Cited by 604Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsMedical Imaging Techniques and Applications

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer's disease: Phase 3 study

Open access

Sabri, Osama, Sabbagh, Marwan N., Seibyl, John, Barthel, Henryk, Akatsu, Hiroyasu, Ouchi, Yasuomi, Senda, Kohei, Murayama, Shigeo, Ishii, Kenji, Takao, Masaki, Beach, Thomas G., Rowe, Christopher C., Leverenz, James B., Ghetti, Bernardino, Ironside, James W., Catafau, Ana M., Stephens, Andrew W., Mueller, Andre, Koglin, Norman, Hoffmann, Anja, Roth, Katrin, Reininger, Cornelia, Schulz‐Schaeffer, Walter J., Florbetaben Phase 3 Study Group·2015·Alzheimer's & Dementia

DOI 10.1016/j.jalz.2015.02.004

Open this paper in CoChat — free account Open PDF

Abstract

Background: Evaluation of brain β‐amyloid by positron emission tomography (PET) imaging can assist in the diagnosis of Alzheimer disease (AD) and other dementias. Methods: Open‐label, nonrandomized, multicenter, phase 3 study to validate the 18 F‐labeled β‐amyloid tracer florbetaben by comparing in vivo PET imaging with post‐mortem histopathology. Results: Brain images and tissue from 74 deceased subjects (of 216 trial participants) were analyzed. Forty‐six of 47 neuritic β‐amyloid‐positive cases were read as PET positive, and 24 of 27 neuritic β‐amyloid plaque‐negative cases were read as PET negative (sensitivity 97.9% [95% confidence interval or CI 93.8–100%], specificity 88.9% [95% CI 77.0–100%]). In a subgroup, a regional tissue‐scan matched analysis was performed. In areas known to strongly accumulate β‐amyloid plaques, sensitivity and specificity were 82% to 90%, and 86% to 95%, respectively. Conclusions: Florbetaben PET shows high sensitivity and specificity for the detection of histopathology‐confirmed neuritic β‐amyloid plaques and may thus be a valuable adjunct to clinical diagnosis, particularly for the exclusion of AD. Trial registration: ClinicalTrials.gov NCT01020838 .

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.jalz.2015.02.004

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Statistical Methods for Rates and Proportions
Fleiss, J. L., Levin, B., & Paik, M. C. (2003) · Wiley Series in Probability and Statistics · doi:10.1002/0471445428 · Cited by 18,646
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123 · cited 4×
“Middle frontal gyrus Intense [19] 18/21 (85.”
Influential
Statistical Methods for Rates and Proportions.
Everitt, B. S., & Fleiss, J. L. (1981) · Biometrics · doi:10.2307/2530193 · Cited by 9,034
The Consortium to Establish a Registry for Alzheimer's Disease (CERAD): Part II. Standardization of the neuropathologic assessment of Alzheimer's disease
Mirra, S. S., Heyman, A., McKeel, D., Sumi, S. M., Crain, B. J., Brownlee, L. M., Vogel, F. S., Hughes, J. P., Belle, G. V., Berg, L., et al. (1991) · Neurology · doi:10.1212/wnl.41.4.479 · Cited by 4,961
Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria
Dubois, B., Feldman, H. H., Jacova, C., Hampel, H., Molinuevo, J. L., Blennow, K., DeKosky, S. T., Gauthier, S., Selkoe, D., Bateman, R., et al. (2014) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70090-0 · Cited by 3,321
Self-citation
National Institute on Aging–Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease
Hyman, B. T., Phelps, C. H., Beach, T. G., Bigio, E. H., Cairns, N. J., Carrillo, M. C., Dickson, D. W., Duyckaerts, C., Frosch, M. P., Masliah, E., et al. (2012) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.10.007 · Cited by 3,092
Self-citation
Consortium to Establish a Registry for Alzheimer's Disease (CERAD)
Fillenbaum, G., & Heyman, A. (2002) · doi:10.1002/0470846410.ch42(iii · Cited by 2,859
Revising the definition of Alzheimer's disease: a new lexicon
Dubois, B., Feldman, H. H., Jacova, C., Cummings, J. L., DeKosky, S. T., Barberger-Gateau, P., Delacourte, A., Frisoni, G., Fox, N. C., Galasko, D., et al. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(10)70223-4 · Cited by 1,869
Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates.
Scheltens, P., Leys, D., Barkhof, F., Huglo, D. G., Weinstein, H. C., Vermersch, P., Kuiper, M. A., Steinling, M., Wolters, E. C., & Valk, J. (1992) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.55.10.967 · Cited by 1,811
Use of Florbetapir-PET for Imaging β-Amyloid Pathology
Clark, C. M. (2011) · JAMA · doi:10.1001/jama.2010.2008 · Cited by 1,117

