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Random forest analysis of midbrain hypometabolism using [18F]-FDG PET identifies Parkinson’s disease at the subject-level
Parkinson’s disease (PD) is currently diagnosed largely on the basis of expert judgement with neuroimaging serving only as a supportive tool. In a recent study, we identified a hypometabolic midbrain cluster, which includes parts of the substantia nigra, as the best differentiating metabolic feat...
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| Huvudupphovsmän: | , , , , , , , |
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| Materialtyp: | Artikel |
| Språk: | engelska |
| Publicerad: |
Philipps-Universität Marburg
2024
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| Sammanfattning: | Parkinson’s disease (PD) is currently diagnosed largely on the basis of expert
judgement with neuroimaging serving only as a supportive tool. In a recent
study, we identified a hypometabolic midbrain cluster, which includes parts
of the substantia nigra, as the best differentiating metabolic feature for PD-
patients based on group comparison of [18F]-fluorodeoxyglucose ([18F]-FDG)
PET scans. Longitudinal analyses confirmed progressive metabolic changes
in this region and, an independent study showed great potential of nigral
metabolism for diagnostic workup of parkinsonian syndromes. In this study, we
applied amachine learning approach to evaluatemidbrainmetabolismmeasured
by [18F]-FDG PET as a diagnostic marker for PD. In total, 51 mid-stage PD-
patients and 16 healthy control subjects underwent high-resolution [18F]-FDG
PET. Normalized tracer update values of the midbrain cluster identified by
between-group comparison were extracted voxel-wise from individuals’ scans.
Extracted uptake values were subjected to a random forest feature classification
algorithm. An adapted leave-one-out cross validation approach was applied
for testing robustness of the model for differentiating between patients and
controls. Performance of the model across all runs was evaluated by calculating
sensitivity, specificity and model accuracy for the validation data set and the
percentage of correctly categorized subjects for test data sets. The randomforest
feature classification of voxel-based uptake values from the midbrain cluster
identified patients in the validation data set with an average sensitivity of 0.91
(Min: 0.82, Max: 0.94). For all 67 runs, in which each of the individuals was
treated once as test data set, the test data set was correctly categorized by
our model. The applied feature importance extraction consistently identified a
subset of voxels within the midbrain cluster with highest importance across all
runs which spatially converged with the left substantia nigra. Our data suggest
midbrain metabolism measured by [18F]-FDG PET as a promising diagnostic imaging tool for PD. Given its close relationship to PD pathophysiology and very
high discriminatory accuracy, this approach could help to objectify PD diagnosis
and enable more accurate classification in relation to clinical trials, which could
also be applicable to patients with prodromal disease. |
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| Beskrivning: | Gefördert durch den Open-Access-Publikationsfonds der UB Marburg. |
| DOI: | 10.3389/fncom.2024.1328699 |