title: A novel computer-assisted image analysis of [123I]β-CIT SPECT images improves the diagnostic accuracy of parkinsonian disorders.
abstract: OBJECTIVE
  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%.
  
  
  CONCLUSIONS
  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.affiliation: Department of Medical Statistics, Informatics and Health Economics, Innsbruck Medical University, Schöpfstrasse 41/2, A-6020, Innsbruck, Austria. georg.goebel@i-med.ac.at
authors.forename: Georg
authors.initials: G
authors.lastname: Goebel
authors.forename: Klaus
authors.initials: K
authors.lastname: Seppi
authors.forename: Eveline
authors.initials: E
authors.lastname: Donnemiller
authors.forename: Boris
authors.initials: B
authors.lastname: Warwitz
authors.forename: Gregor K
authors.initials: GK
authors.lastname: Wenning
authors.forename: Irene
authors.initials: I
authors.lastname: Virgolini
authors.forename: Werner
authors.initials: W
authors.lastname: Poewe
authors.forename: Christoph
authors.initials: C
authors.lastname: Scherfler
mesh_terms: D000368:Aged; D000465:Algorithms; D000704:Analysis of Variance; D001921:Brain / Q000000981:diagnostic imaging / Q000378:metabolism; D016022:Case-Control Studies; D003042:Cocaine / Q000031:analogs & derivatives*; D050483:Dopamine Plasma Membrane Transport Proteins / Q000378:metabolism; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D019578:Multiple System Atrophy / Q000000981:diagnostic imaging / Q000378:metabolism; D010300:Parkinson Disease / Q000000981:diagnostic imaging / Q000378:metabolism; D020734:Parkinsonian Disorders / Q000000981:diagnostic imaging* / Q000378:metabolism; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013494:Supranuclear Palsy, Progressive / Q000000981:diagnostic imaging / Q000378:metabolism; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*
chemical_list: D050483:Dopamine Plasma Membrane Transport Proteins; C069723:2beta-carbomethoxy-3beta-(4-iodophenyl)tropane; D003042:Cocaine