Show all 33 references

Sorted by citations

Items 501–600 of 604

Dual-mode Non-invasive Highly Sensitive Electromagnetic Square-shaped slotted Bio-Sensor for Brain Disease Detection
Mendiratta, N., Anand, R., Dutta, D., Reddy, G. S., & Sarkar, A. (2024) · IEEE Applied Sensing Conference (APSCON) · doi:10.1109/apscon60364.2024.10465829
PET and SPECT imaging as a solid guide to detect and discriminate atypical phenotypes of neurodegenerative disorders
Nuclear Medicine Division, A. O. –. U. D. P., Ruffini, L., Zilioli, A., Neurology Division, A. O. –. U. D. P., Cervati, V., Nuclear Medicine Division, A. O. –. U. D. P., Lauretani, F., Misirocchi, F., Neurology Division, A. O. –. U. D. P., Maggio, M. G., et al. (2024) · European Journal of Clinical and Experimental Medicine · doi:10.15584/ejcem.2024.1.20 · Cited by 1
Outline of blood base biomarkers
岩田, 淳. (2024) · Nippon Ronen Igakkai Zasshi Japanese Journal of Geriatrics · doi:10.3143/geriatrics.61.23
Additional file 1 of Comparison of regional flortaucipir PET with quantitative tau immunohistochemistry in three subjects with Alzheimer’s disease pathology: a clinicopathological study
Pontecorvo, M. J., Keene, C. D., Beach, T. G., Montine, T. J., Arora, A., Devous, M. D., Navitsky, M. A., Kennedy, I. A., Joshi, A. D., Lu, M., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12486836
Self-citation
Plasma amyloid-β biomarkers for predicting amyloid positivity in mild cognitive impairment: a prospective cohort studyPreprint
Ataka, T., Kimura, N., Kaneko, N., Masuda, T., Takeuchi, Y., Yabuuchi, K., Mizukami, T., Takeuchi, T., Ito, T., Tasai, H., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4361497/v1
Assessing the Use of Transfer Learning in Neural Networks Used to Assist in the Diagnosis of Alzheimer’s Disease
Muñoz-Sánchez, D., Górriz, J. M., Ramı́rez, J., Martínez-Murcia, F. J., & Segovia, F. (2024) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic/rtsd57108.2024.10658134
The Intrinsic Connectivity between the Default Mode and Dorsal Attention Networks Is a Marker of Alzheimer's Pathology Burden Independent of Cognitive ReservePreprint
Lombardo, D., & Beckmann, C. F. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4980156
Bundle-wise functional connectivity density and fractional amplitude of low-frequency fluctuations decrease in white matter in preclinical Alzheimer's disease and are associated with A[beta] levels and cognition
Chang, Y., Xu, L., Gao, C., Khairi, N. M., Gore, J. C., Landman, B. A., & Gao, Y. (2025) · SPIE Medical Imaging: Clinical and Biomedical Imaging · doi:10.1117/12.3046835
Functional Neuroimaging in Nuclear Medicine
Dizdar, N. A., & Cayir, D. (2025) · IntechOpen eBooks · doi:10.5772/intechopen.1009689
Synthesis and Evaluation of Novel 18F-labeled Oxadiazole-based Positron Emission Tomography Tracers for β-Amyloid
Qi, Z., Guo, W., Cui, M., & Lu, J. (2025) · Radiation Research · doi:10.1667/rade-25-00035.1
A century of Alzheimer's disease: Merging neuropathology with in vivo biomarkers
Harrison, T. M., & Jagust, W. J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-822240-9.00013-2
Integrating Artificial Intelligence with Neuroimaging
Malviya, R., Kumar, S., Solanke, A. S., Goyal, P., & Chauhan, K. (2025) · Wiley eBooks · doi:10.1002/9781394347537.ch6
Imaging Markers of Amyloid and Tau in Alzheimer Disease
Tarawneh, R. M. (2025) · Springer eBooks · doi:10.1007/978-3-031-80849-4_20
Fluid and Imaging Markers of Amyloid Pathology
West, T., Meyer, M. R., Kirmess, K. M., Yarasheski, K. E., Braunstein, J. B., & Verghese, P. B. (2025) · Springer eBooks · doi:10.1007/978-3-031-80849-4_1
Long Axial Field of View PET
Mehadji, B., Abdelhafez, Y. G., Alavi, A., Chung, K. J., Karakatsanis, N. A., Omidvari, N., & Nardo, L. (2025) · PET Clinics · doi:10.1016/j.cpet.2025.07.009
The Intrinsic Connectivity between the Default Mode and Dorsal Attention Networks is an Independent fMRI Biomarker of Alzheimer's Disease Pathology BurdenPreprint
Lombardo, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5401595
Co-occurrence of primary familial brain calcification from a likely pathogenic SLC20A2 variant and Alzheimer’s disease biology
Lee, H.-J., Yoo, S., Kim, M., Kim, H. S., & Kim, J. (2025) · Neurocase · doi:10.1080/13554794.2025.2562918
PET-Imaging bei Demenzerkrankungen
Parvizi, T. (2025) · psychopraxis neuropraxis · doi:10.1007/s00739-025-01127-2
Mendelian Randomization Reveals Unidirectional Links Between Amyloid-β and Tau in Alzheimer’s Disease
Kim, J. P., Lee, H., Kim, B., Nho, K. T., Risacher, S. L., Saykin, A. J., Seo, S. W., & Kim, H. (2025) · Dementia and Neurocognitive Disorders · doi:10.12779/dnd.2025.24.4.246
