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A discriminative event-based model for subtype diagnosis of sporadic Creutzfeldt-Jakob disease using brain MRI
Vikram Venkatraghavan1,2,3, Riccardo Pascuzzo4, Esther E Bron1
1Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
Introduction:
Sporadic Creutzfeldt-Jakob disease (sCJD) comprises multiple subtypes (MM1, MM2, MV1, MV2C, MV2K, VV1, and VV2) with distinct disease durations and spatiotemporal cascades of brain lesions. Our goal was to establish the ante mortem diagnosis of sCJD subtype, based on patient-specific estimates of the spatiotemporal cascade of lesions detected by diffusion-weighted magnetic resonance imaging (DWI).
Methods:
We included 488 patients with autopsy-confirmed diagnosis of sCJD subtype and 50 patients with exclusion of prion disease. We applied a discriminative event-based model (DEBM) to infer the spatiotemporal cascades of lesions, derived from the DWI scores of 12 brain regions assigned by three neuroradiologists. Based on the DEBM cascades and the prion protein genotype at codon 129, we developed and validated a novel algorithm for the diagnosis of the sCJD subtype.
Results:
Cascades of MM1, MM2, MV1, MV2C, and VV1 originated in the parietal cortex and, following subtype-specific orderings of propagation, went toward the striatum, thalamus, and cerebellum; conversely, VV2 and MV2K cascades showed a striatum-to-cortex propagation. The proposed algorithm achieved 76.5% balanced accuracy for the sCJD subtype diagnosis, with low rater dependency (differences in accuracy of ± 1% among neuroradiologists).
Discussion:
Ante mortem diagnosis of sCJD subtype is feasible with this novel data-driven approach, and it may be valuable for patient prognostication, stratification in targeted clinical trials, and future therapeutics.
Highlights:
Subtype diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD) is achievable with diffusion MRI. Cascades of diffusion MRI abnormalities in the brain are subtype-specific in sCJD. We proposed a diagnostic algorithm based on cascades of diffusion MRI abnormalities and demonstrated that it is accurate. Our method may aid early diagnosis, prognosis, stratification in clinical trials, and future therapeutics. The present approach is applicable to other neurodegenerative diseases, enhancing the differential diagnoses.
Insights
Diagnosing sporadic Creutzfeldt-Jakob disease (sCJD) subtypes is now possible before death using diffusion MRI. This novel approach analyzes lesion patterns to accurately identify sCJD subtypes, aiding prognosis and treatment strategies.
Area of Science:
- Neuroimaging
- Neurology
- Prion Diseases
Background:
- Sporadic Creutzfeldt-Jakob disease (sCJD) presents with diverse subtypes, each characterized by unique disease durations and lesion progression patterns.
- Accurate ante mortem diagnosis of sCJD subtypes is crucial for patient management and therapeutic development.
Purpose of the Study:
- To develop and validate an algorithm for ante mortem diagnosis of sCJD subtypes.
- To leverage diffusion-weighted magnetic resonance imaging (DWI) to analyze spatiotemporal lesion cascades for subtype differentiation.
Main Methods:
- A discriminative event-based model (DEBM) was applied to DWI data from 488 autopsy-confirmed sCJD patients.
- Lesion propagation patterns across 12 brain regions were inferred and correlated with prion protein genotype at codon 129.
- A novel diagnostic algorithm was developed and validated based on these spatiotemporal cascades.
Main Results:
- Distinct subtype-specific lesion propagation patterns were identified, with some originating in the parietal cortex and others in the striatum.
- The developed algorithm achieved a balanced accuracy of 76.5% for sCJD subtype diagnosis.
- The algorithm demonstrated low rater dependency, with accuracy variations of only ±1% among neuroradiologists.
Conclusions:
- Ante mortem diagnosis of sCJD subtypes is feasible using a data-driven approach based on DWI lesion cascades.
- This method holds potential for improving patient prognostication, stratifying patients for clinical trials, and guiding future therapeutic interventions.
- The approach may also enhance differential diagnoses in other neurodegenerative diseases.
