TY - JOUR
T1 - qFIBS: A Novel Automated Technique for Quantitative Evaluation of Fibrosis, Inflammation, Ballooning, and Steatosis in Patients With Nonalcoholic Steatohepatitis
AU - Petta, Salvatore
AU - Anstee, Quentin M.
AU - Lim, Kiat-Hon
AU - Tan, Chee-Kiat
AU - Chang, Pik-Eu Jason
AU - Harrison, Stephen A.
AU - Wan, Wei-Keat
AU - Wee, Aileen
AU - Goh, George Boon-Bee
AU - Leow, Wei-Qiang
AU - Wang, Xiao-Xiao
AU - Liu, Feng
AU - Tiniakos, Dina
AU - Wei, Lai
AU - Lim, Kiat-Hon
AU - Wang, Qin
AU - Liu, Feng
AU - Bugianesi, Elisabetta
AU - Tiniakos, Dina
AU - Romero-Gomez, Manuel
AU - Wei, Lai
AU - Zhao, Jing-Min
AU - Rao, Hui-Ying
PY - 2019
Y1 - 2019
N2 - 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 novel second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) imaging-based tool to provide an automated quantitative assessment of histological features pertinent to NASH. 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 (AUROC) 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. Conclusion: 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
AB - 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 novel second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) imaging-based tool to provide an automated quantitative assessment of histological features pertinent to NASH. 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 (AUROC) 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. Conclusion: 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
UR - http://hdl.handle.net/10447/387182
M3 - Article
SN - 0270-9139
JO - Hepatology
JF - Hepatology
ER -