Gatos Ilias, Tsantis Stavros, Spiliopoulos Stavros, Karnabatidis Dimitris, Theotokas Ioannis, Zoumpoulis Pavlos, Loupas Thanasis, Hazle John D, Kagadis George C
Department of Medical Physics, School of Medicine, University of Patras, Rion, Greece.
2nd Department of Radiology, School of Medicine, University of Athens, Athens, Greece.
Ultrasound Med Biol. 2017 Sep;43(9):1797-1810. doi: 10.1016/j.ultrasmedbio.2017.05.002. Epub 2017 Jun 19.
The purpose of the present study was to employ a computer-aided diagnosis system that classifies chronic liver disease (CLD) using ultrasound shear wave elastography (SWE) imaging, with a stiffness value-clustering and machine-learning algorithm. A clinical data set of 126 patients (56 healthy controls, 70 with CLD) was analyzed. First, an RGB-to-stiffness inverse mapping technique was employed. A five-cluster segmentation was then performed associating corresponding different-color regions with certain stiffness value ranges acquired from the SWE manufacturer-provided color bar. Subsequently, 35 features (7 for each cluster), indicative of physical characteristics existing within the SWE image, were extracted. A stepwise regression analysis toward feature reduction was used to derive a reduced feature subset that was fed into the support vector machine classification algorithm to classify CLD from healthy cases. The highest accuracy in classification of healthy to CLD subject discrimination from the support vector machine model was 87.3% with sensitivity and specificity values of 93.5% and 81.2%, respectively. Receiver operating characteristic curve analysis gave an area under the curve value of 0.87 (confidence interval: 0.77-0.92). A machine-learning algorithm that quantifies color information in terms of stiffness values from SWE images and discriminates CLD from healthy cases is introduced. New objective parameters and criteria for CLD diagnosis employing SWE images provided by the present study can be considered an important step toward color-based interpretation, and could assist radiologists' diagnostic performance on a daily basis after being installed in a PC and employed retrospectively, immediately after the examination.
本研究的目的是采用一种计算机辅助诊断系统，该系统利用超声剪切波弹性成像（SWE）成像，并结合硬度值聚类和机器学习算法对慢性肝病（CLD）进行分类。分析了126例患者的临床数据集（56例健康对照，70例CLD患者）。首先，采用了RGB到硬度的逆映射技术。然后进行五聚类分割，将相应的不同颜色区域与从SWE制造商提供的色条中获取的特定硬度值范围相关联。随后，提取了35个特征（每个聚类7个），这些特征表明了SWE图像中存在的物理特征。使用逐步回归分析进行特征约简，以得到一个约简后的特征子集，该子集被输入到支持向量机分类算法中，以区分CLD患者和健康个体。支持向量机模型对健康个体与CLD患者进行分类的最高准确率为87.3%，敏感性和特异性值分别为93.5%和81.2%。受试者工作特征曲线分析得出曲线下面积值为0.87（置信区间：0.77 - 0.92）。本文介绍了一种机器学习算法，该算法根据SWE图像的硬度值对颜色信息进行量化，并区分CLD患者和健康个体。本研究提供的利用SWE图像进行CLD诊断的新的客观参数和标准可被视为迈向基于颜色解释的重要一步，并且在安装到个人电脑中并在检查后立即进行回顾性应用后，可在日常工作中协助放射科医生的诊断工作。
