MIT Lincoln Laboratory, Lexington, Massachusetts, USA; Center for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Center for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Ultrasound Med Biol. 2020 Oct;46(10):2667-2676. doi: 10.1016/j.ultrasmedbio.2020.05.016. Epub 2020 Jul 2.
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）的自动方法来对肝纤维化≥F2 期进行分类，并评估该系统与手动方法相比的性能。参考方法包括从每个 SWE 图像中手动选择至少 8 个感兴趣区域，计算每个感兴趣区域的组织硬度均值，并将均值的中位数作为最终硬度值。该 527 例研究对象数据库包含 5526 个 SWE 图像和病理学家评分活检，数据由单个系统在单个地点采集。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 测量中选择感兴趣区域，并对纤维化≥F2 期进行基于机器学习的多图像 SWE 分类。使用五重交叉验证，通过研究对象分层的训练、验证和测试集，开发和测试了几种分类方法。性能指标包括接收者操作特征曲线下的面积（AUROC）、95%灵敏度特异性和所需 SWE 图像数量。最终的自动化方法的 AUROC 为 0.93（95%置信区间：0.90-0.94），而参考方法为 0.69（95%置信区间：0.65-0.72），特异性为 71%，灵敏度为 95%，而每个决策所需的图像数量为 4 张，而非 8 张或更多。总之，本研究中报告的自动化方法显著提高了 SWE 测量的≥F2 分类的准确性，同时减少了所需的测量数量，这有可能减少临床工作流程。
