）技术，即深度神经网络（DNN），我们设计了一个肺癌筛查框架。首先，采用半自动标注策略对图像进行训练标注。然后，提出了基于DNN的肺结节（LN）检测模型以及良性或恶性分类模型，用于从LDCT图像中识别肺癌。最后，将构建的基于DNN的LN检测与识别系统命名为DeepLN，并使用大规模数据集进行验证。
结果
一个多分辨率LDCT图像数据集由多学科团队构建并标注，用于训练和评估所提出的模型。在薄层子集（自由响应接收者操作特征曲线（FROC）为0.716）和厚层子集（FROC为0.699）中，LN检测的灵敏度分别为96.5%和89.6%。良性或恶性识别的综合结果显示出非常好的性能，准确率为92.46%±0.20%，特异性为95.93%±0.47%，精确率为90.46%±0.93%。DeepLN系统与人类专家进行的三项回顾性临床比较显示，检测准确率高达99.02%。
结论
在本研究中，我们提出了一个基于AI的系统，该系统有潜力提高放射科医生在肺癌筛查中的表现和工作效率。通过回顾性临床评估验证了所提系统的有效性。因此，预计该系统的未来应用将造福患者和社会。
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