Hiroshi Matsuda1,2,3, Kyoji Okita4, Yumiko Motoi5, Toshiki Mizuno6, Manabu Ikeda7, Nobuo Sanjo8, Koji Murakami9, Taiki Kambe10, Toshiki Takayama11, Kei Yamada12, Takashi Suehiro7, Keiko Matsunaga13, Takanori Yokota8, Ukihide Tateishi14, Yoko Shigemoto15, Yukio Kimura15, Emiko Chiba15, Takahiro Kawashima16, Yui Tomo16, Hisateru Tachimori16, Yuichi Kimura17, Noriko Sato15. 1. Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1, Ogawa-higashi, Kodaira, Tokyo, 187-8551, Japan. matsudah@ncnp.go.jp. 2. Department of Biofunctional Imaging, Fukushima Medical University, 1 Hikariga-oka, Fukushima City, Fukushima, 960-1295, Japan. matsudah@ncnp.go.jp. 3. Drug Discovery and Cyclotron Research Center, Southern Tohoku Research Institute for Neuroscience, 7-61-2 Yatsuyamada, Koriyama, Fukushima, 963-8052, Japan. matsudah@ncnp.go.jp. 4. Integrative Bain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawa-higashi, Kodaira, Tokyo, 187-8551, Japan. 5. Department of Diagnosis, Prevention, and Treatment of Dementia, Juntendo University Graduate School of Medicine, 3-1-3 Hongo, Bunkyo-ku, Tokyo, 113-8431, Japan. 6. Department of Neurology, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kamigyo Ward, Kyoto, 602-8566, Japan. 7. Department of Psychiatry, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan. 8. Department of Neurology and Neurological Science, Tokyo Medical and Dental University Graduate School of Medical and Dental Sciences, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan. 9. Department of Radiology, Juntendo University Graduate School of Medicine, 3-1-3 Hongo, Bunkyo-ku, Tokyo, 113-8431, Japan. 10. Department of Neurology, Juntendo University Graduate School of Medicine, 3-1-3 Hongo, Bunkyo-ku, Tokyo, 113-8431, Japan. 11. Department of Psychiatry, Juntendo University Graduate School of Medicine, 3-1-3 Hongo, Bunkyo-ku, Tokyo, 113-8431, Japan. 12. Department of Radiology, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kamigyo Ward, Kyoto, 602-8566, Japan. 13. Department of Molecular Imaging in Medicine, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan. 14. Department of Diagnostic Radiology, Tokyo Medical and Dental University Graduate School of Medical and Dental Sciences, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan. 15. Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1, Ogawa-higashi, Kodaira, Tokyo, 187-8551, Japan. 16. Department of Clinical Data Science, Clinical Research & Education Promotion Division, National Center of Neurology and Psychiatry, 4-1-1, Ogawa-higashi, Kodaira, Tokyo, 187-8551, Japan. 17. Faculty of Informatics, Cyber Informatics Research Institute, Kindai University、3-4-1, Kowakae, Higashiosaka, Osaka, 577-8502, Japan.
Abstract
OBJECTIVE: Amyloid positron emission tomography (PET) can reliably detect senile plaques and fluorinated ligands are approved for clinical use. However, the clinical impact of amyloid PET imaging is still under investigation. The aim of this study was to evaluate the diagnostic impact and clinical utility in patient management of amyloid PET using 18F-florbetapir in patients with cognitive impairment and suspected Alzheimer's disease (AD). We also aimed to determine the cutoffs for amyloid positivity for quantitative measures by investigating the agreement between quantitative and visual assessments. METHODS: Ninety-nine patients suspected of having AD underwent 18F-florbetapir PET at five institutions. Site-specialized physicians provided a diagnosis of AD or non-AD with a percentage estimate of their confidence and their plan for patient management in terms of medication, prescription dosage, additional diagnostic tests, and care planning both before and after receiving the amyloid imaging results. A PET image for each patient was visually assessed and dichotomously rated as either amyloid-positive or amyloid-negative by four board-certified nuclear medicine physicians. The PET images were also quantitatively analyzed using the standardized uptake value ratio (SUVR) and Centiloid (CL) scale. RESULTS: Visual interpretation obtained 48 positive and 51 negative PET scans. The amyloid PET results changed the AD and non-AD diagnosis in 39 of 99 patients (39.3%). The change rates of 26 of the 54 patients (48.1%) with a pre-scan AD diagnosis were significantly higher than those of 13 of the 45 patients with a pre-scan non-AD diagnosis (χ2 = 5.334, p = 0.0209). Amyloid PET results also resulted in at least one change to the patient management plan in 42 patients (42%), mainly medication (20 patients, 20%) and care planning (25 patients, 25%). Receiver-operating characteristic analysis determined the best agreement of the quantitative assessments and visual interpretation of PET scans to have an area under the curve of 0.993 at an SUVR of 1.19 and CL of 25.9. CONCLUSION: Amyloid PET using 18F-florbetapir PET had a substantial clinical impact on AD and non-AD diagnosis and on patient management by enhancing diagnostic confidence. In addition, the quantitative measures may improve the visual interpretation of amyloid positivity.
