非酒精性脂肪性肝炎
脂肪变性
医学
纤维化
内科学
肝活检
胃肠病学
接收机工作特性
脂肪性肝炎
置信区间
非酒精性脂肪肝
肝硬化
慢性肝病
病理
活检
脂肪肝
疾病
作者
Feng Liu,George Boon‐Bee Goh,Dina Tiniakos,Aileen Wee,Wei Qiang Leow,Jingmin Zhao,Huiying Rao,Xiaoxiao Wang,Qin Wang,Wei‐Keat Wan,Kiat‐Hon Lim,Manuel Romero‐Gómez,Salvatore Petta,Elisabetta Bugianesi,Chee‐Kiat Tan,Stephen A. Harrison,Quentin M. Anstee,Pik‐Eu Chang,Lai Wei
出处
期刊:Hepatology
[Wiley]
日期:2020-05-07
卷期号:71 (6): 1953-1966
被引量:81
摘要
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 < 0.001; qFibrosis [ 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.
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