ArticleTranslational lung cancer research2022
Malignant thoracic lymph node classification with deep convolutional neural networks on real-time endobronchial ultrasound (EBUS) images.
Article in Translational lung cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis.BMC pulmonary medicine · 2025Pooled it
- Artificial intelligence in bronchoscopy: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Artificial intelligence and digital health in bronchoscopy: advances in diagnostic accuracy, real-time navigation and procedural skills training.Breathe (Sheffield, England) · 2026Review
- Review
- Applications of Artificial Intelligence in Endobronchial Ultrasound for Lung Cancer Diagnosis and Staging: A Scoping Review.Current oncology (Toronto, Ont.) · 2026Article
- Artificial Intelligence in Pediatric Bronchoscopy: Current Evidence and Future Perspectives.Pediatric pulmonology · 2026Review
- Explainability of a Deep Learning Model for Mediastinal Lymph Node Station Classification in Endobronchial Ultrasound (EBUS).Bioengineering (Basel, Switzerland) · 2026Article
- Deep learning-based classification of pleural malignancy using medical thoracoscopic images.Translational lung cancer research · 2025Article
- Review
- Deep learning for detection and diagnosis of intrathoracic lymphadenopathy from endobronchial ultrasound multimodal videos: A multi-center study.Cell reports. Medicine · 2025Article
- A New Deep Learning-Based Method for Automated Identification of Thoracic Lymph Node Stations in Endobronchial Ultrasound (EBUS): A Proof-of-Concept Study.Journal of imaging · 2025Article
- Evaluation of deep sedation effects and respiratory aspects of remimazolam besylate in elderly patients undergoing fiberoptic bronchoscopy.Frontiers in medicine · 2025Article
- Deep learning-based prediction of nodal metastasis in lung cancer using endobronchial ultrasound.JTCVS techniques · 2024Article
- Automatic Segmentation of Mediastinal Lymph Nodes and Blood Vessels in Endobronchial Ultrasound (EBUS) Images Using Deep Learning.Journal of imaging · 2024Article
- Diagnostic value of endobronchial ultrasound elastography in differentiating between benign and malignant hilar and mediastinal lymph nodes: a retrospective study.Quantitative imaging in medicine and surgery · 2023Article
- Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy.BMC medicine · 2022Article
- Science progress distinguishing different types of airway stents under bronchoscopy by artificial intelligence.Science progressArticle
- Artificial Intelligence in Interventional Pulmonology.Therapeutic advances in pulmonary and critical care medicineReview
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThoracic lymph node (LN) evaluation is essential for the accurate diagnosis of lung cancer and deciding the appropriate course of treatment. Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is considered a standard method for mediastinal nodal staging. This study aims to build a deep convolutional neural network (CNN) for the automatic classification of metastatic malignancies involving thoracic LN, using EBUS-TBNA.
methodsPatients who underwent EBUS-TBNAs to assess the presence of malignancy in mediastinal LNs during a ten-month period at Severance Hospital, Seoul, Republic of Korea, were included in the study. Corresponding LN ultrasound images, pathology reports, demographic data, and clinical history were collected and analyzed.
resultsA total of 2,394 endobronchial ultrasound (EBUS) images of 1,459 benign LNs from 193 patients, and 935 malignant LNs from 177 patients, were collected. We employed the visual geometry group (VGG)-16 network to classify malignant LNs using only traditional cross-entropy for classification loss. The sensitivity, specificity, and accuracy of predicting malignancy were 69.7%, 74.3%, and 72.0%, respectively, and the overall area under the curve (AUC) was 0.782. We applied the new loss function to train the network and, using the modified VGG-16, the AUC improved to a value of 0.8. The sensitivity, specificity, and accuracy improved to 72.7%, 79.0%, and 75.8%, respectively. In addition, the proposed network can process 63 images per second on a single mainstream graphics processing unit (GPU) device, making it suitable for real-time analysis of EBUS images.
conclusionsDeep CNNs can effectively classify malignant LNs from EBUS images. Selecting LNs that require biopsy using real-time EBUS image analysis with deep learning is expected to shorten the EBUS-TBNA procedure time, increase lung cancer nodal staging accuracy, and improve patient safety.
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