METHODS: PubMed and Scopus electronic databases were searched based on the guidelines established by PRISMA to obtain studies investigating the integration of DTI in intracranial RT/RS treatment planning. References and citations from Google Scholar were also extracted. Eligible studies were extracted for information on changes in dose distribution, treatment parameters, and outcome after DTI integration.
RESULTS: Eighteen studies were selected for inclusion with 406 patients (median study size, 19; range: 2-144). Dose distribution, with or without DTI integration, described changes of treatment parameters, and the reported outcome of treatment were compared in 12, 7, and 10 studies, respectively. Dose distributions after DTI integration improved in all studies. Delivery time or monitor unit was higher after integration. In studies with long-term follow-up (median, >12 months), neurologic deficits were significantly fewer in patients with DTI integration.
CONCLUSIONS: Integrating DTI into RT/RS treatment planning improved dose distribution, with higher treatment delivery time or monitor unit as a potential drawback. Fewer neurologic deficits were found with DTI integration.
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.