Classification of amyloid-positivity in controls: Comparison of visual read and quantitative approaches

An important research application of amyloid imaging with positron emission tomography (PET) is detection of the earliest evidence of fibrillar amyloid-beta (Aβ) deposition. Use of amyloid PET for this purpose, requires a reproducible method for defining a cutoff that separates individuals with no s...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Jg. 71; S. 207 - 215
Hauptverfasser:	Cohen, Ann D., Mowrey, Wenzhu, Weissfeld, Lisa A., Aizenstein, Howard J., McDade, Eric, Mountz, James M., Nebes, Robert D., Saxton, Judith A., Snitz, Beth, DeKosky, Steven, Williamson, Jeff, Lopez, Oscar L., Price, Julie C., Mathis, Chester A., Klunk, William E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.05.2013 Elsevier Limited
Schlagworte:	Adult Aged Agreements Alzheimer's disease Amyloid Amyloid - analysis Amyloidosis - diagnostic imaging Aniline Compounds Brain - diagnostic imaging Brain - pathology Cluster Analysis Humans Methods Middle Aged Pittsburgh compound B Positron emission tomography Positron-Emission Tomography - methods Radiopharmaceuticals Retention Studies Thiazoles Visual read Cluster analysis Amyloid Visual read Positron emission tomography Pittsburgh compound B
ISSN:	1053-8119, 1095-9572, 1095-9572
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Abstract	An important research application of amyloid imaging with positron emission tomography (PET) is detection of the earliest evidence of fibrillar amyloid-beta (Aβ) deposition. Use of amyloid PET for this purpose, requires a reproducible method for defining a cutoff that separates individuals with no significant Aβ deposition from those in which Aβ deposition has begun. We previously reported the iterative outlier approach (IO) for the analysis of Pittsburgh Compound-B (PiB) PET data. Developments in amyloid imaging since the initial report of IO have led us to re-examine the generalizability of this method. IO was developed using full-dynamic atrophy-corrected PiB PET data obtained from a group of control subjects with a fairly distinct separation between PiB-positive [PiB(+)] and PiB-negative [PiB(−)] subjects. We tested the performance of IO using late-summed tissue ratio data with atrophy correction or with an automated template method without atrophy correction and tested the robustness of the method when applied to a cohort of older subjects in which separation between PiB(+) and PiB(−) subjects was not so distinct. The IO method did not perform consistently across analyses and performed particularly poorly when separation was less clear. We found that a sparse k-means (SKM) cluster analysis approach performed significantly better; performing more consistently across methods and subject cohorts. We also compared SKM to a consensus visual read approach and found very good correspondence. The visual read and SKM methods, applied together, may optimize the identification of early Aβ deposition. These methods have the potential to provide a standard approach to the detection of PiB-positivity that is generalizable across centers. ► Limitations of the iterative outlier method for Aβ-positive cutoffs ► Use of sparse k-means as a method for identification of Aβ-positive cutoffs ► Comparison of the above objective methods to visual reads shows good agreement. ► A combination of methods may be the best approach to identify early Aβ.
AbstractList	An important research application of amyloid imaging with positron emission tomography (PET) is detection of the earliest evidence of fibrillar amyloid-beta (Aβ) deposition. Use of amyloid PET for this purpose, requires a reproducible method for defining a cutoff that separates individuals with no significant Aβ deposition from those in which Aβ deposition has begun. We previously reported the iterative outlier approach (IO) for the analysis of Pittsburgh Compound-B (PiB) PET data. Developments in amyloid imaging since the initial report of IO have led us to re-examine the generalizability of this method. IO was developed using full-dynamic atrophy-corrected PiB PET data obtained from a group of control subjects with a fairly distinct separation between PiB-positive [PiB(+)] and PiB-negative [PiB(-)] subjects. We tested the performance of IO using late-summed tissue ratio data with atrophy correction or with an automated template method without atrophy correction and tested the robustness of the method when applied to a cohort of older subjects in which separation between PiB(+) and PiB(-) subjects was not so distinct. The IO method did not perform consistently across analyses and performed particularly poorly when separation was less clear. We found that a sparse k-means (SKM) cluster analysis approach performed significantly better; performing more consistently across methods and subject cohorts. We also compared SKM to a consensus visual read approach and found very good correspondence. The visual read and SKM methods, applied together, may optimize the identification of early Aβ deposition. These methods have the potential to provide a standard approach to the detection of PiB-positivity that is generalizable across centers. An important research application of amyloid imaging with positron emission tomography (PET) is detection of the earliest evidence of fibrillar amyloid-beta (Aβ) deposition. Use of amyloid PET for this purpose, requires a reproducible method for defining a cutoff that separates individuals with no significant Aβ deposition from those in which Aβ deposition has begun. We previously reported the iterative outlier approach (IO) for the analysis of Pittsburgh Compound-B (PiB) PET data. Developments in amyloid imaging since the initial report of IO have led us to re-examine the generalizability of this method. IO was developed using full-dynamic atrophy-corrected PiB PET data obtained from a group of control subjects with a fairly distinct separation between PiB-positive [PiB(+)] and PiB-negative [PiB(−)] subjects. An important research application of amyloid imaging with positron emission tomography (PET) is detection of the earliest evidence of fibrillar amyloid-beta (Aβ) deposition. Use of amyloid PET for this purpose, requires a reproducible method for defining a cutoff that separates individuals with no significant Aβ deposition from those in which Aβ deposition has begun. We previously reported the iterative outlier approach (IO) for the analysis of Pittsburgh Compound-B (PiB) PET data. Developments in amyloid imaging since the initial report of IO have led us to re-examine the generalizability of this method. IO was developed using full-dynamic atrophy-corrected PiB PET data obtained from a group of control subjects with a fairly distinct separation between PiB-positive [PiB(+)] and PiB-negative [PiB(−)] subjects. We tested the performance of IO using late-summed tissue ratio data with atrophy correction or with an automated template method without atrophy correction and tested the robustness of the method when applied to a cohort of older subjects in which separation between PiB(+) and PiB(−) subjects was not so distinct. The IO method did not perform consistently across analyses and performed particularly poorly when separation was less clear. We found that a sparse k-means (SKM) cluster analysis approach performed significantly better; performing more consistently across methods and subject cohorts. We also compared SKM to a consensus visual read approach and found very good correspondence. The visual read and SKM methods, applied together, may optimize the identification of early Aβ deposition. These methods have the potential to provide a standard approach to the detection of PiB-positivity that is generalizable across centers. ► Limitations of the iterative outlier method for Aβ-positive cutoffs ► Use of sparse k-means as a method for identification of Aβ-positive cutoffs ► Comparison of the above objective methods to visual reads shows good agreement. ► A combination of methods may be the best approach to identify early Aβ. An important research application of amyloid imaging with positron emission tomography (PET) is detection of the earliest evidence of fibrillar amyloid-beta (Aβ) deposition. Use of amyloid PET for this purpose, requires a reproducible method for defining a cutoff that separates individuals with no significant Aβ deposition from those in which Aβ deposition has begun. We previously reported the iterative outlier approach (IO) for the analysis of Pittsburgh Compound-B (PiB) PET data. Developments in amyloid imaging since the initial report of IO have led us to re-examine the generalizability of this method. IO was developed using full-dynamic atrophy-corrected PiB PET data obtained from a group of control subjects with a fairly distinct separation between PiB-positive [PiB(+)] and PiB-negative [PiB(-)] subjects. Methods We tested the performance of IO using late-summed tissue ratio data with atrophy correction or with an automated template method without atrophy correction and tested the robustness of the method when applied to a cohort of older subjects in which separation between PiB(+) and PiB(-) subjects was not so distinct. Results The IO method did not perform consistently across analyses and performed particularly poorly when separation was less clear. We found that a sparse k-means (SKM) cluster analysis approach performed significantly better; performing more consistently across methods and subject cohorts. We also compared SKM to a consensus visual read approach and found very good correspondence. Conclusion The visual read and SKM methods, applied together, may optimize the identification of early Aβ deposition. These methods have the potential to provide a standard approach to the detection of PiB-positivity that is generalizable across centers. An important research application of amyloid imaging with positron emission tomography (PET) is detection of the earliest evidence of fibrillar amyloid-beta (Aβ) deposition. Use of amyloid PET for this purpose, requires a reproducible method for defining a cutoff that separates individuals with no significant Aβ deposition from those in which Aβ deposition has begun. We previously reported the iterative outlier approach (IO) for the analysis of Pittsburgh Compound-B (PiB) PET data. Developments in amyloid imaging since the initial report of IO have led us to re-examine the generalizability of this method. IO was developed using full-dynamic atrophy-corrected PiB PET data obtained from a group of control subjects with a fairly distinct separation between PiB-positive [PiB(+)] and PiB-negative [PiB(-)] subjects.UNLABELLEDAn important research application of amyloid imaging with positron emission tomography (PET) is detection of the earliest