Algebraic topology-based machine learning using MRI predicts outcomes in primary sclerosing cholangitis.

ABSTRACT:

Background

Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that can lead to cirrhosis and hepatic decompensation. However, predicting future outcomes in patients with PSC is challenging. Our aim was to extract magnetic resonance imaging (MRI) features that predict the development of hepatic decompensation by applying algebraic topology-based machine learning (ML).

Methods

We conducted a retrospective multicenter study among adults with large duct PSC who underwent MRI. A topological data analysis-inspired nonlinear framework was used to predict the risk of hepatic decompensation, which was motivated by algebraic topology theory-based ML. The topological representations (persistence images) were employed as input for classification to predict who d

SUBMITTER: Singh Y 

PROVIDER: S-EPMC9672219 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature