A data-driven SSM/PCA analysis approach for differential diagnosis of parkinsonism using ¹¹C-PE2I PET

Scaled Subprofile Modelling using principal component analysis (SSM/PCA) is a multivariate analysis technique primarily used in ¹⁸F-FDG PET brain studies to produce disease-specific patterns (DPs) and scalar scores aiding neurological diagnosis. SSM/PCA relies on well-characterized reference groups, posing challenges in real-world clinical datasets where diagnoses may be uncertain. A data-driven ensemble approach may offer a more robust alternative to random sampling when reference groups are unavailable.

To apply SSM/PCA to dynamic ¹¹C-PE2I-PET data for differential diagnosis of parkinsonism using a Monte Carlo cross-validation-inspired framework with ensemble prediction.

Dopamine transporter availability, expressed as the specific binding ratio (SBR) relative to cerebellar gray matter and relative cerebral blood flow (R₁) images from 47 healthy controls and 316 patients who underwent dynamic¹¹C-PE2I-PET on a Discovery MI PET/CT scanner were included. Patients had a single most probable diagnosis of Parkinson's disease (PD), dementia with Lewy bodies (DLB), or progressive supranuclear palsy (PSP) based on clinical information and the PET reading. A stratified 80/20 training/testing split was applied, repeated across 100 seeds, to generate DPs used for training ensemble classification models. Classification accuracy was assessed on the test-set.

Combining SBR and R₁ improved accuracy yielding a balanced accuracy of 90%, with SBR primarily differentiating patients from healthy controls and R₁ for differentiating between PD, DLB and PSP.

Our results highlight the potential of an ensemble-based SSM/PCA method to assist differential diagnosis of parkinsonism. Future work will focus on including additional atypical parkinsonian disorders.