Automatic quantification of cardinal histologic features of nonalcoholic fatty liver disease (NAFLD) may reduce human variability and allow continuous rather than semiquantitative assessment of injury. We recently developed an automated classifier that can detect and quantify macrosteatosis with greater than or equal to 95% precision and recall (sensitivity). Here, we report our early results on the classifier's performance in detecting lobular inflammation and hepatocellular ballooning. Automatic quantification of lobular inflammation and ballooning was performed on digital images of hematoxylin and eosin-stained slides of liver biopsy samples from 59 individuals with normal liver histology and varying severity of NAFLD. Two expert hepatopathologists scored liver biopsies according the nonalcoholic steatohepatitis clinical research network scoring system and provided annotations of lobular inflammation and hepatocyte ballooning on the digital images. The classifier had precision and recall of 70% and 49% for lobular inflammation, and 91% and 54% for hepatocyte ballooning. In addition, the classifier had an area under the curve of 95% for lobular inflammation and 98% for hepatocyte ballooning. The Spearman rank correlation coefficient for comparison with pathologist grades was 45.2% for lobular inflammation and 46% for hepatocyte ballooning. Our novel observations demonstrate that automatic quantification of cardinal NAFLD histologic lesions is feasible and offer promise for further development of automatic quantification as a potential aid to pathologists evaluating NAFLD biopsies in clinical practice and clinical trials.
Keywords: Digital image analysis; Fatty liver; Hepatocyte ballooning; Lobular inflammation; Machine learning; NAFLD activity score.
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