Ebba Gløersen Müller1,2, Caroline Stokke3,4, Henning Langen Stokmo1,2, Trine Holt Edwin5,6, Anne-Brita Knapskog6, Mona-Elisabeth Revheim1,2. 1. Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway. 2. Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. 3. Division of Radiology and Nuclear Medicine, Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway. 4. Department of Physics, University of Oslo, Oslo, Norway. 5. Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway. 6. Department of Geriatric Medicine, The memory clinic, Oslo University Hospital, Oslo, Norway.
Abstract
BACKGROUND: 18F-flutemetamol positron emission tomography (PET) is used to assess cortical amyloid-β burden in patients with cognitive impairment to support a clinical diagnosis. Visual classification is the most widely used method in clinical practice although semi-quantification is beneficial to obtain an objective and continuous measure of the Aβ burden. The aims were: first to evaluate the correspondence between standardized uptake value ratios (SUVRs) from three different software, Centiloids and visual classification, second to estimate thresholds for supporting visual classification and last to assess differences in semi-quantitative measures between clinical diagnoses. METHODS: This observational study included 195 patients with cognitive impairment who underwent 18F-flutemetamol PET. PET images were semi-quantified with SyngoVia, CortexID suite, and PMOD. Receiver operating characteristics curves were used to compare visual classification with composite SUVR normalized to pons (SUVRpons) and cerebellar cortex (SUVRcer), and Centiloids. We explored correlations and differences between semi-quantitative measures as well as differences in SUVR between two clinical diagnosis groups: Alzheimer's disease-group and non-Alzheimer's disease-group. RESULTS: PET images from 191 patients were semi-quantified with SyngoVia and CortexID and 86 PET-magnetic resonance imaging pairs with PMOD. All receiver operating characteristics curves showed a high area under the curve (>0.98). Thresholds for a visually positive PET was for SUVRcer: 1.87 (SyngoVia) and 1.64 (CortexID) and for SUVRpons: 0.54 (SyngoVia) and 0.55 (CortexID). The threshold on the Centiloid scale was 39.6 Centiloids. All semi-quantitative measures showed a very high correlation between different software and normalization methods. Composite SUVRcer was significantly different between SyngoVia and PMOD, SyngoVia and CortexID but not between PMOD and CortexID. Composite SUVRpons were significantly different between all three software. There were significant differences in the mean rank of SUVRpons, SUVRcer, and Centiloid between Alzheimer's disease-group and non-Alzheimer's disease-group. CONCLUSIONS: SUVR from different software performed equally well in discriminating visually positive and negative 18F-Flutemetamol PET images. Thresholds should be considered software-specific and cautiously be applied across software without preceding validation to categorize scans as positive or negative. SUVR and Centiloid may be used alongside a thorough clinical evaluation to support a clinical diagnosis. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: 18F-flutemetamol positron emission tomography (PET) is used to assess cortical amyloid-β burden in patients with cognitive impairment to support a clinical diagnosis. Visual classification is the most widely used method in clinical practice although semi-quantification is beneficial to obtain an objective and continuous measure of the Aβ burden. The aims were: first to evaluate the correspondence between standardized uptake value ratios (SUVRs) from three different software, Centiloids and visual classification, second to estimate thresholds for supporting visual classification and last to assess differences in semi-quantitative measures between clinical diagnoses. METHODS: This observational study included 195 patients with cognitive impairment who underwent 18F-flutemetamol PET. PET images were semi-quantified with SyngoVia, CortexID suite, and PMOD. Receiver operating characteristics curves were used to compare visual classification with composite SUVR normalized to pons (SUVRpons) and cerebellar cortex (SUVRcer), and Centiloids. We explored correlations and differences between semi-quantitative measures as well as differences in SUVR between two clinical diagnosis groups: Alzheimer's disease-group and non-Alzheimer's disease-group. RESULTS: PET images from 191 patients were semi-quantified with SyngoVia and CortexID and 86 PET-magnetic resonance imaging pairs with PMOD. All receiver operating characteristics curves showed a high area under the curve (>0.98). Thresholds for a visually positive PET was for SUVRcer: 1.87 (SyngoVia) and 1.64 (CortexID) and for SUVRpons: 0.54 (SyngoVia) and 0.55 (CortexID). The threshold on the Centiloid scale was 39.6 Centiloids. All semi-quantitative measures showed a very high correlation between different software and normalization methods. Composite SUVRcer was significantly different between SyngoVia and PMOD, SyngoVia and CortexID but not between PMOD and CortexID. Composite SUVRpons were significantly different between all three software. There were significant differences in the mean rank of SUVRpons, SUVRcer, and Centiloid between Alzheimer's disease-group and non-Alzheimer's disease-group. CONCLUSIONS: SUVR from different software performed equally well in discriminating visually positive and negative 18F-Flutemetamol PET images. Thresholds should be considered software-specific and cautiously be applied across software without preceding validation to categorize scans as positive or negative. SUVR and Centiloid may be used alongside a thorough clinical evaluation to support a clinical diagnosis. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Authors: Marilyn S Albert; Steven T DeKosky; Dennis Dickson; Bruno Dubois; Howard H Feldman; Nick C Fox; Anthony Gamst; David M Holtzman; William J Jagust; Ronald C Petersen; Peter J Snyder; Maria C Carrillo; Bill Thies; Creighton H Phelps Journal: Alzheimers Dement Date: 2011-04-21 Impact factor: 21.566
Authors: M L Gorno-Tempini; A E Hillis; S Weintraub; A Kertesz; M Mendez; S F Cappa; J M Ogar; J D Rohrer; S Black; B F Boeve; F Manes; N F Dronkers; R Vandenberghe; K Rascovsky; K Patterson; B L Miller; D S Knopman; J R Hodges; M M Mesulam; M Grossman Journal: Neurology Date: 2011-02-16 Impact factor: 9.910
Authors: Vincent Doré; Santiago Bullich; Christopher C Rowe; Pierrick Bourgeat; Salamata Konate; Osama Sabri; Andrew W Stephens; Henryk Barthel; Jurgen Fripp; Colin L Masters; Ludger Dinkelborg; Olivier Salvado; Victor L Villemagne; Susan De Santi Journal: Alzheimers Dement Date: 2019-05-14 Impact factor: 21.566
Authors: Michael Navitsky; Abhinay D Joshi; Ian Kennedy; William E Klunk; Christopher C Rowe; Dean F Wong; Michael J Pontecorvo; Mark A Mintun; Michael D Devous Journal: Alzheimers Dement Date: 2018-07-11 Impact factor: 21.566
Authors: Katya Rascovsky; John R Hodges; David Knopman; Mario F Mendez; Joel H Kramer; John Neuhaus; John C van Swieten; Harro Seelaar; Elise G P Dopper; Chiadi U Onyike; Argye E Hillis; Keith A Josephs; Bradley F Boeve; Andrew Kertesz; William W Seeley; Katherine P Rankin; Julene K Johnson; Maria-Luisa Gorno-Tempini; Howard Rosen; Caroline E Prioleau-Latham; Albert Lee; Christopher M Kipps; Patricia Lillo; Olivier Piguet; Jonathan D Rohrer; Martin N Rossor; Jason D Warren; Nick C Fox; Douglas Galasko; David P Salmon; Sandra E Black; Marsel Mesulam; Sandra Weintraub; Brad C Dickerson; Janine Diehl-Schmid; Florence Pasquier; Vincent Deramecourt; Florence Lebert; Yolande Pijnenburg; Tiffany W Chow; Facundo Manes; Jordan Grafman; Stefano F Cappa; Morris Freedman; Murray Grossman; Bruce L Miller Journal: Brain Date: 2011-08-02 Impact factor: 13.501
Authors: A Chincarini; E Peira; S Morbelli; M Pardini; M Bauckneht; J Arbizu; M Castelo-Branco; K A Büsing; A de Mendonça; M Didic; M Dottorini; S Engelborghs; C Ferrarese; G B Frisoni; V Garibotto; E Guedj; L Hausner; J Hugon; J Verhaeghe; P Mecocci; M Musarra; M Queneau; M Riverol; I Santana; U P Guerra; F Nobili Journal: Neuroimage Clin Date: 2019-05-04 Impact factor: 4.881
Authors: Bernard J Hanseeuw; Vincent Malotaux; Laurence Dricot; Lisa Quenon; Yves Sznajer; Jiri Cerman; John L Woodard; Christopher Buckley; Gill Farrar; Adrian Ivanoiu; Renaud Lhommel Journal: Eur J Nucl Med Mol Imaging Date: 2020-06-29 Impact factor: 9.236