ArticleEuropean radiology experimental2022
Algebraic topology-based machine learning using MRI predicts outcomes in primary sclerosing cholangitis.
Article in European radiology experimental, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Artificial intelligence prognostication of liver disease using imaging.The British journal of radiology · 2026Review
- Radiomics-Based Prognostication in Primary Sclerosing Cholangitis: A Proof-of-Concept Study.Liver international : official journal of the International Association for the Study of the Liver · 2025Article
- AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.Journal of clinical medicine · 2024Review
- Computed tomography-based radiomics and body composition model for predicting hepatic decompensation.Oncotarget · 2024Article
- Article
- Deep Learning-Based Prediction of Hepatic Decompensation in Patients With Primary Sclerosing Cholangitis With Computed Tomography.Mayo Clinic proceedings. Digital health · 2024Article
- Beyond pixels: Graph filtration learning unveils new dimensions in hepatocellular carcinoma imaging.Oncotarget · 2024Article
- A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data.Scientific reports · 2024Article
- The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data.BMC gastroenterology · 2023Article
- Topological data analysis in medical imaging: current state of the art.Insights into imaging · 2023Review
Corrections and comments
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Authors and funding
21 authors at 6 institutions in 3 countries.
Funding
Abstract
backgroundPrimary 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).
methodsWe 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.
resultsWe 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).
conclusionsAlgebraic 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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