Case 2: Alzheimer’s Disease: [18F]FDG, Tau, and Amyloid PET
Mínguez, F., Romera, M., Bastidas, J. F., Riverol, M., & Arbizu, J. (2025) · Springer eBooks · doi:10.1007/978-3-032-00522-9_2
Cerebrospinal Fluid, Molecular Neuroimaging, and Blood Biomarkers of Neurodegenerative Disease: Past, Present, and Future
Soleimani‐Meigooni, D. N., & Rabinovici, G. D. (2025) · Cambridge University Press eBooks · doi:10.1017/9781108985055.006
White Matter Hyperintensities in Relation to Cognition and Amyloid Burden in a South Korean Cohort With Subjective Cognitive Decline
Lim, E. Y., Jeong, J. H., Park, K. H., Kim, S., Wang, M. J., Shim, Y. S., Choi, S. H., & Yang, D. W. (2025) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0000000000000683
Noninvasive imaging of amyloid‐beta and tau in rodent and nonhuman primate models
Ni, R., & Rominger, A. (2025) · Open Research (University of Surrey) · doi:10.1002/viw.20250071
Additional file 1 of Evaluation of cerebrospinal fluid glycoprotein NMB (GPNMB) as a potential biomarker for Alzheimer’s disease
Aichholzer, F., Klafki, H., Ogorek, I., Vogelgsang, J. S., Wiltfang, J., Scherbaum, N., Weggen, S., & Wirths, O. (2021) · Figshare · doi:10.6084/m9.figshare.14538839
Additional file 1 of Performance of plasma Aβ42/40, measured using a fully automated immunoassay, across a broad patient population in identifying amyloid status
Bun, S., Ito, D., Tezuka, T., Kubota, M., Ueda, R., Takahata, K., Moriguchi, S., Kurose, S., Momota, Y., Suzuki, N., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26614732
Additional file 2 of Evaluation of cerebrospinal fluid glycoprotein NMB (GPNMB) as a potential biomarker for Alzheimer’s disease
Aichholzer, F., Klafki, H., Ogorek, I., Vogelgsang, J. S., Wiltfang, J., Scherbaum, N., Weggen, S., & Wirths, O. (2021) · Figshare · doi:10.6084/m9.figshare.14538842
Additional file 1 of Deep learning-based amyloid PET positivity classification model in the Alzheimer’s disease continuum by using 2-[18F]FDG PET
Kim, S., Lee, P., Oh, K. T., Byun, M. S., Yi, D., Lee, J. H., Kim, Y. K., Ye, B. S., Yun, M. J., Lee, D. Y., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14767314
Additional file 1 of Effect of APOE ε4 genotype on amyloid-β and tau accumulation in Alzheimer’s disease
Baek, M. S., Cho, H., Lee, H. S., Lee, J. H., Ryu, Y. H., & Lyoo, C. H. (2020) · Figshare · doi:10.6084/m9.figshare.13174043
Additional file of Comparison of regional flortaucipir PET with quantitative tau immunohistochemistry in three subjects with Alzheimer’s disease pathology: a clinicopathological study
Pontecorvo, M. J., Keene, C. D., Beach, T. G., Montine, T. J., Arora, A., Devous, M. D., Navitsky, M. A., Kennedy, I. A., Joshi, A. D., Lu, M., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12872198
Self-citation
Additional file 1 of Dynamic network model reveals distinct tau spreading patterns in early- and late-onset Alzheimer disease
Lee, W. J., Cho, H., Baek, M. S., Kim, H.-K., Lee, J. H., Ryu, Y. H., Lyoo, C. H., & Seong, J.-K. (2022) · Figshare · doi:10.6084/m9.figshare.20887205
Additional file 1 of Early tau detection in flortaucipir images: validation in autopsy-confirmed data and implications for disease progression
Kotari, V., Southekal, S. S., Navitsky, M. A., Kennedy, I. A., Lu, M., Morris, A., Zimmer, J. A., Fleisher, A. S., Mintun, M. A., Devous, M. D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26571886
Additional file 5 of Performance of plasma Aβ42/40, measured using a fully automated immunoassay, across a broad patient population in identifying amyloid status
Bun, S., Ito, D., Tezuka, T., Kubota, M., Ueda, R., Takahata, K., Moriguchi, S., Kurose, S., Momota, Y., Suzuki, N., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26614744
Additional file 1 of Blood-based CNS regionally and neuronally enriched extracellular vesicles carrying pTau217 for Alzheimer’s disease diagnosis and differential diagnosis
Guo, Z., Tian, C., Shi, Y., Song, X.-R., Yin, W., Tao, Q.-Q., Liu, J., Peng, G.-P., Wu, Z.-Y., Wang, Y.-J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26689169
Additional file 1 of Principal components of tau positron emission tomography and longitudinal tau accumulation in Alzheimer’s disease
Cho, H., Baek, M. S., Lee, H. S., Lee, J. H., Ryu, Y. H., & Lyoo, C. H. (2020) · Figshare · doi:10.6084/m9.figshare.12997390
Additional file 2 of Identifying novel genetic variants for brain amyloid deposition: a genome-wide association study in the Korean population
Kim, H.-R., Jung, S.-H., Kim, J., Jang, H., Kang, S. H., Hwangbo, S., Kim, J. P., Kim, S. Y., Kim, B., Kim, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14821168
