PURPOSE: The purpose of this study was to develop an observer-independent algorithm for the correct classification of dopamine transporter SPECT images as Parkinson's disease (PD), multiple system atrophy parkinson variant (MSA-P), progressive supranuclear palsy (PSP) or normal. METHODS: A total of 60 subjects with clinically probable PD (n = 15), MSA-P (n = 15) and PSP (n = 15), and 15 age-matched healthy volunteers, were studied with the dopamine transporter ligand [(123)I]β-CIT. Parametric images of the specific-to-nondisplaceable equilibrium partition coefficient (BP(ND)) were generated. Following a voxel-wise ANOVA, cut-off values were calculated from the voxel values of the resulting six post-hoc t-test maps. The percentages of the volume of an individual BP(ND) image remaining below and above the cut-off values were determined. The higher percentage of image volume from all six cut-off matrices was used to classify an individual's image. For validation, the algorithm was compared to a conventional region of interest analysis. RESULTS: The predictive diagnostic accuracy of the algorithm in the correct assignment of a [(123)I]β-CIT SPECT image was 83.3% and increased to 93.3% on merging the MSA-P and PSP groups. In contrast the multinomial logistic regression of mean region of interest values of the caudate, putamen and midbrain revealed a diagnostic accuracy of 71.7%. CONCLUSION: In contrast to a rater-driven approach, this novel method was superior in classifying [(123)I]β-CIT-SPECT images as one of four diagnostic entities. In combination with the investigator-driven visual assessment of SPECT images, this clinical decision support tool would help to improve the diagnostic yield of [(123)I]β-CIT SPECT in patients presenting with parkinsonism at their initial visit.
PURPOSE: The purpose of this study was to develop an observer-independent algorithm for the correct classification of dopamine transporter SPECT images as Parkinson's disease (PD), multiple system atrophy parkinson variant (MSA-P), progressive supranuclear palsy (PSP) or normal. METHODS: A total of 60 subjects with clinically probable PD (n = 15), MSA-P (n = 15) and PSP (n = 15), and 15 age-matched healthy volunteers, were studied with the dopamine transporter ligand [(123)I]β-CIT. Parametric images of the specific-to-nondisplaceable equilibrium partition coefficient (BP(ND)) were generated. Following a voxel-wise ANOVA, cut-off values were calculated from the voxel values of the resulting six post-hoc t-test maps. The percentages of the volume of an individual BP(ND) image remaining below and above the cut-off values were determined. The higher percentage of image volume from all six cut-off matrices was used to classify an individual's image. For validation, the algorithm was compared to a conventional region of interest analysis. RESULTS: The predictive diagnostic accuracy of the algorithm in the correct assignment of a [(123)I]β-CIT SPECT image was 83.3% and increased to 93.3% on merging the MSA-P and PSP groups. In contrast the multinomial logistic regression of mean region of interest values of the caudate, putamen and midbrain revealed a diagnostic accuracy of 71.7%. CONCLUSION: In contrast to a rater-driven approach, this novel method was superior in classifying [(123)I]β-CIT-SPECT images as one of four diagnostic entities. In combination with the investigator-driven visual assessment of SPECT images, this clinical decision support tool would help to improve the diagnostic yield of [(123)I]β-CIT SPECT in patients presenting with parkinsonism at their initial visit.
Authors: Robert B Innis; Vincent J Cunningham; Jacques Delforge; Masahiro Fujita; Albert Gjedde; Roger N Gunn; James Holden; Sylvain Houle; Sung-Cheng Huang; Masanori Ichise; Hidehiro Iida; Hiroshi Ito; Yuichi Kimura; Robert A Koeppe; Gitte M Knudsen; Juhani Knuuti; Adriaan A Lammertsma; Marc Laruelle; Jean Logan; Ralph Paul Maguire; Mark A Mintun; Evan D Morris; Ramin Parsey; Julie C Price; Mark Slifstein; Vesna Sossi; Tetsuya Suhara; John R Votaw; Dean F Wong; Richard E Carson Journal: J Cereb Blood Flow Metab Date: 2007-05-09 Impact factor: 6.200
Authors: Klaus Seppi; Christoph Scherfler; Eveline Donnemiller; Irene Virgolini; Michael F H Schocke; Georg Goebel; Katherina J Mair; Sylvia Boesch; Christian Brenneis; Gregor K Wenning; Werner Poewe Journal: Arch Neurol Date: 2006-08
Authors: I Huertas-Fernández; F J García-Gómez; D García-Solís; S Benítez-Rivero; V A Marín-Oyaga; S Jesús; M T Cáceres-Redondo; J A Lojo; J F Martín-Rodríguez; F Carrillo; P Mir Journal: Eur J Nucl Med Mol Imaging Date: 2014-08-14 Impact factor: 9.236
Authors: Jennifer L Whitwell; Günter U Höglinger; Angelo Antonini; Yvette Bordelon; Adam L Boxer; Carlo Colosimo; Thilo van Eimeren; Lawrence I Golbe; Jan Kassubek; Carolin Kurz; Irene Litvan; Alexander Pantelyat; Gil Rabinovici; Gesine Respondek; Axel Rominger; James B Rowe; Maria Stamelou; Keith A Josephs Journal: Mov Disord Date: 2017-05-13 Impact factor: 10.338
Authors: Michael Nocker; Klaus Seppi; Sylvia Boesch; Eveline Donnemiller; Irene Virgolini; Gregor K Wenning; Werner Poewe; Christoph Scherfler Journal: Mov Disord Clin Pract Date: 2016-11-02