Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network
Open accessAbstract
Background: Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well-trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images. Materials and Methods: Open-source data sets and multicenter data sets have been used in this study. A three-dimensional convolutional neural network (CNN) was designed to detect pulmonary nodules and classify them into malignant or benign diseases based on pathologically and laboratory proven results. Results: The sensitivity and specificity of this well-trained model were found to be 84.4% (95% confidence interval [CI], 80.5%–88.3%) and 83.0% (95% CI, 79.5%–86.5%), respectively. Subgroup analysis of smaller nodules (<10 mm) have demonstrated remarkable sensitivity and specificity, similar to that of larger nodules (10–30 mm). Additional model validation was implemented by comparing manual assessments done by different ranks of doctors with those performed by three-dimensional CNN. The results show that the performance of the CNN model was superior to manual assessment. Conclusion: Under the companion diagnostics, the three-dimensional CNN with a deep learning algorithm may assist radiologists in the future by providing accurate and timely information for diagnosing pulmonary nodules in regular clinical practices. Implications for Practice: The three-dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment. Although it still warrants further improvement and validation in larger screening cohorts, its clinical application could definitely facilitate and assist doctors in clinical practice.
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Twenty-one respiratory studies compared algorithm performance against healthcare professionals.
“Twenty-one54,63–67,70,72,76–88 studies compared algorithm performance against healthcare professionals.”
From Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis (2021) · cites this paper as [63]
Thirteen respiratory studies validated algorithms on external data.
“13 (refs. 63–75) studies validated algorithms on external data”
From Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis (2021) · cites this paper as [63]
Only two respiratory studies used prospectively collected data.
“Only two studies62,63 used prospectively collected data”
From Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis (2021) · cites this paper as [63]
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- Authors
- Zhang, C., Sun, X., Dang, K., Li, K., Guo, X.-W., Chang, J., Yu, Z.-Q., Huang, F.-Y., Wu, Y.-S., Liang, Z., Liu, Z.-Y., Zhang, X.-G., Gao, X.-L., Huang, S.-H., Qin, J., Feng, W.-N., Zhou, T., Zhang, Y.-B., Fang, W.-J., Zhao, M.-F., Yang, X.-N., Zhou, Q., Wu, Y.-L., & Zhong, W.-Z.
- Affiliations
- Guangdong Academy of Medical Sciences (CN)
- Tencent (China) (CN)
- Tencent Youtu Lab, Shanghai, People's Republic of China
- Department of Radiology, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China
- Tsinghua University (CN)
- Department of Respiration, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China
- The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China
- Third Affiliated Hospital of Sun Yat-sen University (CN)
- First People's Hospital of Foshan, Foshan, People's Republic of China
- First People's Hospital of Foshan (CN)
- Guangzhou Chest Hospital (CN)
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, People's Republic of China
- Corresponding author
- Wu, Yi-long
- Zhong, Wen-zhao
- ORCID
- Venue
- The Oncologist
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-04-17
- Volume
- 24
- Issue
- 9
- Pages
- 1159-1165
- ISSN
- 1083-7159 (ISSN-L)
- 1549-490X
- PMC
- PMC6738288
- OpenAlex
- W2936111010
- Cited by
- 168
- References
- 23
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 32 · 2023: 32 · 2022: 35 · 2021: 20 · 2020: 11 · 2019: 6
- FWCI
- 8.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- The Oncologist (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Guangzhou Science and Technology Bureau (201704020161)
- National Key R&D Program of China (2016YFC1303800)
- National Natural Science Foundation (81872510)
- Special Fund of Public Interest by National Health and Family Control Committee (201402031)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- lung cancer, lung nodule detection, lung nodule classification, 3D convolutional neural networks, computed tomography, deep learning, nodule size, companion diagnostics, lung cancer screening
- MeSH terms
- Deep Learning, Algorithms, Female, Humans, Lung, Lung Neoplasms, Male, Middle Aged, Radiographic Image Interpretation, Computer-Assisted, Retrospective Studies, ROC Curve, Tomography, X-Ray Computed, Databases, Factual, Neural Networks, Computer
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