Additional file 3 of Evaluation of cerebrospinal fluid glycoprotein NMB (GPNMB) as a potential biomarker for Alzheimer’s disease
Aichholzer, F., Klafki, H., Ogorek, I., Vogelgsang, J. S., Wiltfang, J., Scherbaum, N., Weggen, S., & Wirths, O. (2021) · Figshare · doi:10.6084/m9.figshare.14538845
Additional file 2 of Early detection of amyloid load using 18F-florbetaben PET
Bullich, S., Roé-Vellvé, N., Marquié, M., Landau, S. M., Barthel, H., Villemagne, V. L., Sanabria, Á., Tartari, J. P., Sotolongo‐Grau, Ó., Doré, V., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14330658
Self-citation
Additional file 1 of Impacts of baseline biomarkers on cognitive trajectories in subjective cognitive decline: the CoSCo prospective cohort study
Hong, Y. J., Ho, S., Jeong, J. H., Park, K. H., Kim, S., Wang, M. J., Choi, S. H., & Yang, D. W. (2023) · Figshare · doi:10.6084/m9.figshare.23902855
Additional file 5 of Early tau detection in flortaucipir images: validation in autopsy-confirmed data and implications for disease progression
Kotari, V., Southekal, S. S., Navitsky, M. A., Kennedy, I. A., Lu, M., Morris, A., Zimmer, J. A., Fleisher, A. S., Mintun, M. A., Devous, M. D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26571898
Additional file 2 of Blood-based CNS regionally and neuronally enriched extracellular vesicles carrying pTau217 for Alzheimer’s disease diagnosis and differential diagnosis
Guo, Z., Tian, C., Shi, Y., Song, X.-R., Yin, W., Tao, Q.-Q., Liu, J., Peng, G.-P., Wu, Z.-Y., Wang, Y.-J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26689172
Additional file 1 of Validation of amyloid PET positivity thresholds in centiloids: a multisite PET study approach
Royse, S. K., Minhas, D. S., Lopresti, B. J., Murphy, A., Ward, T., Koeppe, R. A., Bullich, S., DeSanti, S., Jagust, W. J., & Landau, S. M. (2021) · Figshare · doi:10.6084/m9.figshare.14570419
Additional file 4 of Performance of plasma Aβ42/40, measured using a fully automated immunoassay, across a broad patient population in identifying amyloid status
Bun, S., Ito, D., Tezuka, T., Kubota, M., Ueda, R., Takahata, K., Moriguchi, S., Kurose, S., Momota, Y., Suzuki, N., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26614741
Additional file 2 of Performance of plasma Aβ42/40, measured using a fully automated immunoassay, across a broad patient population in identifying amyloid status
Bun, S., Ito, D., Tezuka, T., Kubota, M., Ueda, R., Takahata, K., Moriguchi, S., Kurose, S., Momota, Y., Suzuki, N., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26614735
Additional file 1 of Identifying novel genetic variants for brain amyloid deposition: a genome-wide association study in the Korean population
Kim, H.-R., Jung, S.-H., Kim, J., Jang, H., Kang, S. H., Hwangbo, S., Kim, J. P., Kim, S. Y., Kim, B., Kim, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14821165
Additional file 1 of Extensive frontal focused ultrasound mediated blood–brain barrier opening for the treatment of Alzheimer’s disease: a proof-of-concept study
Park, S. H., Baik, K., Jeon, S., Chang, W. S., Ye, B. S., & Chang, J. W. (2021) · Figshare · doi:10.6084/m9.figshare.16943671
Additional file 1 of Alteration of medial temporal lobe metabolism related to Alzheimer’s disease and dementia with lewy bodies
Kang, S., Jeon, S., Lee, Y.-G., & Ye, B. S. (2024) · Figshare · doi:10.6084/m9.figshare.25678570
Additional file 3 of Early tau detection in flortaucipir images: validation in autopsy-confirmed data and implications for disease progression
Kotari, V., Southekal, S. S., Navitsky, M. A., Kennedy, I. A., Lu, M., Morris, A., Zimmer, J. A., Fleisher, A. S., Mintun, M. A., Devous, M. D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26571892
Additional file 4 of Early tau detection in flortaucipir images: validation in autopsy-confirmed data and implications for disease progression
Kotari, V., Southekal, S. S., Navitsky, M. A., Kennedy, I. A., Lu, M., Morris, A., Zimmer, J. A., Fleisher, A. S., Mintun, M. A., Devous, M. D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26571895
Additional file 4 of Evaluation of cerebrospinal fluid glycoprotein NMB (GPNMB) as a potential biomarker for Alzheimer’s disease
Aichholzer, F., Klafki, H., Ogorek, I., Vogelgsang, J. S., Wiltfang, J., Scherbaum, N., Weggen, S., & Wirths, O. (2021) · Figshare · doi:10.6084/m9.figshare.14538848
Additional file 5 of Evaluation of cerebrospinal fluid glycoprotein NMB (GPNMB) as a potential biomarker for Alzheimer’s disease
Aichholzer, F., Klafki, H., Ogorek, I., Vogelgsang, J. S., Wiltfang, J., Scherbaum, N., Weggen, S., & Wirths, O. (2021) · Figshare · doi:10.6084/m9.figshare.14538851
