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Malignant thoracic lymph node classification with deep convolutional neural networks on real-time endobronchial ultrasound (EBUS) images

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dc.contributor.author김은영-
dc.contributor.author박무석-
dc.contributor.author용승현-
dc.contributor.author이상훈-
dc.contributor.author장윤수-
dc.date.accessioned2022-05-09T17:14:04Z-
dc.date.available2022-05-09T17:14:04Z-
dc.date.issued2022-01-
dc.identifier.issn2218-6751-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/188454-
dc.description.abstractBackground: Thoracic 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. Methods: Patients 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. Results: A 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. Conclusions: Deep 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.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherPioneer Bioscience Publishing Company-
dc.relation.isPartOfTRANSLATIONAL LUNG CANCER RESEARCH-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleMalignant thoracic lymph node classification with deep convolutional neural networks on real-time endobronchial ultrasound (EBUS) images-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학교실)-
dc.contributor.googleauthorSeung Hyun Yong-
dc.contributor.googleauthorSang Hoon Lee-
dc.contributor.googleauthorSang-Il Oh-
dc.contributor.googleauthorJi-Soo Keum-
dc.contributor.googleauthorKyung Nam Kim-
dc.contributor.googleauthorMoo Suk Park-
dc.contributor.googleauthorYoon Soo Chang-
dc.contributor.googleauthorEun Young Kim-
dc.identifier.doi10.21037/tlcr-21-870-
dc.contributor.localIdA00811-
dc.contributor.localIdA01457-
dc.contributor.localIdA06000-
dc.contributor.localIdA02836-
dc.contributor.localIdA03456-
dc.relation.journalcodeJ03382-
dc.identifier.eissn2226-4477-
dc.identifier.pmid35242624-
dc.subject.keywordConvolutional neural networks (CNNs)-
dc.subject.keyworddeep learning-
dc.subject.keywordendobronchial ultrasound (EBUS)-
dc.subject.keywordendobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA)-
dc.subject.keywordlung cancer-
dc.contributor.alternativeNameKim, Eun Young-
dc.contributor.affiliatedAuthor김은영-
dc.contributor.affiliatedAuthor박무석-
dc.contributor.affiliatedAuthor용승현-
dc.contributor.affiliatedAuthor이상훈-
dc.contributor.affiliatedAuthor장윤수-
dc.citation.volume11-
dc.citation.number1-
dc.citation.startPage14-
dc.citation.endPage23-
dc.identifier.bibliographicCitationTRANSLATIONAL LUNG CANCER RESEARCH, Vol.11(1) : 14-23, 2022-01-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

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