Abstract
In recent studies, iron overload has been reported in atypical parkinsonian syndromes. The topographic patterns of iron distribution in deep brain nuclei vary by each subtype of parkinsonian syndrome, which is affected by underlying disease pathologies. In this study, we developed a novel framework that automatically analyzes the disease-specific patterns of iron accumulation using susceptibility weighted imaging (SWI). We constructed various machine learning models that can classify diseases using radiomic features extracted from SWI, representing distinctive iron distribution patterns for each disorder. Since radiomic features are sensitive to the region of interest, we used a combination of T1-weighted MRI and SWI to improve the segmentation of deep brain nuclei. Radiomics was applied to SWI from 34 patients with a parkinsonian variant of multiple system atrophy, 21 patients with cerebellar variant multiple system atrophy, 17 patients with progressive supranuclear palsy, and 56 patients with Parkinson’s disease. The machine learning classifiers that learn the radiomic features extracted from iron-reflected segmentation results produced an average area under receiver operating characteristic curve (AUC) of 0.8607 on the training data and 0.8489 on the testing data, which is superior to the conventional classifier with segmentation using only T1-weighted images. Our radiomic model based on the hybrid images is a promising tool for automatically differentiating atypical parkinsonian syndromes.
Highlights
Published: 5 March 2022In neurodegenerative disease, abnormal neuronal cells die rapidly in parts of the nervous system or the entire brain, resulting in loss of brain function, including cognitive and motor abilities
The deep gray matter (DGM) contrast is weak and the cortex contrast is clear in the T1w, while the trend is opposite for susceptibility weighted imaging (SWI)
The SVM with radial basis function (RBF) kernel that learns the radiomic features extracted from ironreflected segmentation results produced an average area under receiver operating characteristic curve (AUC) of 0.8607 in training and 0.8489 in testing
Summary
Abnormal neuronal cells die rapidly in parts of the nervous system or the entire brain, resulting in loss of brain function, including cognitive and motor abilities. Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s and is accompanied by motor symptoms such as bradykinesia, tremor, and gait disturbance, making it difficult to conduct daily activities and many nonmotor symptoms such as cognitive impairment, depression, autonomic dysfunction, and sleep disturbance. Atypical parkinsonian syndromes (APSs), comprising of progressive supranuclear palsy (PSP) and a parkinsonian variant of multiple system atrophy (MSA-P), are degenerative diseases that share similar Parkinsonism symptoms and signs with PD [1]. But show additional symptoms and different rates of functional deterioration and prognosis [2].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
