A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging

• Main Authors: Nair, S.R., Tan, L.K., Ramli, Norlisah, Lim, S.Y., Rahmat, K., Nor, H.M.
• Format: Article
• Language: English
• Published: Springer Verlag 2013
• Subjects: R Medicine
• Online Access: http://eprints.um.edu.my/11216/1/Nair-2013-A_decision_tree_for.pdf; http://eprints.um.edu.my/11216/; http://www.ncbi.nlm.nih.gov/pubmed/23300042

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). 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. 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. Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD. aEuro cent Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. aEuro cent Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. aEuro cent A decision tree is descriptive and predictive in differentiating between clinical entities. aEuro cent A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.