Additional file 1 of Machine learning application for classification of Alzheimer's disease stages using 18F-flortaucipir positron emission tomography
Park, S. W., Yeo, N. Y., Lee, J., Lee, S.-H., Byun, J., Park, D. Y., Yum, S., Kim, J.-K., Byeon, G., Kim, Y., et al. (2023) · Figshare · doi:10.6084/m9.figshare.26585997
Additional file 1 of Early detection of amyloid load using 18F-florbetaben PET
Bullich, S., Roé-Vellvé, N., Marquié, M., Landau, S. M., Barthel, H., Villemagne, V. L., Sanabria, Á., Tartari, J. P., Sotolongo‐Grau, Ó., Doré, V., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14330655
Self-citation
Additional file 3 of Performance of plasma Aβ42/40, measured using a fully automated immunoassay, across a broad patient population in identifying amyloid status
Bun, S., Ito, D., Tezuka, T., Kubota, M., Ueda, R., Takahata, K., Moriguchi, S., Kurose, S., Momota, Y., Suzuki, N., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26614738
Additional file 2 of Early tau detection in flortaucipir images: validation in autopsy-confirmed data and implications for disease progression
Kotari, V., Southekal, S. S., Navitsky, M. A., Kennedy, I. A., Lu, M., Morris, A., Zimmer, J. A., Fleisher, A. S., Mintun, M. A., Devous, M. D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26571889
Ethical dilemmas and medicolegal implications of nuclear medicine imaging in dementias
Kabunda, J., Ndlovu, H., Chimbabantu, K., Knoesen, Z., Mashigoane, K., Mokoala, K., & Sathekge, M. (2025) · Clinical and Translational Imaging · doi:10.1007/s40336-025-00735-7
Is there a link between multiple sclerosis and Alzheimer disease? A critical note
Jellinger, K. (2025) · Journal of Neural Transmission · doi:10.1007/s00702-025-03073-0
Cryo-EM methods to study binding between amyloid fibrils and chemical compounds
Zhao, Q., Liu, K., Li, D., & Liu, C. (2026) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2026.01.029
Association of residential neighborhood disadvantage with amyloid PET positivity among cognitively impaired individuals
Windon, C., Tsoy, E., Livaudais‐Toman, J., Neilands, T. B., Palmer, N. R., Heston, M. B., Johnson, J. K., Hanna, L. S., Gatsonis, C., Romanoff, J., et al. (2026) · Alzheimer s & Dementia Behavior & Socioeconomics of Aging · doi:10.1002/bsa3.70058
Posicionamiento de la Sociedad Española de Medicina Nuclear e Imagen Molecular para la preparación del sistema sanitario ante la llegada de los nuevos tratamientos contra la enfermedad de Alzheimer
Vicente, J. S. (2026) · Revista Española de Medicina Nuclear e Imagen Molecular · doi:10.1016/j.remn.2025.500267
The association between temporal-lobe tensor-based morphometry and plasma amyloid-β in mild cognitive impairment
Nasiri, H., KHOSRAVI, F., Ashrafi, M., Niknam, N., Fard, A. M., Koumleh, B. A., Amirian, M. A., Marnani, S. A. S., Sadeghi, M., Delshad, S., et al. (2026) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2026.109360
Alzheimer’s risk markers and resting-state dynamic functional connectivity: Cross-Sectional Findings from the AGUEDA StudyPreprint
Coca‐Pulido, A., Solis‐Urra, P., Contreras‐Rodríguez, O., Biarnés, C., Olvera‐Rojas, M., Jain, S., Sehrawat, A., Chen, Y., García‐Rivero, Y., Gómez-Río, M. M., et al. (2026) · medRxiv · doi:10.64898/2026.02.24.26346860
In-vitro interaction studies between the amyloid PET tracer florbetaben and the amyloid-beta targeting antibodies lecanemab and donanemab on AD brain samples reveal no interferences
Mueller, A., Jovalekic, A., Chapleau, M., Seidel, J., Ritter, M., Bickel, E., Kiessling, N., MacSweeney, E., Stephens, A. W., & Koglin, N. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-026-02001-y
Self-citation
Assessment of early-phase [ 18 F]florbetaben images as a proxy for brain metabolism in mouse models of Alzheimer’s disease
Gröger, R., Englert, A. L., Lalia, M., de Weerd, L., Rovere, M., Kunze, L. H., Palumbo, G., Wind-Mark, K., Fixemer, S., Roemer, S. N., et al. (2026) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x261420082
Differentiating Early Alzheimer’s Disease from MCI Using Comprehensive Semiquantitative Parameters in Dual-Phase Amyloid PET: A Pilot Study
Choi, H. J., Cho, A. J., You, J. H., Park, S., Lee, S. R., & Kim, D. H. (2026) · Medicina · doi:10.3390/medicina62030529
Plasma glial fibrillary acidic protein as a potential biomarker for differentiating amyloid-negative subjective cognitive decline and mild cognitive impairment
Yoon, B. W., Lee, H.-J., Chae, H., Oh, E., Hong, Y. A., Jeong, J. H., Park, K. H., Kim, S., Wang, M. J., Choi, S. H., et al. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261427806
