Visual assessment of [18F]flutemetamol PET images can detect early amyloid pathology and grade its extent

Lyduine E Collij; Gemma Salvadó; Mahnaz Shekari; Isadora Lopes Alves; Juhan Reimand; Alle Meije Wink; Marissa Zwan; Aida Niñerola-Baizán; Andrés Perissinotti; Philip Scheltens; Milos D Ikonomovic; Adrian P L Smith; Gill Farrar; José Luis Molinuevo; Frederik Barkhof; Christopher J Buckley; Bart N M van Berckel; Juan Domingo Gispert

doi:10.1007/s00259-020-05174-2

Published February 22, 2021 | Version v1

Journal article Open

Visual assessment of [18F]flutemetamol PET images can detect early amyloid pathology and grade its extent

1. Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
2. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain ; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
3. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
4. Alzheimer Center and department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands. ; Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia. ; Radiology Centre, North Estonia Medical Centre, Tallinn, Estonia
5. Alzheimer Center and department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands
6. Nuclear Medicine Department, Hospital Clínic Barcelona & Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
7. Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA. ; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. ; Department of Geriatric Research Education and Clinical Center, VA Pittsburgh HS, Pittsburgh, PA, USA
8. GE Healthcare, Life Sciences, Amersham, UK
9. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA Universitat Pompeu Fabra, Barcelona, Spain CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
10. Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands ; Centre for Medical Image Computing, and Queen Square Institute of Neurology, UCL, London, UK
11. Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan, 1117, Amsterdam, Netherlands. b.berckel@amsterdamumc.nl ; Department of Radiology and Nuclear Medicine, VU University Medical Center, De Boelelaan 1117, 1108 HV, Amsterdam, The Netherlands
12. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain. jdgispert@barcelonabeta.org ; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain. jdgispert@barcelonabeta.org ; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. jdgispert@barcelonabeta.org ; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain. jdgispert@barcelonabeta.org. ; Alzheimer Prevention Program, BarcelonaBeta Brain Research Center (BBRC), C/ Wellington, 30, 08005, Barcelona, Spain

Abstract:

Purpose: To investigate the sensitivity of visual read (VR) to detect early amyloid pathology and the overall utility of regional VR.

Methods: [¹⁸F]Flutemetamol PET images of 497 subjects (ALFA+ N = 352; ADC N = 145) were included. Scans were visually assessed according to product guidelines, recording the number of positive regions (0-5) and a final negative/positive classification. Scans were quantified using the standard and regional Centiloid (CL) method. The agreement between VR-based classification and published CL-based cut-offs for early (CL = 12) and established (CL = 30) pathology was determined. An optimal CL cut-off maximizing Youden's index was derived. Global and regional CL quantification was compared to VR. Finally, 28 post-mortem cases from the [¹⁸F]flutemetamol phase III trial were included to assess the percentage agreement between VR and neuropathological classification of neuritic plaque density.

Results: VR showed excellent agreement against CL = 12 (κ = .89, 95.2%) and CL = 30 (κ = .88, 95.4%) cut-offs. ROC analysis resulted in an optimal CL = 17 cut-off against VR (sensitivity = 97.9%, specificity = 97.8%). Each additional positive VR region corresponded to a clear increase in global CL. Regional VR was also associated with regional CL quantification. Compared to mCERAD_SOT-based classification (i.e., any region mCERAD_SOT > 1.5), VR was in agreement in 89.3% of cases, with 13 true negatives, 12 true positives, and 3 false positives (FP). Regional sparse-to-moderate neuritic and substantial diffuse Aβ plaque was observed in all FP cases. Regional VR was also associated with regional plaque density.

Conclusion: VR is an appropriate method for assessing early amyloid pathology and that grading the extent of visual amyloid positivity could present clinical value.

Notes

This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115952. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. The ALFA Study is funded by "la Caixa" Foundation (LCF/PR/GN17/10300004) and the Alzheimer's Association and an international anonymous charity foundation through the the TriBEKa Imaging Platform project (TriBEKa-17-519007). Additional funding has been obtained by Project RTI2018-102261-B-I00, funded by European Regional Development Fund (EDRF) / Ministry of Science and Innovation - State Research Agency (Spain). This publication solely reflects the author's view and neither IMI nor the European Union, and EFPIA are responsible for any use that may be made of the information contained herein.

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