Department of Radiology, Centre hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada.
Department of Radiology, Radio-oncology and Nuclear Medicine, Université de Montréal and CRCHUM, 1000 rue Saint-Denis, Montréal, Québec, H2X 0C2, Canada.
Eur Radiol. 2019 May;29(5):2175-2184. doi: 10.1007/s00330-018-5915-z. Epub 2018 Dec 17.
To develop a machine learning model based on quantitative ultrasound (QUS) parameters to improve classification of steatohepatitis with shear wave elastography in rats by using histopathology scoring as the reference standard.
This study received approval from the institutional animal care committee. Sixty male Sprague-Dawley rats were either fed a standard chow or a methionine- and choline-deficient diet. Ultrasound-based radiofrequency images were recorded in vivo to generate QUS and elastography maps. Random forests classification models and a bootstrap method were used to identify the QUS parameters that improved the classification accuracy of elastography. Receiver-operating characteristic analyses were performed.
For classification of not steatohepatitis vs borderline or steatohepatitis, the area under the receiver-operating characteristic curve (AUC) increased from 0.63 for elastography alone to 0.72 for a model that combined elastography and QUS techniques (p < 0.001). For detection of liver steatosis grades 0 vs ≥ 1, ≤ 1 vs ≥ 2, ≤ 2 vs 3, respectively, the AUCs increased from 0.70, 0.65, and 0.69 to 0.78, 0.78, and 0.75 (p < 0.001). For detection of liver inflammation grades 0 vs ≥ 1, ≤ 1 vs ≥ 2, ≤ 2 vs 3, respectively, the AUCs increased from 0.58, 0.77, and 0.78 to 0.66, 0.84, and 0.87 (p < 0.001). For staging of liver fibrosis grades 0 vs ≥ 1, ≤ 1 vs ≥ 2, and ≤ 2 vs ≥ 3, respectively, the AUCs increased from 0.79, 0.92, and 0.91 to 0.85, 0.98, and 0.97 (p < 0.001).
QUS parameters improved the classification accuracy of steatohepatitis, liver steatosis, inflammation, and fibrosis compared to shear wave elastography alone.
• Quantitative ultrasound and shear wave elastography improved classification accuracy of liver steatohepatitis and its histological features (liver steatosis, inflammation, and fibrosis) compared to elastography alone. • A machine learning approach based on random forest models and incorporating local attenuation and homodyned-K tissue modeling shows promise for classification of nonalcoholic steatohepatitis. • Further research should be performed to demonstrate the applicability of this multi-parametric QUS approach in a human cohort and to validate the combinations of parameters providing the highest classification accuracy.
基于定量超声（QUS）参数开发机器学习模型，以提高使用组织病理学评分作为参考标准的大鼠脂肪性肝炎剪切波弹性成像的分类准确性。
本研究获得了机构动物护理委员会的批准。60 只雄性 Sprague-Dawley 大鼠分别喂食标准饲料或蛋氨酸和胆碱缺乏饲料。在体内记录基于超声的射频图像以生成 QUS 和弹性成像图。随机森林分类模型和自举方法用于确定改善弹性成像分类准确性的 QUS 参数。进行了接收者操作特征分析。
对于非脂肪性肝炎与边界或脂肪性肝炎的分类，接收者操作特征曲线（AUC）的面积从单独弹性成像的 0.63 增加到结合弹性成像和 QUS 技术的模型的 0.72（p < 0.001）。对于检测肝脂肪变性等级 0 与≥1、≤1 与≥2、≤2 与 3，AUC 分别从 0.70、0.65 和 0.69 增加到 0.78、0.78 和 0.75（p < 0.001）。对于检测肝炎症等级 0 与≥1、≤1 与≥2、≤2 与 3，AUC 分别从 0.58、0.77 和 0.78 增加到 0.66、0.84 和 0.87（p < 0.001）。对于肝纤维化分期 0 与≥1、≤1 与≥2 和≤2 与≥3，AUC 分别从 0.79、0.92 和 0.91 增加到 0.85、0.98 和 0.97（p < 0.001）。
与单独的剪切波弹性成像相比，QUS 参数可提高脂肪性肝炎、肝脂肪变性、炎症和纤维化的分类准确性。
与单独的弹性成像相比，定量超声和剪切波弹性成像可提高肝脂肪性肝炎及其组织学特征（肝脂肪变性、炎症和纤维化）的分类准确性。
基于随机森林模型并结合局部衰减和同调-K 组织建模的机器学习方法有望用于非酒精性脂肪性肝炎的分类。
应进一步研究以证明这种多参数 QUS 方法在人类队列中的适用性，并验证提供最高分类准确性的参数组合。