From autopsy to PET to cerebrospinal fluid to blood: Quantifying reference-standard dependence in Alzheimer's disease amyloid biomarker validation
Verma, K., & Kumar, S. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261430858
Florzolotau (18F) retention is linked to neuropsychological performance in tauopathy
Shimizu, A., Iwabuchi, Y., Bun, S., Watanabe, M., Kubota, M., Shimohama, S., Tezuka, T., Takahata, K., Tabuchi, H., Seki, M., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71319
Early memory deficits and extensive brain network disorganization in the AppNL-F/MAPT double knock-in mouse model of familial Alzheimer's disease
Borcuk, C. (2021) · theses.fr (ABES) · doi:10.70675/c316f2dcz54d3z41cfzb3f5z99c7b2d3cd95 · Cited by 1
La place des biomarqueurs dans les essais de prévention de la maladie d'Alzheimer
Delrieu, J. (2021) · theses.fr (ABES) · doi:10.70675/2b5f4719z01f9z4376z9dabzd5a185dc4d79
Neuroimagerie des altérations du lobe temporal médian : avancées pour la compréhension, l'évaluation et la prévention de la maladie d'Alzheimer 22017004
Chauveau, L. (2024) · theses.fr (ABES) · doi:10.70675/21081e29ze9c3z431azb7a5zb77aaaddfc26
Amyloid-β PET scans, economic strain and financial decision-making among persons with cognitive impairment and care partners: a mixed-methods analysis of the CARE-IDEAS study
Houtven, C. H. V., Ford, C. B., Couch, E., Shepherd‐Banigan, M. E., Zhang, W., Bélanger, E., Plassman, B. L., Burke, J. R., Sorenson, C., DePasquale, N., et al. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-026-02010-x
APOE Genotype Modifies the Predictive Performance of Plasma Biomarkers for Amyloid Plaque Burden in Subjective Cognitive Decline
Lee, H. B., Yoon, B., Hong, Y. J., Jeong, J. H., Kim, S., Wang, M. J., Choi, S. H., Ryu, J. S., Kang, S., Park, K. H., et al. (2026) · Journal of Korean Medical Science · doi:10.3346/jkms.2026.41.e184
Therapeutic Landscape of Early Symptomatic Alzheimer’s Disease Translated into Everyday Practice for Geriatric Providers
Bailey-Taylor, M. J., Morrow, P., Hendrix, J., Galluzzi, K. E., & Sharafsaleh, G. (2026) · Clinical Interventions in Aging · doi:10.2147/cia.s585263
Brain Amyloid Deposition Is Negatively Associated with Cardiac Amyloid Retention in Apo E4 Carriers: A Pilot Study
Pak, K., Kim, J., Kim, K., Kim, K., Kim, Y. J., Lee, H., Suh, H., Moon, E., Lee, B.-D., Park, J., et al. (2026) · Molecular Imaging and Biology · doi:10.1007/s11307-026-02108-6
Tracking of Neuroinflammation Dynamics During Combined Anti-β-Amyloid Therapy (AAT) and Immunomodulation in a Preclinical Alzheimer’s Disease Model
Wind-Mark, K., Kunze, L. H., Willem, M., Palumbo, G. A., Giudici, C., Nuscher, B., Boening, G., Gildehaus, F. J., Lindner, S., Werner, R. A., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27104632
Mitochondrial dysfunction in Alzheimer’s disease: connecting pathophysiology with neuroimaging
Hu, J., Lai, J., Zhang, B., Jiang, X., Ma, H. F., & Liu, Y. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1748227
A local-to-distant shift in dynamic brain connectivity marks early Alzheimer’s risk
Coca‐Pulido, A., Solis‐Urra, P., Contreras‐Rodríguez, O., Biarnés, C., Olvera‐Rojas, M., Jain, S., Sehrawat, A., Chen, Y., García‐Rivero, Y., Gómez-Río, M., et al. (2026) · GeroScience · doi:10.1007/s11357-026-02333-5
Regional amyloid PET asymmetry and long‐term clinical trajectory in mild cognitive impairment
Lee, Y., Kang, S., Kim, Y. W., Kim, J. S., & Kim, H. (2026) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70394
The Centiloid Scale in Amyloid PET Imaging: Current Role in Alzheimer’s Disease Diagnosis, Treatment Planning, and Monitoring During Anti-Amyloid Therapy: A Clinical Perspective
Sotoudeh, H., & Alizadeh, M. (2026) · Diagnostics · doi:10.3390/diagnostics16131989
Decomposition‐based harmonization for quantitative PET imaging across scanners and radiotracers
Zhao, S., Huang, Q., Nie, B., Zhang, T., Zuo, C. Y., Zhang, G., Mao, G. G., & Shan, B. (2026) · Medical Physics · doi:10.1002/mp.70545
Genetic Associations with Temporal Modeling of Alzheimer’s Disease Progression Supports a Novel Paradigm for Disease RiskPreprint
Jordan, D. M., Kritzer, E., Thompson, R. C., Lund, A. N., Patel, T., Grice, A. B., John, D., Initiative, A. D. N., Initiative, A. D. N., Goate, A., et al. (2026) · medRxiv · doi:10.64898/2026.07.07.26356710
Threshold in Focus—Defining Amyloid Positivity on the Centiloid Scale
Nasrallah, I. M., & Brown, C. (2026) · JAMA · doi:10.1001/jama.2026.12565
Blood transcriptomic signatures link β-amyloid deposition to molecular pathways across SCD, MCI, and dementia
Lin, Y., Cheng, L., Zhang, Z., Cui, L., Li, W., Guo, Q., & Miao, Y. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1816733
