Ito Yuki, Nakajima Takahiro, Inage Terunaga, Otsuka Takeshi, Sata Yuki, Tanaka Kazuhisa, Sakairi Yuichi, Suzuki Hidemi, Yoshino Ichiro
Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan.
Department of General Thoracic Surgery, Dokkyo Medical University, Tochigi 321-0207, Japan.
Cancers (Basel). 2022 Jul 8;14(14):3334. doi: 10.3390/cancers14143334.
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.
支气管内超声引导下经支气管针吸活检术（EBUS-TBNA）是一种有效的肺癌淋巴结分期方法。EBUS的超声特征有助于确定可疑淋巴结（LN）。为便于该方法的应用，基于机器学习的医学影像计算机辅助诊断（CAD）已引入临床实践。本研究探讨了利用支气管内超声图像通过CAD预测肺癌淋巴结转移的可行性。从视频片段中收集接受EBUS-TBNA患者的图像数据。使用Xception作为卷积神经网络来预测肺癌的淋巴结转移。通过深度学习（DL）预测淋巴结转移的准确性采用五折交叉验证和留出法进行评估。五折交叉验证中使用80%的收集图像，留出法则使用所有图像。本研究纳入了91例患者（166个LN）。分别使用五折交叉验证和留出法对从视频片段中提取的5255张和6444张图像进行了分析。利用EBUS图像通过CAD预测LN转移显示出高诊断准确性和高特异性。EBUS-TBNA期间的CAD可能有助于提高诊断效率并降低该操作的侵入性。
