Brain scans using positron emission tomography (PET) can identify with high accuracy which form of Parkinsonism a patient has. Such early diagnosis is essential to ensure patients receive the correct treatment, and do not receive ineffective treatments owing to misdiagnosis. The findings are reported in an Article Online First and in the February edition of the Lancet Neurology, written by Dr David Eidelberg, Center for Neurosciences,The Feinstein Institute for Medical Research, Manhasset, NY, USA, and colleagues.
Idiopathic Parkinson's disease can present with symptoms similar to those of multiple system atrophy or progressive supranuclear palsy. In this study, the investigators aimed to assess whether metabolic brain imaging combined with pattern analysis could accurately discriminate patients with different forms of Parkinsonism.
The study assessed 167 patients from the New York area who were recruited between 1998 and 2006, each of whom had parkinsonian features but uncertain clinical diagnosis. The patients underwent PET at The Feinstein Institute for Medical Research. The research team developed an automated image-based classification procedure to differentiate individual patients with idiopathic Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy. For each patient, the likelihood of having each of the three diseases was calculated, and a classification was made according to probability measurements. After imaging, patients were assessed by movement disorders specialists (who were unaware of the PET results) for a mean of 2.6 years before a final clinical diagnosis was made. The accuracy of the initial image-based classification was assessed by comparison with the final clinical diagnosis.
The researchers found that image-based classification for idiopathic Parkinson's disease had 84% sensitivity*, 97% specificity**, 98% positive predictive value (PPV)***, and 82% negative predictive value (NPV)****. Imaging classifications were also accurate for multiple system atrophy (85% sensitivity, 96% specificity, 97% PPV, and 83% NPV) and progressive supranuclear palsy (88% sensitivity, 94% specificity, 91% PPV, and 92% NPV). Dr Eidelberg says1: "The excellent specificity and PPV of the imaging classification makes this test suitable for diagnostic use rather than as a screening tool."
As well as the reasons above, the authors point out that early correct diagnosis is essential to ensure that patients with the correct diagnosis are enrolled in drug trials for potentially disease-modifying drugs for the various parkinsonian disorders. They also hope to extend their work to be able to differentiate other forms of parkinsonism.
They say: "Automated image-based classification has high specificity in distinguishing between parkinsonian disorders and could help in selecting treatment for early stage patients and identifying participants for clinical trials."
They add: "Blinded, prospective imaging studies—ideally involving multiple centres, a larger validation group, repeat imaging, and more extensive post-mortem confirmation—are needed to establish the accuracy of this pattern-based categorisation procedure."
In an accompanying Reflection and Reaction comment, Professor Angelo Antonini, IRCCS San Camillo, Venice and Parkinson Institute, Milan, Italy, says: "The clinical and research relevance of these findings should not be underestimated. Neuroprotective and disease-modifying drug research is intensifying and results mostly rely on accurate early diagnosis."
He concludes: "Although imaging might be cost effective for early diagnosis, I expect that these procedures will find their natural application in the identification of suitable candidates for drug trials or complex surgical procedures (eg, deep brain stimulation, stem-cell transplantation, or foetal tissue transplantation). However, additional blinded, prospective, multicentre studies will first be needed to confirm the accuracy of this pattern-based categorisation procedure."
Dr David Eidelberg, Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA. T) +1 516 562 2498E) david1@nshs.edu
Professor Angelo Antonini, IRCCS San Camillo, Venice and Parkinson Institute, Milan, Italy. T) +39 3483037170 E) angelo3000@yahoo.com
For full Article and Reflection and Reaction comment, see: http://press.thelancet.com/tlneidelberg.pdf
Notes to editors: *Sensitivity: Sensitivity (measures the proportion of actual positives which are correctly identified as such (e.g. the percentage of sick people who are identified as having the condition).
**Specificity: Specificity measures the proportion of negatives which are correctly identified.
***PPV: The proportion of patients with positive test results who are correctly diagnosed.
Journal
The Lancet Neurology