Abstract
The diagnosis of Parkinson’s disease (PD) is challenging at all stages due to variable symptomatology, comorbidities, and mimicking conditions. Postmortem assessment remains the gold standard for a definitive diagnosis. While it is well recognized that PD manifests pathologically in the central nervous system with aggregation of α-synuclein as Lewy bodies and neurites, similar Lewy-type synucleinopathy (LTS) is additionally found in the peripheral nervous system that may be useful as an antemortem biomarker. We have previously found that detection of LTS in submandibular gland (SMG) biopsies is sensitive and specific for advanced PD; however, the sensitivity is suboptimal especially for early-stage disease. Further, visual microscopic assessment of biopsies by a neuropathologist to identify LTS is impractical for large-scale adoption. Here, we trained and validated a convolutional neural network (CNN) for detection of LTS on 283 digital whole slide images (WSI) from 95 unique SMG biopsies. A total of 8,450 LTS and 35,066 background objects were annotated following an inter-rater reliability study with Fleiss Kappa = 0.72. We used transfer learning to train a CNN model to classify image patches (151 × 151 pixels at 20× magnification) with and without the presence of LTS objects. The trained CNN model showed the following performance on image patches: sensitivity: 0.99, specificity: 0.99, precision: 0.81, accuracy: 0.99, and F-1 score: 0.89. We further tested the trained network on 1230 naïve WSI from the same cohort of research subjects comprising 42 PD patients and 14 controls. Logistic regression models trained on features engineered from the CNN predictions on the WSI resulted in sensitivity: 0.71, specificity: 0.65, precision: 0.86, accuracy: 0.69, and F-1 score: 0.76 in predicting clinical PD status, and 0.64 accuracy in predicting PD stage, outperforming expert neuropathologist LTS density scoring in terms of sensitivity but not specificity. These findings demonstrate the practical utility of a CNN detector in screening for LTS, which can translate into a computational tool to facilitate the antemortem tissue-based diagnosis of PD in clinical settings.
Highlights
Parkinson’s disease (PD), dementia with Lewy bodies, and multiple system atrophy, which are all characterized histologically by intracellular aggregates of misprocessed α-synuclein, are the most common among the spectrumSignaevsky et al Acta Neuropathologica Communications (2022) 10:21 of synucleinopathies, and are the second most common neurodegenerative conditions after Alzheimer disease (AD), with PD being the most common in this group [23]
Ground truth generation First, to ensure reproducibility in our ground truth dataset used for training the neural network, we asked whether Lewy-type synucleinopathy (LTS) objects could be annotated in a consistent fashion and a minimal interobserver variability bias
whole slide images (WSI) were randomly divided into 80% for training and validation, with 20% reserved as a test set for performance evaluation (Additional file 1: Table Systemic Synuclein Sampling Study (S4))
Summary
Parkinson’s disease (PD), dementia with Lewy bodies, and multiple system atrophy, which are all characterized histologically by intracellular aggregates of misprocessed α-synuclein, are the most common among the spectrumSignaevsky et al Acta Neuropathologica Communications (2022) 10:21 of synucleinopathies, and are the second most common neurodegenerative conditions after Alzheimer disease (AD), with PD being the most common in this group [23]. Parkinson’s disease (PD), dementia with Lewy bodies, and multiple system atrophy, which are all characterized histologically by intracellular aggregates of misprocessed α-synuclein, are the most common among the spectrum. It is widely accepted that accurate and early diagnosis of PD is needed for meaningful therapeutic approaches for disease modification [30]. The antemortem diagnosis is challenging due to a variable clinical presentation and several mimicking conditions, exemplified most commonly by dementia with Lewy bodies, multiple system atrophy, and progressive supranuclear palsy, obscuring the clinical picture, and delaying treatment [2, 44]. Many clinical trials in PD have failed to identify disease-modifying therapies [8]. Robust and accurate PD biomarkers are crucial to enable early diagnosis, test for target engagement, and serve as surrogate measures of disease in clinical trials
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