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 GR 26504, Greece.
Department of Radiology, School of Medicine, University of Athens, Athens GR 12461, Greece.
Med Phys. 2016 Mar;43(3):1428-36. doi: 10.1118/1.4942383.
Classify chronic liver disease (CLD) from ultrasound shear-wave elastography (SWE) imaging by means of a computer aided diagnosis (CAD) system.
The proposed algorithm employs an inverse mapping technique (red-green-blue to stiffness) to quantify 85 SWE images (54 healthy and 31 with CLD). Texture analysis is then applied involving the automatic calculation of 330 first and second order textural features from every transformed stiffness value map to determine functional features that characterize liver elasticity and describe liver condition for all available stages. Consequently, a stepwise regression analysis feature selection procedure is utilized toward a reduced feature subset that is fed into the support vector machines (SVMs) classification algorithm in the design of the CAD system.
With regard to the mapping procedure accuracy, the stiffness map values had an average difference of 0.01 ± 0.001 kPa compared to the quantification results derived from the color-box provided by the built-in software of the ultrasound system. Highest classification accuracy from the SVM model was 87.0% with sensitivity and specificity values of 83.3% and 89.1%, respectively. Receiver operating characteristic curves analysis gave an area under the curve value of 0.85 with [0.77-0.89] confidence interval.
The proposed CAD system employing color to stiffness mapping and classification algorithms offered superior results, comparing the already published clinical studies. It could prove to be of value to physicians improving the diagnostic accuracy of CLD and can be employed as a second opinion tool for avoiding unnecessary invasive procedures.
通过计算机辅助诊断（CAD）系统，根据超声剪切波弹性成像（SWE）对慢性肝病（CLD）进行分类。
所提出的算法采用逆映射技术（红-绿-蓝到硬度）对85幅SWE图像（54幅健康图像和31幅CLD图像）进行量化。然后应用纹理分析，从每个变换后的硬度值图自动计算330个一阶和二阶纹理特征，以确定表征肝脏弹性并描述所有可用阶段肝脏状况的功能特征。因此，在CAD系统设计中，利用逐步回归分析特征选择程序得到一个简化的特征子集，并将其输入支持向量机（SVM）分类算法。
关于映射程序的准确性，与超声系统内置软件提供的色框得出的量化结果相比，硬度图值的平均差异为0.01±0.001 kPa。SVM模型的最高分类准确率为87.0%，灵敏度和特异度值分别为83.3%和89.1%。受试者工作特征曲线分析得出曲线下面积值为0.85，置信区间为[0.77 - 0.89]。
与已发表的临床研究相比，所提出的采用颜色到硬度映射和分类算法的CAD系统提供了更优的结果。它可能对提高CLD诊断准确性的医生具有价值，并可作为避免不必要侵入性检查的二次诊断工具。
