Prediction of Nodal Metastasis in Lung Cancer Using Deep Learning of Endobronchial Ultrasound Images

Yuki Ito; Takahiro Nakajima; Terunaga Inage; Takeshi Otsuka; Yuki Sata; Kazuhisa Tanaka; Yuichi Sakairi; Hidemi Suzuki; Ichiro Yoshino

doi:10.3390/cancers14143334

Prediction of Nodal Metastasis in Lung Cancer Using Deep Learning of Endobronchial Ultrasound Images

Cancers (Basel). 2022 Jul 8;14(14):3334. doi: 10.3390/cancers14143334.

Authors

Yuki Ito¹, Takahiro Nakajima², Terunaga Inage¹, Takeshi Otsuka³, Yuki Sata¹, Kazuhisa Tanaka¹, Yuichi Sakairi¹, Hidemi Suzuki¹, Ichiro Yoshino¹

Affiliations

¹ Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan.
² Department of General Thoracic Surgery, Dokkyo Medical University, Tochigi 321-0207, Japan.
³ Advanced Image Processing Technology 4, Electrical Engineering, Olympus Medical Systems Corporation, Tokyo 192-8507, Japan.

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.

Keywords: EBUS-TBNA; deep learning-based computer-aided diagnosis; echo B-mode imaging; nodal staging.

Grants and funding

This is research is a collaboration between Chiba University Graduate School of Medicine and Olympus Medical Systems Corp. (Tokyo, Japan) based on the contract. Funding support was neither provided by Olympus Medical Systems Corp. nor did Olympus Medical Systems Corp. influence the study design or interpretation of the results in this study. This work was supported by JSPS KAKENHI, grant number 21K08880 (PI: Takahiro Nakajima). There was no funding support from Olympus Medical Systems Corp., nor did they influence the study design or interpretation of the results in this study.