From image reconstruction to clinical prognosis: A comprehensive analysis of centiloid-based amyloid PET assessment
Hirschmüller, K. C. E., Prieto, E., Guillén, E. F., Echeveste, B., Mínguez, F., Romera, M., Pareja, F., Fahmi, R., Riverol, M., & Arbizu, J. (2026) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-026-07943-x
Optimal acquisition timing for perfusion imaging in dual-phase amyloid-PET: a within-patient list-mode comparison of 5-minute versus 10-minute protocols
Maffione, A. M., Ferretti, A., Bartoletti, P., Pavan, L., Massaro, A., Chondrogiannis, S., Osele, A., Gava, M. M., & Marzola, M. C. (2026) · Clinical and Translational Imaging · doi:10.1007/s40336-026-00791-7
Quantification of global gray matter amyloid‐β plaque from 18 F‐Florbetaben PET/MRI and correlation to BAPL score in patients with cognitive impairment
Farag, A., Mirshahvalad, S. A., Ortega, C., Tartaglia, M. C., Veit‐Haibach, P., & Metser, U. (2026) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70449
Validations of Plasma Biomarker Tests
Yang, C. S.-Y., & Sabbagh, M. N. (2026) · Springer eBooks · doi:10.1007/978-3-032-23863-4_4
Self-citation
Clinical and Biologic Characterization of a 3-Level Centiloid-Based Classification of Amyloid PET
Mathoux, G., Boccalini, C., Peretti, D. E., Scheffler, M., Margot, H., Ashton, N. J., Zetterberg, H., Frisoni, G. B., & Garibotto, V. (2026) · Journal of Nuclear Medicine · doi:10.2967/jnumed.126.272655
Diagnosis of neurodegenerative diseases based on molecular imaging
Quan, L., Lang, M., Yue, J., Zhu, J., Xu, J., Wang, K., Huang, H., Song, Q., Zhao, Y., Zhu, Z., et al. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.180865
Radiopharmaceuticals in Molecular Neuroimaging
Ponisio, M. R. (2026) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2026.07.002
APOE4-dependent cholinergic/dopaminergic contributions to clinical symptoms across Alzheimer’s disease spectrum: A retrospective observational study
Kang, S., Jeon, S., Lee, D., Lee, H., Jeon, S.-H., Choi, M., Lee, Y., Shin, N.-Y., Yun, M., & Ye, B. S. (2026) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-026-08165-x
Amyloid-β PET Radioligands in Alzheimer’s Disease: From Plaque Detection to Biomarker-Guided Patient Stratification and Therapeutic Monitoring
Lista, S., Filippi, L., Emanuele, E., López‐Ortiz, S., Cisale, G. Y., Minoretti, P., Garaci, F. G., & Santos‐Lozano, A. (2026) · Molecular Diagnosis & Therapy · doi:10.1007/s40291-026-00879-w
Comparison of Expert Reads Versus Local Clinical Reads of Amyloid PET Scans in the IDEAS Study
Chiotis, K., Windon, C., Siegel, B. V., Apgar, C., Carrillo, M. C., Gareen, I., Hanna, L., March, A., Romanoff, J., Gatsonis, C., et al. (2026) · Journal of Nuclear Medicine · doi:10.2967/jnumed.126.272571
Circulating Metabolite Signatures Associated With Amyloid Positivity, Cognitive Performance and Plasma P-tau181 Levels in Cognitively Unimpaired Older Adults
Sharmin, T., Doecke, J. D., Chatterjee, P., Sohrabi, H. R., Zetterberg, H., Ashton, N. J., Garg, M. L., Blennow, K., & Martins, R. N. (2026) · Molecular Neurobiology · doi:10.1007/s12035-026-06184-1
Novel Radiotracers in PET Imaging
Gimblet, G. R., & Lapi, S. E. (2026) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2026.08.011
Clinical Practice Guideline for Brain PET Imaging in Neurodegenerative Diseases: A Summary of Recommendations from the Korean Society of Nuclear Medicine
Bang, J.-I., Pak, K., Na, S., Yoo, H. S., Lee, Y., Kim, N.-Y., Kang, S. Y., Kim, J. H., Choi, M., & Yoon, H. (2026) · Nuclear Medicine and Molecular Imaging · doi:10.1007/s13139-026-01073-w
In vivo molecular brain imaging of metabolic dysfunction and protein accumulation in neurodegenerative diseases
Perovnik, M., & Kolmančič, K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34176-2.00013-4
Multimodal neuroimaging in dementia and neurodegenerative disorders: complementary roles of clinically available nuclear medicine and MRI in the era of disease-modifying therapies
Inui, Y., Takechi, H., Watanabe, H., Kato, T., Ito, K., Kobayashi, R., Toyama, H., & Inoue, M. (2026) · Japanese Journal of Radiology · doi:10.1007/s11604-026-02080-9
Modality-Specific Cutoffs for 18F-Florbetaben Amyloid PET/CT and PET/MR: A Derivation and External Validation StudyPreprint
Yan, S., Wang, J., Yan, S., Wang, J., Yang, Y., Guan, Y., Chen, Y., Wang, R., Zhang, J., Chang, Y., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-11056936/v1
miR‐214‐3p and NCKAP1: A microglial functional dimension of Alzheimer's progression
Noh, M., 문연실, Kang, S., Moon, W., Kim, H., Seo, S. W., Ahn, S., Kang, M., Kim, Y. S., Kim, H., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71903

