ArticleCancers2022
Prediction of Nodal Metastasis in Lung Cancer Using Deep Learning of Endobronchial Ultrasound Images.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 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 and digital health in bronchoscopy: advances in diagnostic accuracy, real-time navigation and procedural skills training.Breathe (Sheffield, England) · 2026Review
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Molecular Analysis of EBUS-TBNA Samples for Nodal Staging in Non-Small Cell Lung Cancer.Cancers · 2026Review
- Applications of Artificial Intelligence in Endobronchial Ultrasound for Lung Cancer Diagnosis and Staging: A Scoping Review.Current oncology (Toronto, Ont.) · 2026Article
- Global trends and academic landscapes of ultrasound applications in lung cancer research: a bibliometric analysis (2000-2024).Cancer imaging : the official publication of the International Cancer Imaging Society · 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
- 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
- AI-based Prediction of Neck Metastasis in Early-stage Lip Squamous Cell Carcinoma Using Preoperative Clinical-morphologic Features.The Journal of craniofacial surgeryArticle
- Artificial Intelligence in Interventional Pulmonology.Therapeutic advances in pulmonary and critical care medicineReview
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a valid modality for nodal lung cancer staging. The sonographic features of EBUS helps determine suspicious lymph nodes (LNs). To facilitate this use of this method, machine-learning-based computer-aided diagnosis (CAD) of medical imaging has been introduced in clinical practice. This study investigated the feasibility of CAD for the prediction of nodal metastasis in lung cancer using endobronchial ultrasound images. Image data of patients who underwent EBUS-TBNA were collected from a video clip. Xception was used as a convolutional neural network to predict the nodal metastasis of lung cancer. The prediction accuracy of nodal metastasis through deep learning (DL) was evaluated using both the five-fold cross-validation and hold-out methods. Eighty percent of the collected images were used in five-fold cross-validation, and all the images were used for the hold-out method. Ninety-one patients (166 LNs) were enrolled in this study. A total of 5255 and 6444 extracted images from the video clip were analyzed using the five-fold cross-validation and hold-out methods, respectively. The prediction of LN metastasis by CAD using EBUS images showed high diagnostic accuracy with high specificity. CAD during EBUS-TBNA may help improve the diagnostic efficiency and reduce invasiveness of the procedure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.