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Updated: Dec 15, 2025
Incorporation of a Survivable Liver Biopsy Procedure in Mice to Assess Non-alcoholic Steatohepatitis NASH Resolution
Published on: April 16, 2019
Computer-aided classification of hepatocellular ballooning in liver biopsies from patients with NASH using persistent
Takashi Teramoto1, Toshiya Shinohara2, Akihiro Takiyama3
1Department of Mathematics, School of Medicine, Asahikawa Medical University, 2-1-1-1 Midorigaoka-Higashi, Asahikawa 078-8510, Japan.
Background And Objective:
Hepatocellular ballooning is an important histological parameter in the diagnosis of nonalcoholic steatohepatitis (NASH), and it is considered to be a morphological pattern that indicates the severity and the progression to cirrhosis and liver-related deaths. There remains uncertainty about the pathological criteria for evaluating the spectrum of non-alcoholic fatty liver disease (NAFLD) in liver biopsies. We introduce persistence images as novel mathematical descriptors for the classification of ballooning degeneration in the pathological diagnosis.
Methods:
We implemented and tested a topological data analysis methodology combined with linear machine learning techniques and applied this to the classification of tissue images into NAFLD subtypes using Matteoni classification in liver biopsies.
Results:
Digital images of hematoxylin- and eosin-stained specimens with a pathologist's visual assessment were obtained from 79 patients who were clinically diagnosed with NAFLD. We obtained accuracy rates of more than 90% for the classification between NASH and non-NASH NAFLD groups. The highest area under the curve from the receiver operating characteristic analysis was 0.946 for the classification of NASH and NAFL2 (type 2 of Matteoni classification), when both 0- and 1-dimensional persistence images were used.
Conclusions:
Our methodology using persistent homology provides quantitative measurements of the topological features in liver biopsies of NAFLD groups with considerable accuracy.
