Zhi Xinxin, Li Jin, Chen Junxiang, Wang Lei, Xie Fangfang, Dai Wenrui, Sun Jiayuan, Xiong Hongkai
Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Front Oncol. 2021 May 31;11:673775. doi: 10.3389/fonc.2021.673775. eCollection 2021.
Endoscopic ultrasound (EBUS) strain elastography can diagnose intrathoracic benign and malignant lymph nodes (LNs) by reflecting the relative stiffness of tissues. Due to strong subjectivity, it is difficult to give full play to the diagnostic efficiency of strain elastography. This study aims to use machine learning to automatically select high-quality and stable representative images from EBUS strain elastography videos.
LNs with qualified strain elastography videos from June 2019 to November 2019 were enrolled in the training and validation sets randomly at a quantity ratio of 3:1 to train an automatic image selection model using machine learning algorithm. The strain elastography videos in December 2019 were used as the test set, from which three representative images were selected for each LN by the model. Meanwhile, three experts and three trainees selected one representative image severally for each LN on the test set. Qualitative grading score and four quantitative methods were used to evaluate images above to assess the performance of the automatic image selection model.
A total of 415 LNs were included in the training and validation sets and 91 LNs in the test set. Result of the qualitative grading score showed that there was no statistical difference between the three images selected by the machine learning model. Coefficient of variation (CV) values of the four quantitative methods in the machine learning group were all lower than the corresponding CV values in the expert and trainee groups, which demonstrated great stability of the machine learning model. Diagnostic performance analysis on the four quantitative methods showed that the diagnostic accuracies were range from 70.33% to 73.63% in the trainee group, 78.02% to 83.52% in the machine learning group, and 80.22% to 82.42% in the expert group. Moreover, there were no statistical differences in corresponding mean values of the four quantitative methods between the machine learning and expert groups (p >0.05).
The automatic image selection model established in this study can help select stable and high-quality representative images from EBUS strain elastography videos, which has great potential in the diagnosis of intrathoracic LNs.
内镜超声（EBUS）弹性成像可通过反映组织的相对硬度来诊断胸内良恶性淋巴结（LN）。由于主观性强，弹性成像的诊断效率难以充分发挥。本研究旨在利用机器学习从EBUS弹性成像视频中自动选择高质量、稳定的代表性图像。
纳入2019年6月至2019年11月有合格弹性成像视频的LN，按3:1的数量比随机分为训练集和验证集，使用机器学习算法训练自动图像选择模型。将2019年12月的弹性成像视频作为测试集，模型为每个LN选择三张代表性图像。同时，三名专家和三名实习生分别为测试集中的每个LN选择一张代表性图像。采用定性分级评分和四种定量方法对上述图像进行评估，以评价自动图像选择模型的性能。
训练集和验证集共纳入415个LN，测试集纳入91个LN。定性分级评分结果显示，机器学习模型选择的三张图像之间无统计学差异。机器学习组四种定量方法的变异系数（CV）值均低于专家组和实习生组的相应CV值，表明机器学习模型具有很高的稳定性。四种定量方法的诊断性能分析显示，实习生组的诊断准确率为70.33%至73.63%，机器学习组为78.02%至83.52%，专家组为80.22%至82.42%。此外，机器学习组和专家组四种定量方法的相应平均值无统计学差异（p>0.05）。
本研究建立的自动图像选择模型有助于从EBUS弹性成像视频中选择稳定、高质量的代表性图像，在胸内LN的诊断中具有很大潜力。