OBJECTIVE: Amyloid positron emission tomography (PET) can reliably detect senile plaques and fluorinated ligands are approved for clinical use. However, the clinical impact of amyloid PET imaging is still under investigation. The aim of this study was to evaluate the diagnostic impact and clinical utility in patient management of amyloid PET using 18F-florbetapir in patients with cognitive impairment and suspected Alzheimer's disease (AD). We also aimed to determine the cutoffs for amyloid positivity for quantitative measures by investigating the agreement between quantitative and visual assessments. METHODS: Ninety-nine patients suspected of having AD underwent 18F-florbetapir PET at five institutions. Site-specialized physicians provided a diagnosis of AD or non-AD with a percentage estimate of their confidence and their plan for patient management in terms of medication, prescription dosage, additional diagnostic tests, and care planning both before and after receiving the amyloid imaging results. A PET image for each patient was visually assessed and dichotomously rated as either amyloid-positive or amyloid-negative by four board-certified nuclear medicine physicians. The PET images were also quantitatively analyzed using the standardized uptake value ratio (SUVR) and Centiloid (CL) scale. RESULTS: Visual interpretation obtained 48 positive and 51 negative PET scans. The amyloid PET results changed the AD and non-AD diagnosis in 39 of 99 patients (39.3%). The change rates of 26 of the 54 patients (48.1%) with a pre-scan AD diagnosis were significantly higher than those of 13 of the 45 patients with a pre-scan non-AD diagnosis (χ2 = 5.334, p = 0.0209). Amyloid PET results also resulted in at least one change to the patient management plan in 42 patients (42%), mainly medication (20 patients, 20%) and care planning (25 patients, 25%). Receiver-operating characteristic analysis determined the best agreement of the quantitative assessments and visual interpretation of PET scans to have an area under the curve of 0.993 at an SUVR of 1.19 and CL of 25.9. CONCLUSION: Amyloid PET using 18F-florbetapir PET had a substantial clinical impact on AD and non-AD diagnosis and on patient management by enhancing diagnostic confidence. In addition, the quantitative measures may improve the visual interpretation of amyloid positivity.
Authors: Dean F Wong; Paul B Rosenberg; Yun Zhou; Anil Kumar; Vanessa Raymont; Hayden T Ravert; Robert F Dannals; Ayon Nandi; James R Brasić; Weiguo Ye; John Hilton; Constantine Lyketsos; Hank F Kung; Abhinay D Joshi; Daniel M Skovronsky; Michael J Pontecorvo Journal: J Nucl Med Date: 2010-06 Impact factor: 10.057
Authors: Rik Ossenkoppele; Willemijn J Jansen; Gil D Rabinovici; Dirk L Knol; Wiesje M van der Flier; Bart N M van Berckel; Philip Scheltens; Pieter Jelle Visser; Sander C J Verfaillie; Marissa D Zwan; Sofie M Adriaanse; Adriaan A Lammertsma; Frederik Barkhof; William J Jagust; Bruce L Miller; Howard J Rosen; Susan M Landau; Victor L Villemagne; Christopher C Rowe; Dong Y Lee; Duk L Na; Sang W Seo; Marie Sarazin; Catherine M Roe; Osama Sabri; Henryk Barthel; Norman Koglin; John Hodges; Cristian E Leyton; Rik Vandenberghe; Koen van Laere; Alexander Drzezga; Stefan Forster; Timo Grimmer; Pascual Sánchez-Juan; Jose M Carril; Vincent Mok; Vincent Camus; William E Klunk; Ann D Cohen; Philipp T Meyer; Sabine Hellwig; Andrew Newberg; Kristian S Frederiksen; Adam S Fleisher; Mark A Mintun; David A Wolk; Agneta Nordberg; Juha O Rinne; Gaël Chételat; Alberto Lleo; Rafael Blesa; Juan Fortea; Karine Madsen; Karen M Rodrigue; David J Brooks Journal: JAMA Date: 2015-05-19 Impact factor: 56.272
Authors: Michael Grundman; Michael J Pontecorvo; Stephen P Salloway; P Murali Doraiswamy; Adam S Fleisher; Carl H Sadowsky; Anil K Nair; Andrew Siderowf; Ming Lu; Anupa K Arora; Abigail Agbulos; Matthew L Flitter; Michael J Krautkramer; Khaled Sarsour; Daniel M Skovronsky; Mark A Mintun Journal: Alzheimer Dis Assoc Disord Date: 2013 Jan-Mar Impact factor: 2.703
Authors: Effie M Mitsis; Heidi A Bender; Lale Kostakoglu; Josef Machac; Jane Martin; Jennifer L Woehr; Margaret C Sewell; Amy Aloysi; Martin A Goldstein; Clara Li; Mary Sano; Sam Gandy Journal: Mol Neurodegener Date: 2014-02-03 Impact factor: 14.195
Authors: Marissa D Zwan; Femke H Bouwman; Elles Konijnenberg; Wiesje M van der Flier; Adriaan A Lammertsma; Frans R J Verhey; Pauline Aalten; Bart N M van Berckel; Philip Scheltens Journal: Alzheimers Res Ther Date: 2017-01-17 Impact factor: 6.982
Authors: Philip S J Weston; Ross W Paterson; John Dickson; Anna Barnes; Jamshed B Bomanji; Irfan Kayani; Michael P Lunn; Catherine J Mummery; Jason D Warren; Martin N Rossor; Nick C Fox; Henrik Zetterberg; Jonathan M Schott Journal: J Alzheimers Dis Date: 2016-10-18 Impact factor: 4.472