qFIBS: An Automated Technique for Quantitative Evaluation of Fibrosis, Inflammation, Ballooning, and Steatosis in Patients With Nonalcoholic Steatohepatitis

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.

Reply

“…We thank Gawrieh et al for their interest in our work and agree with them that computational methods leveraging machine learning and artificial intelligence approaches offer promise to enhance histopathological assessment of liver biopsies …”

Digital pathology: accurate technique for quantitative assessment of histological features in metabolic‐associated fatty liver disease

“…To reduce institutional bias, the histopathologic evaluation, whole‐tissue section scanning and automated quantification were centralised at a single institution. Finally, we used a relatively simple computational tool based on elementary parameters like colour and shape from sample images, avoiding complex algorithms of several tissue features that require specific devices, such as the second harmonic generation microscopy or supervised machine learning 17,43,63 . The automated nature of the computational digital image analysis provides highly reproducible data and facilitates applicability.…”

An Improved qFibrosis Algorithm for Precise Screening and Enrollment into Non-Alcoholic Steatohepatitis (NASH) Clinical Trials

“…In addition, as we recognize that response to an improvement in steatohepatitis, such as decreased ballooning degeneration, steatosis, and portal inflammation, may also indicate therapeutic efficacy, both fibrosis and disease activity should be measured endpoints, as both are markers of therapeutic efficacy [39]. A combined system, such as qFIBS, addresses that need with a single technology [14].…”

“…Second-harmonic generation (SHG) microscopy (qFibrosis) has been described as a precise and robust method for assessing fibrosis in NASH since it provides more granularity toward staging fibrosis on a continuous score rather than the traditional semi-quantitative method [13,14]. We hypothesize that qFibrosis can identify the subtle differences in F1 and F2 fibrosis patients and increase the accuracy of patient enrollment into NASH clinical trials by exploring signature qFibrosis parameters that arise because of discordant readings between in-house and central read pathologists.…”