title: A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging.
abstract: 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.
  
  
  CONCLUSIONS
  Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD.
  
  
  CONCLUSIONS
  • 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.
authors.affiliation: Department of Biomedical Imaging, Faculty of Medicine, University Malaya Research Imaging Centre, 50603, Kuala Lumpur, Malaysia.
authors.forename: Shalini Rajandran
authors.initials: SR
authors.lastname: Nair
authors.forename: Li Kuo
authors.initials: LK
authors.lastname: Tan
authors.forename: Norlisah
authors.initials: N
authors.lastname: Mohd Ramli
authors.forename: Shen Yang
authors.initials: SY
authors.lastname: Lim
authors.forename: Kartini
authors.initials: K
authors.lastname: Rahmat
authors.forename: Hazman
authors.initials: H
authors.lastname: Mohd Nor
mesh_terms: D000368:Aged; D016880:Anisotropy; D001931:Brain Mapping / Q000379:methods; D003663:Decision Trees; D003937:Diagnosis, Differential; D056324:Diffusion Tensor Imaging / Q000379:methods; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D019578:Multiple System Atrophy / Q000175:diagnosis*; D015588:Observer Variation; D010300:Parkinson Disease / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013997:Time Factors