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

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.

SUBMITTER: Yong SH 

PROVIDER: S-EPMC8825650 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature