Predicting amyloid status in corticobasal syndrome using modified clinical criteria, magnetic resonance imaging and fluorodeoxyglucose positron emission tomography

Sha, Sharon J.; Ghosh, Pia M.; Lee, Suzee E.; Corbetta-Rastelli, Chiara; Jagust, Willian J.; Kornak, John; Rankin, Katherine P.; Grinberg, Lea T.; Vinters, Harry V.; Mendez, Mario F.; Dickson, Dennis W.; Seeley, William W.; Gorno-Tempini, Marilu; Kramer, Joel; Miller, Bruce L.; Boxer, Adam L.; Rabinovici, Gil D.

doi:10.1186/s13195-014-0093-y

Title: Predicting amyloid status in corticobasal syndrome using modified clinical criteria, magnetic resonance imaging and fluorodeoxyglucose positron emission tomography

Journal Article · Thu Jan 01 00:00:00 EST 2015 · Alzheimer's Research & Therapy

DOI:https://doi.org/10.1186/s13195-014-0093-y· OSTI ID:1627017

Sha, Sharon J. ^[1]; Ghosh, Pia M. ^[2]; Lee, Suzee E. ^[3]; Corbetta-Rastelli, Chiara ^[2]; Jagust, Willian J. ^[4]; Kornak, John ^[5]; Rankin, Katherine P. ^[3]; Grinberg, Lea T. ^[3]; Vinters, Harry V. ^[6]; Mendez, Mario F. ^[3]; Dickson, Dennis W. ^[7]; Seeley, William W. ^[3]; Gorno-Tempini, Marilu ^[3]; Kramer, Joel ^[3]; Miller, Bruce L. ^[3]; Boxer, Adam L. ^[3]; Rabinovici, Gil D. ^[4]

Stanford Univ., CA, (United Staets). Dept. of Neurology and Neurological Sciences
Univ. of California, San Francisco, San Francisco, CA, (United States). Dept. of Neurology; Univ. of California, Berkeley, Berkeley, CA, (United States). Helen Wills Neuroscience Institute
Univ. of California, San Francisco, San Francisco, CA, (United States). Dept. of Neurology
Univ. of California, San Francisco, San Francisco, CA, (United States). Dept. of Neurology; Univ. of California, Berkeley, Berkeley, CA, (United States). Helen Wills Neuroscience Institute; ; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Univ. of California, San Francisco, San Francisco, CA (United States). Department of Epidemiology and Biostatistics
Univ. of California, San Francisco, San Francisco, CA, (United States). Dept. of Neurology; Univ. of California, Los Angeles, CA, (United States). Dept. of Pathology and Laboratory Medicine
Mayo Clinic, Jacksonville, FL (United States). Dept. of Laboratory Medicine & Pathology

Introduction Group comparisons demonstrate greater visuospatial and memory deficits and temporoparietal-predominant degeneration on neuroimaging in patients with corticobasal syndrome (CBS) found to have Alzheimer’s disease (AD) pathology versus those with underlying frontotemporal lobar degeneration (FTLD). The value of these features in predicting underlying AD pathology in individual patients is unknown. The goal of this study is to evaluate the utility of modified clinical criteria and visual interpretations of magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) for predicting amyloid deposition (as a surrogate of Alzheimer’s disease neuropathology) in patients presenting with CBS. Methods In total, 25 patients meeting CBS core criteria underwent amyloid (Pittsburgh compound B; PIB) PET scans. Clinical records, MRI, and FDG scans were reviewed blinded to PIB results. Modified clinical criteria were used to classify CBS patients as temporoparietal variant CBS (tpvCBS) or frontal variant CBS (fvCBS). MRI and FDG-PET were classified based on the predominant atrophy/hypometabolism pattern (frontal or temporoparietal). Results A total of 9 out of 13 patients classified as tpvCBS were PIB+, compared to 2out of 12 patients classified as fvCBS (P < 0.01, sensitivity 82%, specificity 71% for PIB+ status). Visual MRI reads had 73% sensitivity and 46% specificity for PIB+ status with moderate intra-rater reliability (Cohen’s kappa = 0.42). Visual FDG reads had higher sensitivity (91%) for PIB+ status with perfect intra-rater reliability (kappa = 1.00), though specificity was low (50%). PIB results were confirmed in all 8 patients with available histopathology (3 PIB+ with confirmed AD, 5 PIB- with FTLD). Conclusions Splitting CBS patients into frontal or temporoparietal clinical variants can help predict the likelihood of underlying AD, but criteria require further refinement. Temporoparietal-predominant neuroimaging patterns are sensitive but not specific for AD.

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Research Organization:: Lawrence Berkeley National Lab (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Institute on Aging; Tau Consortium; John Douglas French Alzheimer’s Foundation; State of California Department of Health Services Alzheimer’s Disease Research Centre of California

Grant/Contract Number:: AC02-05CH11231; R01-AG027859; P01-AG1972403; P50-AG023501; R01AG038791; 04–33516

OSTI ID:: 1627017

Journal Information:: Alzheimer's Research & Therapy, Vol. 7, Issue 1; ISSN 1758-9193

Publisher:: BioMed CentralCopyright Statement

Country of Publication:: United States

Language:: English

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