Feng Liu1, George Boon-Bee Goh2, Dina Tiniakos3,4, Aileen Wee5, Wei-Qiang Leow6, Jing-Min Zhao7, Hui-Ying Rao1, Xiao-Xiao Wang1, Qin Wang1, Wei-Keat Wan6, Kiat-Hon Lim6, Manuel Romero-Gomez8, Salvatore Petta9, Elisabetta Bugianesi10, Chee-Kiat Tan2, Stephen A Harrison11, Quentin M Anstee3,12, Pik-Eu Jason Chang2, Lai Wei1,13,14. 1. Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Peking University People's Hospital, Beijing, China. 2. Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore. 3. Institute of Clinical and Translational Research, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom. 4. Department of Pathology, Aretaieion Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece. 5. Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore, National University Hospital, Singapore. 6. Department of Anatomical Pathology, Singapore General Hospital, Singapore. 7. Department of Pathology and Hepatology, The Fifth Medical Center, Chinese PLA General Hospital, Beijing, China. 8. Unit for the Clinical Management of Digestive Diseases, Centro para la Investigacion Biomedica en Red de Enfermedades Hepaticas y Digestivas (CIBEREHD), Institute of Biomedicine Seville (IBIS), Virgen del Rocio University Hospital, University of Seville, Seville, Spain. 9. Sezione di Gastroenterologia ed Epatologia, Dipartimento di Medicina Interna e Specialistica, DIBIMIS, Universita di Palermo, Palermo, Italy. 10. Division of Gastroenterology, Department of Medical Sciences, University of Turin, Turin, Italy. 11. Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom. 12. Newcastle NIHR Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Trust, Freeman Hospital, Newcastle upon Tyne, United Kingdom. 13. Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China. 14. Institute for Precision Medicine, Tsinghua University, Beijing, China.
Abstract
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.
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.
Authors: Amaro Taylor-Weiner; Harsha Pokkalla; Ling Han; Catherine Jia; Ryan Huss; Chuhan Chung; Hunter Elliott; Benjamin Glass; Kishalve Pethia; Oscar Carrasco-Zevallos; Chinmay Shukla; Urmila Khettry; Robert Najarian; Ross Taliano; G Mani Subramanian; Robert P Myers; Ilan Wapinski; Aditya Khosla; Murray Resnick; Michael C Montalto; Quentin M Anstee; Vincent Wai-Sun Wong; Michael Trauner; Eric J Lawitz; Stephen A Harrison; Takeshi Okanoue; Manuel Romero-Gomez; Zachary Goodman; Rohit Loomba; Andrew H Beck; Zobair M Younossi Journal: Hepatology Date: 2021-06-24 Impact factor: 17.425