The purpose of this study was to develop an automated method for classifying liver fibrosis stage ≥F2 based on ultrasound shear wave elastography (SWE) and to assess the system's performance in comparison with a reference manual approach. The reference approach consists of manually selecting a region of interest from each of eight or more SWE images, computing the mean tissue stiffness within each of the regions of interest and computing a resulting stiffness value as the median of the means. The 527-subject database consisted of 5526 SWE images and pathologist-scored biopsies, with data collected from a single system at a single site. The automated method integrates three modules that assess SWE image quality, select a region of interest from each SWE measurement and perform machine learning-based, multi-image SWE classification for fibrosis stage ≥F2. Several classification methods were developed and tested using fivefold cross-validation with training, validation and test sets partitioned by subject. Performance metrics were area under receiver operating characteristic curve (AUROC), specificity at 95% sensitivity and number of SWE images required. The final automated method yielded an AUROC of 0.93 (95% confidence interval: 0.90–0.94) versus 0.69 (95% confidence interval: 0.65–0.72) for the reference method, 71% specificity with 95% sensitivity versus 5% and four images per decision versus eight or more. In conclusion, the automated method reported in this study significantly improved the accuracy for ≥F2 classification of SWE measurements as well as reduced the number of measurements needed, which has the potential to reduce clinical workflow.

本研究的目的是开发一种基于超声剪切波弹性成像 (SWE) 对肝纤维化阶段≥F2 进行分类的自动化方法，并与参考手动方法进行比较来评估系统的性能。参考方法包括从八个或更多 SWE 图像的每一个中手动选择一个感兴趣的区域，计算每个感兴趣区域内的平均组织刚度，并计算所得刚度值作为平均值的中值。527 个受试者的数据库由 5526 个 SWE 图像和病理学家评分的活组织检查组成，数据从单个系统的单个站点收集。该自动化方法集成了三个模块，用于评估 SWE 图像质量、从每个 SWE 测量中选择一个感兴趣的区域并执行基于机器学习的、纤维化阶段≥F2的多图像SWE分类。使用按主题划分的训练、验证和测试集的五重交叉验证开发和测试了几种分类方法。性能指标是接受者操作特征曲线下的面积 (AUROC)、95% 灵敏度下的特异性和所需的 SWE 图像数量。最终的自动化方法产生的 AUROC 为 0.93（95% 置信区间：0.90–0.94），而参考方法的 AUROC 为 0.69（95% 置信区间：0.65–0.72），特异性为 71%，灵敏度为 95%，灵敏度为 5%，每个图像为 4 张决定与八个或更多。总之，本研究报告的自动化方法显着提高了 SWE 测量≥F2 分类的准确性，并减少了所需的测量次数，