Authors: Sabri, O., Sabbagh, M. N., Seibyl, J., Barthel, H., Akatsu, H., Ouchi, Y., Senda, K., Murayama, S., Ishii, K., Takao, M., Beach, T. G., Rowe, C. C., Leverenz, J. B., Ghetti, B., Ironside, J. W., Catafau, A. M., Stephens, A. W., Mueller, A., Koglin, N., Hoffmann, A., Roth, K., Reininger, C., Schulz‐Schaeffer, W. J., & Florbetaben Phase 3 Study Group
Affiliations: University Hospital Leipzig (DE)
Banner Sun Health Research Institute (US)
Molecular Neuroimaging LLC New Haven CT USA
Nagoya City University (JP)
Hamamatsu University (JP)
Koseikai Hospital Toyohashi Japan
Tokyo Metropolitan Institute of Gerontology (JP)
Institute of Brain and Blood Vessels (JP)
The University of Melbourne (AU)
University of Washington (US)
Indiana University Indianapolis (US)
University of Edinburgh (GB)
Piramal (Germany) (DE)
Piramal Imaging GmbH Berlin Germany
Bayer (Germany) (DE)
Universitätsmedizin Göttingen (DE)
Corresponding author: Sabri, Osama
ORCID: Sabri, Osama
Sabbagh, Marwan N.
Seibyl, John
Barthel, Henryk
Akatsu, Hiroyasu
Ouchi, Yasuomi
Murayama, Shigeo
Ishii, Kenji
Takao, Masaki
Beach, Thomas G.
Rowe, Christopher C.
Leverenz, James B.
Ghetti, Bernardino
Ironside, James W.
Stephens, Andrew W.
Koglin, Norman
Roth, Katrin
Schulz‐Schaeffer, Walter J.
Venue: Alzheimer's & Dementia
Publisher: Wiley
Published: 2015-03-27
Volume: 11
Issue: 8
Pages: 964-974
ISSN: 1552-5260 (ISSN-L)
1552-5279
DOI: 10.1016/j.jalz.2015.02.004
OpenAlex: W2169508236
Cited by: 602
References: 33
Citations by year: 2026: 57 · 2025: 64 · 2024: 59 · 2023: 50 · 2022: 56 · 2021: 64 · 2020: 61 · 2019: 46 · 2018: 42 · 2017: 47 · 2016: 42 · 2015: 13
FWCI: 39.00
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Alzheimer s & Dementia (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
GRO (submittedVersion)
Funders: Bioscript Medical
Piramal Imaging S.A
Bayer Pharma AG
Piramal Imaging S.A.
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
Keywords: florbetaben, amyloid PET
MeSH terms: Aged, Aged, 80 and over, Alzheimer Disease, Analysis of Variance, Aniline Compounds, Brain, Diagnosis, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Psychiatric Status Rating Scales, Radiography, ROC Curve, Stilbenes, Cohort Studies, Amyloid beta-Peptides, Positron-Emission Tomography, Plaque, Amyloid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer's disease: Phase 3 study

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks