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14 results on '"Silverman, D.H.S."'

2. A blood-based signature of cerebrospinal fluid A beta(1-42) status

Author

Goudey, B., Fung, B.J., Schieber, C., Faux, N.G., Weiner, M.W., Aisen, P., Petersen, R., Jack, C.R., Jagust, W., Trojanowki, J.Q., Toga, A.W., Beckett, L., Green, R.C., Saykin, A.J., Morris, J., Shaw, L.M., Kaye, J., Quinn, J., Silbert, L., Lind, B., Carter, R., Dolen, S., Schneider, L.S., Pawluczyk, S., Beccera, M., Teodoro, L., Spann, B.M., Brewer, J., Vanderswag, H., Fleisher, A., Heidebrink, J.L., Lord, J.L., Mason, S.S., Albers, C.S., Knopman, D., Johnson, K., Doody, R.S., Villanueva-Meyer, J., Chowdhury, M., Rountree, S., Dang, M., Stern, Y., Honig, L.S., Bell, K.L., Ances, B., Morris, J.C., Carroll, M., Creech, M.L., Franklin, E., Mintun, M.A., Schneider, S., Oliver, A., Marson, D., Griffth, R., Clark, D., Geldmacher, D., Brockington, J., Roberson, E., Natelson Love, M., Grossman, H., Mitsis, E., Shah, R.C., deToledo-Morrell, L., Duara, R., Varon, D., Greig, M.T., Roberts, P., Albert, M., Onyike, C., D'Agostino, D., Kielb, S., Galvin, J.E., Cerbone, B., Michel, C.A., Pogorelec, D.M., Rusinek, H., Leon, M.J. de, Glodzik, L., De Santi, S., Doraiswamy, P.M., Petrella, J.R., Borges-Neto, S., Wong, T.Z., Coleman, E., Smith, C.D., Jicha, G., Hardy, P., Sinha, P., Oates, E., Conrad, G., Porsteinsson, A.P., Goldstein, B.S., Martin, K., Makino, K.M., Ismail, M.S., Brand, C., Mulnard, R.A., Thai, G., McAdams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Levey, A.I., Lah, J.J., Cellar, J.S., Burns, J.M., Swerdlow, R.H., Brooks, W.M., Apostolova, L., Tingus, K., Woo, E., Silverman, D.H.S., Lu, P.H., Bartzokis, G., Graff-Radford, N.R., Parftt, F., Kendall, T., Johnson, H., Farlow, M.R., Hake, A.M., Matthews, B.R., Brosch, J.R., Herring, S., Hunt, C., Dyck, .H. van, Carson, R.E., MacAvoy, M.G., Varma, P., Chertkow, H., Bergman, H., Hosein, C., Black, S., Stefanovic, B., Caldwell, C., Hsiung, Ging-Yuek Robin, Feldman, H., Mudge, B., Assaly, M., Finger, E., Pasternack, S., Rachisky, I., Trost, D., Kertesz, A., Bernick, C., Munic, D., Mesulam, M.-M., Lipowski, K., Weintraub, S., Bonakdarpour, B., Kerwin, D., Wu, C.-K., Johnson, N., Sadowsky, C., Villena, T., Turner, R.S., Reynolds, B., Sperling, R.A., Johnson, K.A., Marshall, G., Yesavage, J., Taylor, J.L., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M.N., Belden, C.M., Jacobson, S.A., Sirrel, S.A., Kowall, N., Killiany, R., Budson, A.E., Norbash, A., Johnson, P.L., Obisesan, T.O., Wolday, S., Allard, J., Lerner, A., Ogrocki, P., Tatsuoka, C., Fatica, P., Fletcher, E., Maillard, P., Olichney, J., DeCarli, C., Carmichael, O., Kittur, S., Borrie, M., Lee, T.-Y., Bartha, R., Johnson, S., Asthana, S., Carlsson, C.M., Potkin, S.G., Preda, A., Nguyen, D., Tariot, P., Burke, A., Trncic, N., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, D.W., Kataki, M., Adeli, A., Zimmerman, E.A., Celmins, D., Brown, A.D., Pearlson, G.D., Blank, K., Anderson, K., Flashman, L.A., Seltzer, M., Hynes, M.L., Santulli, R.B., Sink, K.M., Gordineer, L., Williamson, J.D., Garg, P., Watkins, F., Ott, B.R., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, H.J., Miller, B.L., Perry, D., Mintzer, J., Spicer, K., Bachman, D., Pomara, N., Hernando, R., Sarrael, A., Relkin, N., Chaing, G., Lin, M., Ravdin, L., Smith, A., Raj, B.A., Fargher, K., Saykin, A., Nho, K., Kling, M., Toledo, J., Shaw, L., Trojanowski, J., Farrer, L., Kastsenmueller, G., Arnold, M., Wishart, D., Wurtz, P., Bhattcharyya, S., Duijin, C. van, Mangravite, L., Han, X., Hankemeier, T., Fiehn, O., Barupal, D., Thiele, I., Heinken, A., Meikle, P., Price, N., Funk, C., Jia, W., Kueider-Paisley, A., Tenebaum, J., Black, C., Moseley, A., Thompson, W., Mahmoudiandehkorki, S., Baillie, R., Welsh-Bohmer, K., Plassman, B., and Epidemiology

Subjects
Male, 0301 basic medicine, Apolipoprotein E, Oncology, medicine.medical_specialty, Amyloid, Amyloid beta, lcsh:Medicine, Article, 03 medical and health sciences, Apolipoproteins E, 0302 clinical medicine, Cerebrospinal fluid, Alzheimer Disease, Predictive Value of Tests, Internal medicine, medicine, Humans, Dementia, Cognitive decline, lcsh:Science, Aged, Aged, 80 and over, Amyloid beta-Peptides, Multidisciplinary, biology, Chemokine CCL26, business.industry, lcsh:R, Alzheimer’s Disease Metabolomics Consortium, Alzheimer’s Disease Neuroimaging Initiative, medicine.disease, Peptide Fragments, 3. Good health, 030104 developmental biology, biology.protein, Chromogranin A, Female, lcsh:Q, Alzheimer's disease, business, Biomarkers, 030217 neurology & neurosurgery, Alzheimer's Disease Neuroimaging Initiative
Abstract

It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accuracy.

Published
2019

4. Visual-Statistical Interpretation of (18)F-FDG-PET Images for Characteristic Alzheimer Patterns in a Multicenter Study: Inter-Rater Concordance and Relationship to Automated Quantitative Evaluation

Author

Yamane, T., Ikari, Y., Nishio, T., Ishii, K., Kato, T., Ito, K., Silverman, D.H.S., Senda, M., Asada, T., Arai, H., Sugishita, M., and Iwatsubo, T.

Subjects
Male, medicine.medical_specialty, Pathology, Concordance, Disease, Audiology, Sensitivity and Specificity, Pattern Recognition, Automated, Neuroimaging, Japan, Alzheimer Disease, Artificial Intelligence, Fluorodeoxyglucose F18, Image Interpretation, Computer-Assisted, medicine, Quantitative assessment, Humans, Radiology, Nuclear Medicine and imaging, Cognitive Dysfunction, Observer Variation, business.industry, Brain, Reproducibility of Results, Cognition, Middle Aged, medicine.disease, Image Enhancement, carbohydrates (lipids), Inter-rater reliability, Multicenter study, Data Interpretation, Statistical, Positron-Emission Tomography, Female, Neurology (clinical), Alzheimer's disease, Radiopharmaceuticals, business, Algorithms
Abstract

