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Radiomics on routine T1-weighted MRI can delineate Parkinson’s disease from multiple system atrophy and progressive supranuclear palsy



This study aimed to explore the feasibility of radiomics features extracted from T1-weighted MRI images to differentiate Parkinson’s disease (PD) from atypical parkinsonian syndromes (APS).


Radiomics features were computed from T1 images of 65 patients with PD, 61 patients with APS (31: progressive supranuclear palsy and 30: multiple system atrophy), and 75 healthy controls (HC). These features were extracted from 19 regions of interest primarily from subcortical structures, cerebellum, and brainstem. Separate random forest classifiers were applied to classify different groups based on a reduced set of most important radiomics features for each classification as determined by the random forest–based recursive feature elimination by cross-validation method.


The PD vs HC classifier illustrated an accuracy of 70%, while the PD vs APS classifier demonstrated a superior test accuracy of 92%. Moreover, a 3-way PD/MSA/PSP classifier performed with 96% accuracy. While first-order and texture-based differences like Gray Level Co-occurrence Matrix (GLCM) and Gray Level Difference Matrix for the substantia nigra pars compacta and thalamus were highly discriminative for PD vs HC, textural features mainly GLCM of the ventral diencephalon were highlighted for APS vs HC, and features extracted from the ventral diencephalon and nucleus accumbens were highlighted for the classification of PD and APS.


This study establishes the utility of radiomics to differentiate PD from APS using routine T1-weighted images. This may aid in the clinical diagnosis of PD and APS which may often be indistinguishable in early stages of disease.

Key Points

• Radiomics features were extracted from T1-weighted MRI images.

• Parkinson’s disease and atypical parkinsonian syndromes were classified at an accuracy of 92%.

• This study establishes the utility of radiomics to differentiate Parkinson’s disease and atypical parkinsonian syndromes using routine T1-weighted images.

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Age at onset


Advanced normalization tools


Atypical parkinsonian syndromes


Area under receiver operating characteristics




Dopamine transporter single-photon emission computed tomography


Fluorodeoxyglucose positron emission tomography


Gray Level Co-occurrence Matrix


Gray Level Dependence Matrix


Gray Level Run Length Matrix


Gray Level Size Zone Matrix


Gray matter


Gradient echo


Healthy controls


Magnetic resonance imaging


Multiple system atrophy


Multiple system atrophy-cerebellar type


Multiple system atrophy-parkinsonian type


Neighboring Gray Tone Difference Matrix


Parkinson’s disease


Progressive supranuclear palsy


Progressive supranuclear palsy-parkinsonism


Progressive supranuclear palsy-Richardson syndrome


Quantitative susceptibility mapping


Random forest


Random forest–based recursive feature elimination with cross-validation


Receiver operating characteristics


Regions of interest


Resting-state functional MRI


Substantia nigra pars compacta


Spectral pre-saturation inversion with recovery


Echo time


Repetition time


Unified Parkinson’s Disease Rating Scale Part III


White matter


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This study has received partial funding by the Department of Science and Technology – Science and Engineering Research Board (DST-SERB) (ECR/2016/000808) and (DST-SERB EMR/2017/004523).

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Correspondence to Madhura Ingalhalikar.

Ethics declarations


The scientific guarantor of this publication is Madhura Ingalhalikar.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects who were part of the cohort have been part of previous studies from our group:

Shinde S, Prasad S, Saboo Y, et al Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. Neuroimage Clin. 2019;22:101748.

Naduthota RM, Bharath RD, Jhunjhunwala K, et al Imaging biomarker correlates with oxidative stress in Parkinson’s disease. Neurol India. 2017;65(2):263-268.

Dash SK, Stezin A, Takalkar T, et al Abnormalities of white and gray matter in early multiple system atrophy: comparison of parkinsonian and cerebellar variants. European radiology. 2019;29(2):716-724.


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• Performed at one institution

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Tupe-Waghmare, P., Rajan, A., Prasad, S. et al. Radiomics on routine T1-weighted MRI can delineate Parkinson’s disease from multiple system atrophy and progressive supranuclear palsy. Eur Radiol 31, 8218–8227 (2021).

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  • Parkinson’s disease
  • Parkinsonism
  • Magnetic resonance imaging
  • Multiple system atrophy
  • Progressive supranuclear palsy