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John Seibyl

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YNICL Journal 2017 Journal Article

Optimized classification of 18F-Florbetaben PET scans as positive and negative using an SUVR quantitative approach and comparison to visual assessment

  • Santiago Bullich
  • John Seibyl
  • Ana M. Catafau
  • Aleksandar Jovalekic
  • Norman Koglin
  • Henryk Barthel
  • Osama Sabri
  • Susan De Santi

Introduction Standardized uptake value ratios (SUVRs) calculated from cerebral cortical areas can be used to categorize 18F-Florbetaben (FBB) PET scans by applying appropriate cutoffs. The objective of this work was first to generate FBB SUVR cutoffs using visual assessment (VA) as standard of truth (SoT) for a number of reference regions (RR) (cerebellar gray matter (GCER), whole cerebellum (WCER), pons (PONS), and subcortical white matter (SWM)). Secondly, to validate the FBB PET scan categorization performed by SUVR cutoffs against the categorization made by post-mortem histopathological confirmation of the Aβ presence. Finally, to evaluate the added value of SUVR cutoff categorization to VA. Methods SUVR cutoffs were generated for each RR using FBB scans from 143 subjects who were visually assessed by 3 readers. SUVR cutoffs were validated in 78 end-of life subjects using VA from 8 independent blinded readers (3 expert readers and 5 non-expert readers) and histopathological confirmation of the presence of neuritic beta-amyloid plaques as SoT. Finally, the number of correctly or incorrectly classified scans according to pathology results using VA and SUVR cutoffs was compared. Results Composite SUVR cutoffs generated were 1. 43 (GCER), 0. 96 (WCER), 0. 78 (PONS) and 0. 71 (SWM). Accuracy values were high and consistent across RR (range 83–94% for histopathology, and 85–94% for VA). SUVR cutoff performed similarly as VA but did not improve VA classification of FBB scans read either by expert readers or the majority read but provided higher accuracy than some non-expert readers. Conclusion The accurate scan classification obtained in this study supports the use of VA as SoT to generate site-specific SUVR cutoffs. For an elderly end of life population, VA and SUVR cutoff categorization perform similarly in classifying FBB scans as Aβ-positive or Aβ-negative. These results emphasize the additional contribution that SUVR cutoff classification may have compared with VA performed by non-expert readers.

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YNIMG Journal 2015 Journal Article

Improved longitudinal [18F]-AV45 amyloid PET by white matter reference and VOI-based partial volume effect correction

  • Matthias Brendel
  • Marcus Högenauer
  • Andreas Delker
  • Julia Sauerbeck
  • Peter Bartenstein
  • John Seibyl
  • Axel Rominger

Amyloid positron-emission-tomography (PET) offers an important research and diagnostic tool for investigating Alzheimer's disease (AD). The majority of amyloid PET studies have used the cerebellum as a reference region, and clinical studies have not accounted for atrophy-based partial volume effects (PVE). Longitudinal studies using cerebellum as reference tissue have revealed only small mean increases and high inter-subject variability in amyloid binding. We aimed to test the effects of different reference regions and PVE-correction (PVEC) on the discriminatory power and longitudinal performance of amyloid PET. We analyzed [18F]-AV45 PET and T1-weighted MRI data of 962 subjects at baseline and two-year follow-up data of 258 subjects. Cortical composite volume-of-interest (VOI) values (COMP) for tracer uptake were generated using either full brain atlas VOIs, gray matter segmented VOIs or gray matter segmented VOIs after VOI-based PVEC. Standard-uptake-value ratios (SUVR) were calculated by scaling the COMP values to uptake in cerebellum (SUVRCBL), brainstem (SUVRBST) or white matter (SUVRWM). Mean SUV, SUVR, and changes after PVEC were compared at baseline between diagnostic groups of healthy controls (HC; N=316), mild cognitive impairment (MCI; N=483) and AD (N=163). Receiver operating characteristics (ROC) were calculated for the discriminations between HC, MCI and AD, and expressed as area under the curve (AUC). Finally, the longitudinal [18F]-AV45-PET data were used to analyze the impact of quantitation procedures on apparent changes in amyloid load over time. Reference region SUV was most constant between diagnosis groups for the white matter. PVEC led to decreases of COMP-SUV in HC (−18%) and MCI (−10%), but increases in AD (+7%). Highest AUCs were found when using PVEC with white matter scaling for the contrast between HC/AD (0. 907) or with brainstem scaling for the contrast between HC/MCI (0. 658). Longitudinal increases were greatest in all diagnosis groups with application of PVEC, and inter-subject variability was lowest for the white matter reference. Thus, discriminatory power of [18F]-AV45-PET was improved by use of a VOI-based PVEC and white matter or brainstem rather than cerebellum reference region. Detection of longitudinal amyloid increases was optimized with PVEC and white matter reference tissue.

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