Related Experiment Video
Updated: May 15, 2026
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
Published on: June 9, 2018
A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging
Shalini Rajandran Nair1, Li Kuo Tan, Norlisah Mohd Ramli
1Department of Biomedical Imaging, Faculty of Medicine, University Malaya Research Imaging Centre, 50603, Kuala Lumpur, Malaysia.
Objective:
To develop a decision tree based on standard magnetic resonance imaging (MRI) and diffusion tensor imaging to differentiate multiple system atrophy (MSA) from Parkinson's disease (PD).
Methods:
3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3.
Results:
Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P < 0.05) differences between MSA and PD with mean MCP width, anteroposterior diameter of pons and mean FA MCP chosen for the decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified.
Conclusion:
Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD.
Key Points:
• Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.
Insights
A new decision tree using MRI and DTI reliably differentiates multiple system atrophy (MSA) from Parkinson
Area of Science:
- Neuroimaging
- Neurology
- Radiology
Background:
- Differentiating multiple system atrophy (MSA) from Parkinson's disease (PD) is clinically challenging.
- Accurate diagnosis is crucial for appropriate treatment and management of these neurodegenerative disorders.
- Conventional MRI and diffusion tensor imaging (DTI) offer potential biomarkers for differential diagnosis.
Purpose of the Study:
- To develop and validate a decision tree model for distinguishing MSA from PD.
- To utilize standard magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) parameters.
- To achieve high diagnostic accuracy in differentiating these two conditions.
Main Methods:
- 3-Tesla brain MRI and DTI were acquired from 26 PD and 13 MSA patients.
- Regions of interest included the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP), and cerebellum.
- Measurements included volumetric data and DTI metrics (fractional anisotropy, mean diffusivity); a three-node decision tree was constructed.
Main Results:
- Nine imaging parameters showed significant differences between MSA and PD (P < 0.05).
- The decision tree incorporated mean MCP width, pons anteroposterior diameter, and mean MCP FA.
- The decision tree achieved 92% sensitivity, 96% specificity, 92% positive predictive value, and 96% negative predictive value, correctly classifying 12 of 13 MSA patients.
Conclusions:
- A decision tree model based on MRI and DTI parameters can effectively differentiate MSA from PD.
- This imaging-based approach provides a descriptive and predictive tool for diagnosis.
- Combined conventional MRI and DTI enhance diagnostic accuracy for differentiating these parkinsonian syndromes.
