ArticleAnnals of translational medicine2020
DeepLN: an artificial intelligence-based automated system for lung cancer screening.
Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Diagnostic performance of deep learning models in differentiating benign and malignant pulmonary nodules: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2026Article
- A Systematic Review of AI Performance in Lung Cancer Detection on CT Thorax.Healthcare (Basel, Switzerland) · 2025Review
- China Protocol for early screening, precise diagnosis, and individualized treatment of lung cancer.Signal transduction and targeted therapy · 2025Review
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Deep Learning in Thoracic Oncology: Meta-Analytical Insights into Lung Nodule Early-Detection Technologies.Cancers · 2025Review
- Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: a qualitative study.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Novel tools for early diagnosis and precision treatment based on artificial intelligence.Chinese medical journal pulmonary and critical care medicine · 2023Review
- Diagnostic study on clinical feasibility of an AI-based diagnostic system as a second reader on mobile CT images: a preliminary result.Annals of translational medicine · 2022Article
- A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer.Sensors (Basel, Switzerland) · 2021Article
- Cell-free DNA from bronchoalveolar lavage fluid (BALF): a new liquid biopsy medium for identifying lung cancer.Annals of translational medicine · 2021Article
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8 authors.
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Abstract
backgroundLung 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.
methodsBased 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.
resultsA 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%.
conclusionsIn 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.
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