Borro Paolo, Ziola Sebastiano, Pasta Andrea, Trombini Marco, Labanca Sara, Marenco Simona, Solarna David, Pisciotta Livia, Baldissarro Isabella, Picciotto Antonino, Dellepiane Silvana
Gastroenterology Unit, Department of Internal Medicine, IRCCS Ospedale Policlinico San Martino, University of Genoa, Genoa, Italy.
Gastroenterology Unit, Department of Internal Medicine, IRCCS Ospedale Policlinico San Martino, University of Genoa, Genoa, Italy.
Ultrasound Med Biol. 2021 Apr;47(4):947-959. doi: 10.1016/j.ultrasmedbio.2020.12.015. Epub 2021 Jan 13.
The aim of this study was to identify a method for staging hepatic fibrosis using a non-invasive, rapid and inexpensive technique based on ultrasound morphologic hepatic features. A total of 215 patients with different liver diseases underwent B-mode (2-D brightness mode) ultrasonography, vibration-controlled transient elastography, 2-D shear wave elastography and measurement of the controlled attenuation parameter with transient elastography. B-Mode images of the anterior margin of the left lobe were obtained and processed with automatic Genoa Line Quantification (GLQ) software based on a neural network for staging liver fibrosis. The accuracy of GLQ was 90.6% during model training and 78.9% in 38 different patients with concordant elastometric measures. Receiver operating characteristic curve analysis of GLQ performance using vibration-controlled transient elastography as a reference yielded areas under the curves of 0.851 for F ≥ F1, 0.793 for F ≥ F2, 0.784 for F ≥ F3 and 0.789 for F ≥ F4. GLQ has the potential to be a rapid, easy-to-perform and tolerable method in the staging of liver fibrosis.
本研究的目的是确定一种基于超声肝脏形态学特征的非侵入性、快速且廉价的肝纤维化分期方法。共有215例不同肝病患者接受了B型(二维亮度模式)超声检查、振动控制瞬时弹性成像、二维剪切波弹性成像以及使用瞬时弹性成像测量控制衰减参数。获取左叶前缘的B型图像,并使用基于神经网络的自动热那亚线量化(GLQ)软件进行处理,以对肝纤维化进行分期。在模型训练期间,GLQ的准确率为90.6%,在38例具有一致弹性测量结果的不同患者中为78.9%。以振动控制瞬时弹性成像为参考,对GLQ性能进行受试者操作特征曲线分析,F≥F1时曲线下面积为0.851,F≥F2时为0.793,F≥F3时为0.784,F≥F4时为0.789。GLQ有可能成为一种快速、易于操作且可耐受的肝纤维化分期方法。