Zhang Jun, Zhu Liangru, Yao Liwen, Ding Xiangwu, Chen Di, Wu Huiling, Lu Zihua, Zhou Wei, Zhang Lihui, An Ping, Xu Bo, Tan Wei, Hu Shan, Cheng Fan, Yu Honggang
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
Department of Gastroenterology, Wuhan Union Hospital, Huazhong University of Science and Technology, Wuhan, China.
Gastrointest Endosc. 2020 Oct;92(4):874-885.e3. doi: 10.1016/j.gie.2020.04.071. Epub 2020 May 6.
EUS is considered one of the most sensitive modalities for pancreatic cancer detection, but it is highly operator-dependent and the learning curve is steep. In this study, we constructed a system named BP MASTER (pancreaticobiliary master) for EUS training and quality control.
The standard procedure of pancreatic EUS was divided into 6 stations. We developed a station classification model and a pancreas/abdominal aorta/portal confluence segmentation model with 19,486 images and 2207 images, respectively. Then, we used 1920 images and 700 images for classification and segmentation internal validation, respectively. To test station recognition we used 396 videos clips. An independent data set containing 180 images was applied for comparing the performance between models and EUS experts. Seven hundred sixty-eight images from 2 other hospitals were used for external validation. A crossover study was conducted to test the system effect on reducing difficulty in ultrasonographics interpretation among trainees.
The models achieved 94.2% accuracy in station classification and .836 dice in segmentation at internal validation. At external validation, the models achieved 82.4% accuracy in station classification and .715 dice in segmentation. For the video test, the station classification model achieved a per-frame accuracy of 86.2%. Compared with EUS experts, the models achieved 90.0% accuracy in classification and .77 and .813 dice in blood vessel and pancreas segmentation, which is comparable with that of experts. In the crossover study, trainee station recognition accuracy improved from 67.2% to 78.4% (95% confidence interval, .058-1.663; P < .01).
The BP MASTER system has the potential to play an important role in shortening the pancreatic EUS learning curve and improving EUS quality control in the future.
超声内镜检查（EUS）被认为是检测胰腺癌最敏感的方法之一，但它高度依赖操作者，且学习曲线较陡。在本研究中，我们构建了一个名为BP MASTER（胰胆大师）的系统用于EUS培训和质量控制。
胰腺EUS的标准操作程序分为6个站点。我们分别用19486幅图像和2207幅图像开发了一个站点分类模型和一个胰腺/腹主动脉/门静脉汇合处分割模型。然后，我们分别用1920幅图像和700幅图像进行分类和分割内部验证。为测试站点识别，我们使用了396个视频片段。应用一个包含180幅图像的独立数据集来比较模型与EUS专家之间的性能。来自其他2家医院的768幅图像用于外部验证。进行了一项交叉研究以测试该系统对降低受训者超声图像解读难度的效果。
在内部验证中，模型在站点分类方面的准确率达到94.2%，在分割方面的骰子系数为0.836。在外部验证中，模型在站点分类方面的准确率达到82.4%，在分割方面的骰子系数为0.715。对于视频测试，站点分类模型的每帧准确率达到86.2%。与EUS专家相比，模型在分类方面的准确率达到90.0%，在血管和胰腺分割方面的骰子系数分别为0.77和0.813，与专家相当。在交叉研究中，受训者的站点识别准确率从67.2%提高到78.4%（95%置信区间，0.058 - 1.663；P < 0.01）。
BP MASTER系统未来有可能在缩短胰腺EUS学习曲线和改善EUS质量控制方面发挥重要作用。
