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

Yashbir Singh1, William A Jons1,2, John E Eaton3

  • 1Radiology, Mayo Clinic, Rochester, MN, USA.

European Radiology Experimental
|November 17, 2022
PubMed
Summary

Machine learning using algebraic topology can predict hepatic decompensation in primary sclerosing cholangitis (PSC) patients. This approach identifies high-risk individuals early using MRI, aiding in better patient management for this chronic liver disease.

Keywords:
AlgorithmCholangitis (Sclerosing)Liver cirrhosisMachine learningMagnetic resonance imaging

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Area of Science:

  • Medical imaging analysis
  • Machine learning applications in hepatology
  • Topological data analysis

Background:

  • Primary sclerosing cholangitis (PSC) is a chronic liver disease with unpredictable outcomes.
  • Predicting hepatic decompensation in PSC patients remains a clinical challenge.
  • Magnetic resonance imaging (MRI) offers potential for prognostic assessment.

Purpose of the Study:

  • To develop a machine learning model utilizing algebraic topology.
  • To extract predictive MRI features for hepatic decompensation in PSC.
  • To enable early identification of patients at high risk for liver failure.

Main Methods:

  • Retrospective multicenter study of adult large duct PSC patients with MRI.
  • Application of a topological data analysis-inspired nonlinear framework.
  • Training and validation of a machine learning model using persistence images from contrast-enhanced T1-weighted MRI.

Main Results:

  • The model was trained on a derivation cohort and validated on an independent cohort (n=115).
  • In the validation cohort, the model achieved an area under the receiver operating characteristic curve of 0.84 for predicting early hepatic decompensation.
  • The algebraic topology-based machine learning approach demonstrated predictive capability for clinical outcomes.

Conclusions:

  • Algebraic topology-based machine learning is a viable method for predicting outcomes in PSC.
  • This approach shows promise for predicting hepatic decompensation and other outcomes in chronic liver diseases.
  • Integrating advanced ML techniques with medical imaging can improve prognostic accuracy in liver disease management.