DeepLN: an artificial intelligence-based automated system for lung cancer screening

Jixiang Guo; Chengdi Wang; Xiuyuan Xu; Jun Shao; Lan Yang; Yuncui Gan; Zhang Yi; Weimin Li

doi:10.21037/atm-20-4461

DeepLN: an artificial intelligence-based automated system for lung cancer screening

Ann Transl Med. 2020 Sep;8(18):1126. doi: 10.21037/atm-20-4461.

Authors

Jixiang Guo¹, Chengdi Wang², Xiuyuan Xu¹, Jun Shao², Lan Yang², Yuncui Gan², Zhang Yi¹, Weimin Li²

Affiliations

¹ Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China.
² Department of Respiratory and Critical Care Medicine, West China School/West China Hospital, Sichuan University, Chengdu, China.

Abstract

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.

Keywords: Deep neural networks (DNNs); lung cancer screening; lung nodule (LN) detection; malignancy identification.