Letter to the Editor: Quantitative Histological Assessment in Nonalcoholic Fatty Liver Disease
Authors: Samer Gawrieh, David E. Kleiner, Naga Chalasani, Scott Vanderbeck
Journal: Hepatology (2020)

Potential conflict of interest: Dr. Gawrieh consults for TransMedics and received research grant support from Cirius, Galmed, Viking, and Zydus. Dr. Chalasani had paid consulting activities with the following companies in the last 12 months: AbbVie, Shire, NuSirt, Afimmune, Axovant, Allergan, Madrigal, Coherus, Siemens, and Genentech. He has received research support from Lilly, Galectin, Gilead, Exact Sciences, and Cumberland. To The Editor: We read with interest the article by Liu et al. describing a new algorithm‐based platform that automates the assessment of a number of parameters that correlate with key histological features of nonalcoholic fatty liver disease (NAFLD).(1) Using unstained liver biopsy slides, Liu's method utilizes second‐harmonic generation microscopy images to visualize and quantify fibrosis‐related parameters as well as two‐photon excitation fluorescence microscopy images to visualize and quantify inflammation‐, ballooning‐, and steatosis‐related parameters. We commend the researchers for their important work. We previously used machine learning, an artificial intelligence method, to automate the direct identification and quantification of steatosis, lobular inflammation, and hepatocellular ballooning in images of hematoxylin and eosin (H&E)‐stained liver biopsy slides from patients with NAFLD.(2,3) Liu et al. incorrectly referenced the performance of our method,(3) which had an area under the receiver operating characteristic curve of 0.95, not 0.45, for detecting lobular inflammation and 0.98, not 0.46, for detecting HB. Using conventional ranges for interpretation of correlation coefficients as a guide,(4) our method and Liu's have correlations in the moderate range of 0.40‐0.69 with pathologists' semiquantitative scores for lobular inflammation and ballooning using the Nonalcoholic Steatohepatitis Clinical Research Network system.(5) Although the precision of lesion detection with these methods and the ability to quantify these lesions on a continuous scale may be more critical than correlation with pathologists' semiquantitative scores, stronger correlation may increase confidence in the performance of these methods, especially when compared to expert hepatopathologists' assessments of liver biopsy. We agree with Liu et al. that further refinement and validation of the performance of these methods is necessary. Finally, although we agree there is a strong correlation for qFibrosis (r = 0.776) and qSteatosis (r = 0.802), we would like to point out that the strength of statistical significance is different from the strength of correlation when it applies to qInflammation and qBallooning. The latter two show moderate correlation with the pathologists' scores. We are curious how moderate correlations were achieved for qInflammation (r = 0.557) and qBallooning (r = 0.533). The individual parameters for these lesions, which are cited in Supporting Tables S6 and S7, show weak correlations (r < 0.20, P > 0.05 for all qInflammation model parameters, and r < 0.30 for qBallooning model parameters with 6 of 13 parameters with P > 0.05).