evidence of fibrillar amyloid-beta (Aβ) deposition. Use of amyloid PET for this purpose, requires a reproducible method for defining a cutoff that separates individuals with no significant Aβ deposition from those in which Aβ deposition has begun. We previously reported the iterative outlier approach (IO) for the analysis of Pittsburgh Compound-B (PiB) PET data. Developments in amyloid imaging since the initial report of IO have led us to re-examine the generalizability of this method. IO was developed using full-dynamic atrophy-corrected PiB PET data obtained from a group of control subjects with a fairly distinct separation between PiB-positive [PiB(+)] and PiB-negative [PiB(-)] subjects.We tested the performance of IO using late-summed tissue ratio data with atrophy correction or with an automated template method without atrophy correction and tested the robustness of the method when applied to a cohort of older subjects in which separation between PiB(+) and PiB(-) subjects was not so distinct.METHODSWe tested the performance of IO using late-summed tissue ratio data with atrophy correction or with an automated template method without atrophy correction and tested the robustness of the method when applied to a cohort of older subjects in which separation between PiB(+) and PiB(-) subjects was not so distinct.The IO method did not perform consistently across analyses and performed particularly poorly when separation was less clear. We found that a sparse k-means (SKM) cluster analysis approach performed significantly better; performing more consistently across methods and subject cohorts. We also compared SKM to a consensus visual read approach and found very good correspondence.RESULTSThe IO method did not perform consistently across analyses and performed particularly poorly when separation was less clear. We found that a sparse k-means (SKM) cluster analysis approach performed significantly better; performing more consistently across methods and subject cohorts. We also compared SKM to a consensus visual read approach and found very good correspondence.The visual read and SKM methods, applied together, may optimize the identification of early Aβ deposition. These methods have the potential to provide a standard approach to the detection of PiB-positivity that is generalizable across centers.CONCLUSIONThe visual read and SKM methods, applied together, may optimize the identification of early Aβ deposition. These methods have the potential to provide a standard approach to the detection of PiB-positivity that is generalizable across centers. An important research application of amyloid imaging with positron emission tomography (PET) is detection of the earliest evidence of fibrillar amyloid-beta (AI2) deposition. Use of amyloid PET for this purpose, requires a reproducible method for defining a cutoff that separates individuals with no significant AI2 deposition from those in which AI2 deposition has begun. We previously reported the iterative outlier approach (IO) for the analysis of Pittsburgh Compound-B (PiB) PET data. Developments in amyloid imaging since the initial report of IO have led us to re-examine the generalizability of this method. IO was developed using full-dynamic atrophy-corrected PiB PET data obtained from a group of control subjects with a fairly distinct separation between PiB-positive [PiB(+)] and PiB-negative [PiB(a)] subjects.
Author	Cohen, Ann D. Snitz, Beth DeKosky, Steven Aizenstein, Howard J. Williamson, Jeff Klunk, William E. Lopez, Oscar L. Mathis, Chester A. Nebes, Robert D. Price, Julie C. Weissfeld, Lisa A. Mowrey, Wenzhu McDade, Eric Saxton, Judith A. Mountz, James M.
AuthorAffiliation	1 Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213 6 Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213 5 Sticht Center on Aging, Wake Forest University Baptist Medical Center, Winston-Salem, NC, 27157 2 Department of Biostatistics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213 3 Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213 4 Office of the Dean and Department of Neurology, University of Virginia, Charlottesville, VA, 22908
AuthorAffiliation_xml	– name: 5 Sticht Center on Aging, Wake Forest University Baptist Medical Center, Winston-Salem, NC, 27157 – name: 3 Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213 – name: 4 Office of the Dean and Department of Neurology, University of Virginia, Charlottesville, VA, 22908 – name: 1 Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213 – name: 2 Department of Biostatistics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213 – name: 6 Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213
Author_xml	– sequence: 1 givenname: Ann D. surname: Cohen fullname: Cohen, Ann D. email: cohenad@upmc.edu organization: Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA – sequence: 2 givenname: Wenzhu surname: Mowrey fullname: Mowrey, Wenzhu organization: Department of Biostatistics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA – sequence: 3 givenname: Lisa A. surname: Weissfeld fullname: Weissfeld, Lisa A. organization: Department of Biostatistics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA – sequence: 4 givenname: Howard J. surname: Aizenstein fullname: Aizenstein, Howard J. organization: Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA – sequence: 5 givenname: Eric surname: McDade fullname: McDade, