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Detection of Abnormal Prion Protein by Immunohistochemistry
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Prion disease diagnosis using subject-specific imaging biomarkers within a multi-kernel Gaussian process.

Liane S Canas1, Carole H Sudre2, Enrico De Vita3

  • 1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom.

Neuroimage. Clinical
|November 18, 2019
PubMed
Summary

This study introduces a new framework to diagnose prion diseases, including Creutzfeldt-Jakob disease (CJD), by combining imaging, genetic, and demographic data. The method accurately predicts disease onset and aids in differential diagnosis for these rare neurodegenerative conditions.

Keywords:
BiomarkersDiagnosisGaussian processInherited Creutzfeldt–Jakob diseasePrion diseasesSporadic Creutzfeldt–Jakob diseaseSubjects’ stratification

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Area of Science:

  • Neuroscience and Neurology
  • Medical Imaging and Diagnostics
  • Computational Biology and Bioinformatics

Background:

  • Prion diseases, including Creutzfeldt-Jakob disease (CJD), are rare, rapidly progressing neurodegenerative disorders with heterogeneous clinical presentations.
  • Current diagnostic methods lack quantitative imaging biomarkers for early prediction or disease progression monitoring, leading to misdiagnosis with other dementias.
  • The phenotypic heterogeneity and inconsistent progression patterns of prion diseases challenge traditional neurodegenerative disease study approaches.

Purpose of the Study:

  • To develop and evaluate a tailored framework for classifying and stratifying prion disease patients based on illness severity.
  • To predict the probability of prion disease diagnosis in healthy subjects and differentiate CJD from other neurodegenerative diseases.
  • To integrate subject-specific imaging biomarkers with genetic and demographic information for enhanced diagnostic accuracy.

Main Methods:

  • Extraction of subject-specific imaging biomarkers.
  • Combination of imaging biomarkers with genetic and demographic data.
  • Application of a Gaussian Process classifier for probability calculation and disease prediction.

Main Results:

  • The framework achieved high accuracy in predicting inherited CJD (92%) and sporadic CJD (95%).
  • Disease stratification yielded an average accuracy of 85% with a recall of 59%.
  • The framework demonstrated effectiveness as a differential diagnosis tool for identifying CJD among other neurodegenerative diseases.

Conclusions:

  • A novel, multi-feature framework enables accurate diagnosis and prediction of prion disease onset.
  • The developed method shows promise for improving diagnostic accuracy and potentially aiding in disease management.
  • This approach may be applicable to other heterogeneous neurological disorders characterized by distinct imaging features.