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 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.

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“…Higher order interactions can be investigated through the structure of these simplicial complexes [48]. Simplex complex algorithms have been used in primary sclerosing cholangitis prediction [73], MS classification and prediction [4], and glioblastoma multiforme classification [74].…”

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“…While we presented plasma BA signatures of CCA in PSC and showed promises for improving CCA prediction, a larger cohort is needed to validate our results. Moreover, imaging techniques such as magnetic resonance imaging/magnetic resonance cholangiopancreatography (MRI/MRCP) provide detailed images of bile ducts and surrounding tissue, and their use has been shown to predict with good accuracy PSC-related complications, such as time to hepatic decompensation and liver-related death, [ 50 – 53 ]. However, data on using MRI/MRCP to predict CCA in patients with PSC are lacking.…”

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