Automated Differentiation of Atypical Parkinsonian Syndromes Using Brain Iron Patterns in Susceptibility Weighted Imaging

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.

The differential diagnosis value of radiomics-based machine learning in Parkinson’s disease: a systematic review and meta-analysis

“…Following the exclusion of 121 duplicate studies, 43 studies were screened based on their titles or abstracts. Ultimately, a total of 28 articles ( Cheng et al, 2019 ; Shinde et al, 2019 ; Wu et al, 2019 ; Xiao et al, 2019 ; Cao et al, 2020 , 2021 ; Liu et al, 2020 ; Pang et al, 2020 , 2022 ; Shu et al, 2020 ; Dhinagar et al, 2021 ; Hu et al, 2021 ; Li et al, 2021 , 2022 ; Ren et al, 2021 ; Shi et al, 2021 , 2022a , 2022b ; Sun et al, 2021 , 2022 ; Tupe-Waghmare et al, 2021 ; Zhang et al, 2021 ; Ben Bashat et al, 2022 ; Guan et al, 2022 ; Kang et al, 2022 ; Kim et al, 2022 ; Shiiba et al, 2022 ; Zhao et al, 2022 ) were deemed eligible and included in this meta-analysis.…”

“…The characteristics of the studies included in this research are shown in Table 1 and Supplementary Table 4 . The original 28 studies were published between 2019 and 2022, with 27 of them from Asia ( Cheng et al, 2019 ; Shinde et al, 2019 ; Wu et al, 2019 ; Xiao et al, 2019 ; Cao et al, 2020 , 2021 ; Liu et al, 2020 ; Pang et al, 2020 , 2022 ; Shu et al, 2020 ; Hu et al, 2021 ; Li et al, 2021 , 2022 ; Ren et al, 2021 ; Shi et al, 2021 , 2022a , 2022b ; Sun et al, 2021 , 2022 ; Tupe-Waghmare et al, 2021 ; Zhang et al, 2021 ; Ben Bashat et al, 2022 ; Guan et al, 2022 ; Kang et al, 2022 ; Kim et al, 2022 ; Shiiba et al, 2022 ; Zhao et al, 2022 ) and one from North America ( Dhinagar et al, 2021 ). The study comprised a total of 6,057 participants, with 3,422 patients diagnosed with PD, 1,983 healthy controls, and 652 cases of APS (476 with MSA and 176 with PSP).…”

“…The study comprised a total of 6,057 participants, with 3,422 patients diagnosed with PD, 1,983 healthy controls, and 652 cases of APS (476 with MSA and 176 with PSP). Among these studies, 22 focused on the diagnosis of PD ( Cheng et al, 2019 ; Shinde et al, 2019 ; Wu et al, 2019 ; Xiao et al, 2019 ; Cao et al, 2020 , 2021 ; Liu et al, 2020 ; Shu et al, 2020 ; Dhinagar et al, 2021 ; Li et al, 2021 , 2022 ; Ren et al, 2021 ; Shi et al, 2021 , 2022a , 2022b ; Sun et al, 2021 , 2022 ; Zhang et al, 2021 ; Ben Bashat et al, 2022 ; Guan et al, 2022 ; Kang et al, 2022 ; Shiiba et al, 2022 ), while six studies focused on the differential diagnosis of PD and APS ( Pang et al, 2020 , 2022 ; Hu et al, 2021 ; Tupe-Waghmare et al, 2021 ; Kim et al, 2022 ; Zhao et al, 2022 ). In addition, two studies addressed the differential diagnosis of PD with or without depression ( Li et al, 2021 ; Zhang et al, 2021 ), and one study fixated on the differential diagnosis of TD and PIGD ( Sun et al, 2021 ).…”

Advanced MRI Techniques: Diagnosis and Follow-Up of Multiple Sclerosis

“…Detection of rimmed lesions supplements established measures of activity without the need for exogenous contrast agents, potentially aiding in discriminating MS from other conditions. Iron imaging could represent a suitable clinical trial outcome measure, especially for therapies targeting iron metabolism or microglia [21,75].…”

3D-CAM: a novel context-aware feature extraction framework for neurological disease classification

“… Pang et al (2020) extracted radiomics features on Susceptibility-weighted-imaging using machine learning methods for differential diagnosis of PD and MSA. Kim et al (2022) constructed a machine learning model to extract radiological features using medical images, successfully differentiating various types of Parkinsonian syndromes. Bu et al (2023) utilized different kinds of medical images to build a radiological model based on machine learning to differentiate PD from MSA.…”