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Algebraic Topology-Based Machine Learning Using MRI Predicts Outcomes in Primary Sclerosing Cholangitis
(2022)
Yashbir Singh, William A. Jons, John E. Eaton, Mette Vesterhus, Tom Karlsen, Ida Bjoerk, Andreas Abildgaard, Kristin Kaasen Jorgensen, Folseraas Trine, Derek Little, Aliya F. Gulamhusein, Kosta Petrovic, Anne Negard, Gian Marco Conte, Joseph D. Sobek, Jaidip Jagtap, Sudhakar K. Venkatesh, Gregory J. Gores, Nicholas F. LaRusso, Konstantinos N. Lazaridis, Bradley J. Erickson
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 classifcation to predict who developed early hepatic decompensation within one year
after their baseline MRI.
Results: We reviewed 590 patients; 298 were excluded due to poor image quality or inadequate liver coverage, leaving 292 potentially eligible subjects, of which 169 subjects were included in the study. We trained our model using
contrast-enhanced delayed phase T1-weighted images on a single center derivation cohort consisting of 54 patients
(hepatic decompensation, n = 21; no hepatic decompensation, n = 33) and a multicenter independent validation
cohort of 115 individuals (hepatic decompensation, n = 31; no hepatic decompensation, n = 84). When our model
was applied in the independent validation cohort, it remained predictive of early hepatic decompensation (area
under the receiver operating characteristic curve = 0.84).
Conclusions: Algebraic topology-based ML is a methodological approach that can predict outcomes in patients
with PSC and has the potential for application in other chronic liver diseases