Katharina A Schindlbeck1, Deepak K Gupta2,3, Chris C Tang1, Sarah A O'Shea2,4, Kathleen L Poston5, Yoon Young Choi1, Vijay Dhawan1, Jean-Paul Vonsattel6, Stanley Fahn2, David Eidelberg7. 1. Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA. 2. Division of Movement Disorders, Columbia University Medical Center, New York, NY, USA. 3. Larner College of Medicine, University of Vermont Medical Center, Burlington, VT, USA. 4. Department of Neurology, Boston University School of Medicine, Boston University, Boston, MA, USA. 5. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA. 6. Division of Neuropathology, Columbia University Medical Center, New York, NY, USA. 7. Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA. deidelbe@northwell.edu.
Abstract
PURPOSE: Up to 25% of patients diagnosed as idiopathic Parkinson's disease (IPD) have an atypical parkinsonian syndrome (APS). We had previously validated an automated image-based algorithm to discriminate between IPD, multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). While the algorithm was accurate with respect to the final clinical diagnosis after long-term expert follow-up, its relationship to the initial referral diagnosis and to the neuropathological gold standard is not known. METHODS: Patients with an uncertain diagnosis of parkinsonism were referred for 18F-fluorodeoxyglucose (FDG) PET to classify patients as IPD or as APS based on the automated algorithm. Patients were followed by a movement disorder specialist and subsequently underwent neuropathological examination. The image-based classification was compared to the neuropathological diagnosis in 15 patients with parkinsonism. RESULTS: At the time of referral to PET, the clinical impression was only 66.7% accurate. The algorithm correctly identified 80% of the cases as IPD or APS (p = 0.02) and 87.5% of the APS cases as MSA or PSP (p = 0.03). The final clinical diagnosis was 93.3% accurate (p < 0.001), but needed several years of expert follow-up. CONCLUSION: The image-based classifications agreed well with autopsy and can help to improve diagnostic accuracy during the period of clinical uncertainty.
PURPOSE: Up to 25% of patients diagnosed as idiopathic Parkinson's disease (IPD) have an atypical parkinsonian syndrome (APS). We had previously validated an automated image-based algorithm to discriminate between IPD, multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). While the algorithm was accurate with respect to the final clinical diagnosis after long-term expert follow-up, its relationship to the initial referral diagnosis and to the neuropathological gold standard is not known. METHODS: Patients with an uncertain diagnosis of parkinsonism were referred for 18F-fluorodeoxyglucose (FDG) PET to classify patients as IPD or as APS based on the automated algorithm. Patients were followed by a movement disorder specialist and subsequently underwent neuropathological examination. The image-based classification was compared to the neuropathological diagnosis in 15 patients with parkinsonism. RESULTS: At the time of referral to PET, the clinical impression was only 66.7% accurate. The algorithm correctly identified 80% of the cases as IPD or APS (p = 0.02) and 87.5% of the APS cases as MSA or PSP (p = 0.03). The final clinical diagnosis was 93.3% accurate (p < 0.001), but needed several years of expert follow-up. CONCLUSION: The image-based classifications agreed well with autopsy and can help to improve diagnostic accuracy during the period of clinical uncertainty.
Authors: J A Obeso; M Stamelou; C G Goetz; W Poewe; A E Lang; D Weintraub; D Burn; G M Halliday; E Bezard; S Przedborski; S Lehericy; D J Brooks; J C Rothwell; M Hallett; M R DeLong; C Marras; C M Tanner; G W Ross; J W Langston; C Klein; V Bonifati; J Jankovic; A M Lozano; G Deuschl; H Bergman; E Tolosa; M Rodriguez-Violante; S Fahn; R B Postuma; D Berg; K Marek; D G Standaert; D J Surmeier; C W Olanow; J H Kordower; P Calabresi; A H V Schapira; A J Stoessl Journal: Mov Disord Date: 2017-09 Impact factor: 10.338
Authors: Martin Niethammer; Chris C Tang; An Vo; Nha Nguyen; Phoebe Spetsieris; Vijay Dhawan; Yilong Ma; Michael Small; Andrew Feigin; Matthew J During; Michael G Kaplitt; David Eidelberg Journal: Sci Transl Med Date: 2018-11-28 Impact factor: 17.956
Authors: J J Hauw; S E Daniel; D Dickson; D S Horoupian; K Jellinger; P L Lantos; A McKee; M Tabaton; I Litvan Journal: Neurology Date: 1994-11 Impact factor: 9.910
Authors: Laura K Teune; Remco J Renken; Deborah Mudali; Bauke M De Jong; Rudi A Dierckx; Jos B T M Roerdink; Klaus L Leenders Journal: Mov Disord Date: 2013-03-11 Impact factor: 10.338
Authors: Tomaž Rus; Petra Tomše; Luka Jensterle; Marko Grmek; Zvezdan Pirtošek; David Eidelberg; Chris Tang; Maja Trošt Journal: Eur J Nucl Med Mol Imaging Date: 2020-04-27 Impact factor: 9.236
Authors: Charles H Adler; Thomas G Beach; Joseph G Hentz; Holly A Shill; John N Caviness; Erika Driver-Dunckley; Marwan N Sabbagh; Lucia I Sue; Sandra A Jacobson; Christine M Belden; Brittany N Dugger Journal: Neurology Date: 2014-06-27 Impact factor: 9.910
Authors: Elon D Wallert; Elsmarieke van de Giessen; Remco J J Knol; Martijn Beudel; Rob M A de Bie; Jan Booij Journal: J Nucl Med Date: 2022-06 Impact factor: 11.082