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

Seung Hyun Yong; Sang Hoon Lee; Sang-Il Oh; Ji-Soo Keum; Kyung Nam Kim; Moo Suk Park; Yoon Soo Chang; Eun Young Kim

doi:10.21037/tlcr-21-870

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

Transl Lung Cancer Res. 2022 Jan;11(1):14-23. doi: 10.21037/tlcr-21-870.

Authors

Seung Hyun Yong¹, Sang Hoon Lee¹, Sang-Il Oh², Ji-Soo Keum², Kyung Nam Kim², Moo Suk Park¹, Yoon Soo Chang¹, Eun Young Kim¹

Affiliations

¹ Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
² Waycen Inc., Seoul, Republic of Korea.

Abstract

Background: 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.

Keywords: Convolutional neural networks (CNNs); deep learning; endobronchial ultrasound (EBUS); endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA); lung cancer.