qFIBS: An Automated Technique for Quantitative Evaluation of Fibrosis, Inflammation, Ballooning, and Steatosis in Patients With Nonalcoholic Steatohepatitis
qFIBS: An Automated Technique for Quantitative Evaluation of Fibrosis, Inflammation, Ballooning, and Steatosis in Patients With Nonalcoholic Steatohepatitis
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DOI：
10.1002/hep.30986
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发表时间：
2020-05-07
期刊：
影响因子：
13.5
通讯作者：
Wei, Lai
中科院分区：
文献类型：
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作者：
Liu, Feng;Goh, George Boon-Bee;Wei, Lai
背景和目的非酒精性脂肪性肝炎（NASH）是慢性肝病的常见原因。临床试验使用NASH临床研究网络（CRN）系统进行疾病严重程度的半定量组织学评估。观察者间的差异可能会妨碍组织学评估，并且并不总是能达成诊断共识。我们评估了二次谐波产生/双光子激发荧光（SHG/TPEF）成像为基础的工具，以提供一个自动化的定量评估的组织学特征相关NASH.Approach和结果图像获得SHG/TPEF从219非酒精性脂肪性肝病（NAFLD）/NASH肝活检样本来自亚洲和欧洲的七个中心。这些用于开发和验证qFIB，这是一种量化NASH关键组织学特征的计算算法。qFIB是基于对四个主要组织病理学特征（即纤维化（qFibrosis）、炎症（qInflammation）、肝细胞气球样变（qFibrosis）和脂肪变性（qSteatosis））的选定特征参数的计算机模拟分析开发的，将每个特征视为连续变量而非分类变量。自动qFIB分析输出显示出与NASH CRN评分的每个相应组分的强相关性（P r = 0.776]、q炎症[r = 0.557]、q炎症[r = 0.533]和q脂肪变性[r = 0.802]）和高受试者工作特征曲线下面积值（q纤维化[0.870-0.951; 95%置信区间{CI}，0.787-1.000; P < 0.001]，q炎症[0.820-0.838; 95% CI，0.726-0.933; P < 0.001），q炎症[0.813-0.844; 95%CI，0.708-0.957; P < 0.001]和q脂肪变性[0.939-0.986; 95%CI，0.867-1.000; P < 0.001]），并且能够区分不同等级/阶段的组织学疾病。qFIB的性能是最好的脂肪变性和纤维化程度评估时，但执行不太好区分严重炎症和较高的气球grades.Conclusions qFIB是一种自动化的工具，准确地量化NASH组织学评估的关键组成部分。它提供了一种工具，可能有助于NASH治疗临床试验所需的肝活检评估的重现性和标准化。
Background and Aims Nonalcoholic steatohepatitis (NASH) is a common cause of chronic liver disease. Clinical trials use the NASH Clinical Research Network (CRN) system for semiquantitative histological assessment of disease severity. Interobserver variability may hamper histological assessment, and diagnostic consensus is not always achieved. We evaluate a second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) imaging-based tool to provide an automated quantitative assessment of histological features pertinent to NASH.Approach and Results Images were acquired by SHG/TPEF from 219 nonalcoholic fatty liver disease (NAFLD)/NASH liver biopsy samples from seven centers in Asia and Europe. These were used to develop and validate qFIBS, a computational algorithm that quantifies key histological features of NASH. qFIBS was developed based on in silico analysis of selected signature parameters for four cardinal histopathological features, that is, fibrosis (qFibrosis), inflammation (qInflammation), hepatocyte ballooning (qBallooning), and steatosis (qSteatosis), treating each as a continuous rather than categorical variable. Automated qFIBS analysis outputs showed strong correlation with each respective component of the NASH CRN scoring (P r = 0.776], qInflammation [r = 0.557], qBallooning [r = 0.533], and qSteatosis [r = 0.802]) and high area under the receiver operating characteristic curve values (qFibrosis [0.870-0.951; 95% confidence interval {CI}, 0.787-1.000; P < 0.001], qInflammation [0.820-0.838; 95% CI, 0.726-0.933; P < 0.001), qBallooning [0.813-0.844; 95% CI, 0.708-0.957; P < 0.001], and qSteatosis [0.939-0.986; 95% CI, 0.867-1.000; P < 0.001]) and was able to distinguish differing grades/stages of histological disease. Performance of qFIBS was best when assessing degree of steatosis and fibrosis, but performed less well when distinguishing severe inflammation and higher ballooning grades.Conclusions qFIBS is an automated tool that accurately quantifies the critical components of NASH histological assessment. It offers a tool that could potentially aid reproducibility and standardization of liver biopsy assessments required for NASH therapeutic clinical trials.