IMR Press / FBL / Volume 27 / Issue 7 / DOI: 10.31083/j.fbl2707212
Open Access Original Research
Deep Learning Empowers Lung Cancer Screening Based on Mobile Low-Dose Computed Tomography in Resource-Constrained Sites
Show Less
1 Department of Respiratory and Critical Care Medicine, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, West China School of Medicine, Sichuan University, 610041 Chengdu, Sichuan, China
2 Precision Medicine Research Center, West China Hospital, Sichuan University, 610041 Chengdu, Sichuan, China
3 Machine Intelligence Laboratory, College of Computer Science, Sichuan University, 610065 Chengdu, Sichuan, China
4 Department of Respiratory Disease, Guang’An Hospital, 638001 Guangan, Sichuan, China
*Correspondence: (Weimin Li); (Zhang Yi)
These authors contributed equally.
Academic Editor: Graham Pawelec
Front. Biosci. (Landmark Ed) 2022, 27(7), 212;
Submitted: 31 December 2021 | Revised: 9 March 2022 | Accepted: 15 March 2022 | Published: 4 July 2022
(This article belongs to the Special Issue Novel Approaches to Cancer Diagnosis and Therapy)
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.

Background: Existing challenges of lung cancer screening included non-accessibility of computed tomography (CT) scanners and inter-reader variability, especially in resource-limited areas. The combination of mobile CT and deep learning technique has inspired innovations in the routine clinical practice. Methods: This study recruited participants prospectively in two rural sites of western China. A deep learning system was developed to assist clinicians to identify the nodules and evaluate the malignancy with state-of-the-art performance assessed by recall, free-response receiver operating characteristic curve (FROC), accuracy (ACC), area under the receiver operating characteristic curve (AUC). Results: This study enrolled 12,360 participants scanned by mobile CT vehicle, and detected 9511 (76.95%) patients with pulmonary nodules. Majority of participants were female (8169, 66.09%), and never-smokers (9784, 79.16%). After 1-year follow-up, 86 patients were diagnosed with lung cancer, with 80 (93.03%) of adenocarcinoma, and 73 (84.88%) at stage I. This deep learning system was developed to detect nodules (recall of 0.9507; FROC of 0.6470) and stratify the risk (ACC of 0.8696; macro-AUC of 0.8516) automatically. Conclusions: A novel model for lung cancer screening, the integration mobile CT with deep learning, was proposed. It enabled specialists to increase the accuracy and consistency of workflow and has potential to assist clinicians in detecting early-stage lung cancer effectively.

pulmonary nodules
deep learning
CT images
lung cancer screening
malignancy risk
Fig. 1.
Back to top