BACKGROUND AND PURPOSE: The role of 18F-FDG-PET in the diagnosis of Alzheimer disease is increasing and should be validated. The aim of this study was to assess the inter-rater variability in the interpretation of 18F-FDG-PET images obtained in the Japanese Alzheimer's Disease Neuroimaging Initiative, a multicenter clinical research project. MATERIALS AND METHODS: This study analyzed 274 18F-FDG-PET scans (67 mild Alzheimer disease, 100 mild cognitive impairment, and 107 normal cognitive) as baseline scans for the Japanese Alzheimer's Disease Neuroimaging Initiative, which were acquired with various types of PET or PET/CT scanners in 23 facilities. Three independent raters interpreted all PET images by using a combined visual-statistical method. The images were classified into 7 (FDG-7) patterns by the criteria of Silverman et al and further into 2 (FDG-2) patterns. RESULTS: Agreement among the 7 visual-statistical categories by at least 2 of the 3 readers occurred in >94% of cases for all groups: Alzheimer disease, mild cognitive impairment, and normal cognitive. Perfect matches by all 3 raters were observed for 62% of the cases by FDG-7 and 76 by FDG-2. Inter-rater concordance was moderate by FDG-7 (κ = 0.57) and substantial in FDG-2 (κ = 0.67) on average. The FDG-PET score, an automated quantitative index developed by Herholz et al, increased as the number of raters who voted for the AD pattern increased (ρ = 0.59, P < .0001), and the FDG-PET score decreased as those for normal pattern increased (ρ = −0.64, P < .0001). CONCLUSIONS: Inter-rater agreement was moderate to substantial for the combined visual-statistical interpretation of 18F-FDG-PET and was also significantly associated with automated quantitative assessment. AD : Alzheimer disease J-ADNI : Japanese Alzheimer's Disease Neuroimaging Initiative MCI : mild cognitive impairment NC : cognitively normal subject

Published
2014

12. PET in oncology: Will it replace the other modalities?

Author

Hoh, C.K., Schiepers, C., Seltzer, M.A., Gambhir, S.S., Silverman, D.H.S., Czernin, J., Maddahi, J., and Phelps, M.E.

Abstract

Medical imaging technology is rapidly expanding and the role of each modality is being redefined constantly. PET has been around since the early sixties and gained clinical acceptance in oncology only after an extreme number of scientific publications. Although PET has the unique ability to image biochemical processes in vivo, this ability is not fully used as a clinical imaging tool. In this overview, the role of PET in relation to other tumor imaging modalities will be discussed and the reported results in the literature will be reviewed. In predicting the future of PET, technical improvements of other imaging modalities need to be dealt with. The fundamental physical principles for image formation with computed tomography (CT), ultrasound (US), magnetic resonance imaging (MRI), photon-emission tomography (PET), and single photon emission CT (SPECT) will not change. The potential variety of radiopharmaceuticals which may be developed is unlimited, however, and this provides nuclear imaging techniques with a significant advantage and adaptive features for future biologic imaging. The current applications of PET in oncology have been in characterizing tumor lesions, differentiating recurrent disease from treatment effects, staging tumors, evaluating the extent of disease, and monitoring therapy. The future developments in medicine may use the unique capabilities of PET not only in diagnostic imaging but also in molecular medicine and genetics. The articles discussed in this review were selected from a literature search covering the last 3 years, and in which comparisons of PET with conventional imaging were addressed specifically. PET studies with the glucose analogue fluorine-18-labeled deoxyglucose (FDG) have shown the ability of detecting tumor foci in a variety of histological neoplasms such as thyroid cancer, breast cancer, lymphoma, lung cancer, head and neck carcinoma colorectal cancer, ovarian carcinoma, and musculoskeletal tumors. Also, the contribution of the whole body PET (WBPET) imaging technique in diagnosis will be discussed. In the current health care environment, a successful imaging technology must not only change medical management but also demonstrate that those changes improve patient outcome

Published
1997
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13. Evaluating tumor biology and oncological disease with positron-emission tomography

Author

Silverman, D.H.S., Hoh, C.K., Seltzer, M.A., Schiepers, C., Cuan, G.S., Gambhir, S.S., Zheng, L., Czernin, J., and Phelps, M.E.

Abstract

The usefulness of positron-emission tomography (PET) for noninvasive assessment of several biological parameters of neoplastic tissue has been reviewed. Numerous radiotracers have been developed, whose particular distribution in the presence of cancer in vivo serves to distinguish medically relevant properties of the tumor cells with which they associate. That distribution is most accurately determined through use of a PET scanner, to localize and quantify the tracer molecules, in which have been incorporated positron-emitting isotopes. These tracers include hypoxia markers, receptor ligands, substrates for enzymatic modification by the products of expression of specific genes, and precursors of protein anabolism and carbohydrate catabolism. In addition, application of PET to evaluation of patients with some particular cancers has been examined, while placing special emphasis on the level of scientific rigor of the evidence underlying conclusions about appropriate use of PET in oncology.

Published
1998
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