Ervik Øyvind, Rødde Mia, Hofstad Erlend Fagertun, Tveten Ingrid, Langø Thomas, Leira Håkon O, Amundsen Tore, Sorger Hanne
Clinic of Medicine, Nord-Trøndelag Hospital Trust, Levanger Hospital, 7601 Levanger, Norway.
Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7030 Trondheim, Norway.
J Imaging. 2025 Jan 5;11(1):10. doi: 10.3390/jimaging11010010.
Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a cornerstone in minimally invasive thoracic lymph node sampling. In lung cancer staging, precise assessment of lymph node position is crucial for clinical decision-making. This study aimed to demonstrate a new deep learning method to classify thoracic lymph nodes based on their anatomical location using EBUS images. Bronchoscopists labeled lymph node stations in real-time according to the Mountain Dressler nomenclature. EBUS images were then used to train and test a deep neural network (DNN) model, with intraoperative labels as ground truth. In total, 28,134 EBUS images were acquired from 56 patients. The model achieved an overall classification accuracy of 59.5 ± 5.2%. The highest precision, sensitivity, and F1 score were observed in station 4L, 77.6 ± 13.1%, 77.6 ± 15.4%, and 77.6 ± 15.4%, respectively. The lowest precision, sensitivity, and F1 score were observed in station 10L. The average processing and prediction time for a sequence of ten images was 0.65 ± 0.04 s, demonstrating the feasibility of real-time applications. In conclusion, the new DNN-based model could be used to classify lymph node stations from EBUS images. The method performance was promising with a potential for clinical use.
支气管内超声引导下经支气管针吸活检术（EBUS-TBNA）是微创胸部淋巴结采样的基石。在肺癌分期中，精确评估淋巴结位置对于临床决策至关重要。本研究旨在展示一种新的深度学习方法，用于基于EBUS图像根据解剖位置对胸部淋巴结进行分类。支气管镜检查人员根据Mountain Dressler命名法实时标记淋巴结站。然后使用EBUS图像训练和测试深度神经网络（DNN）模型，将术中标记作为真实值。总共从56例患者中获取了28134张EBUS图像。该模型的总体分类准确率为59.5±5.2%。在4L站观察到最高的精度、灵敏度和F1分数，分别为77.6±13.1%、77.6±15.4%和77.6±15.4%。在10L站观察到最低的精度、灵敏度和F1分数。十张图像序列的平均处理和预测时间为0.65±0.04秒，证明了实时应用的可行性。总之，新的基于DNN的模型可用于从EBUS图像中对淋巴结站进行分类。该方法性能良好，具有临床应用潜力。
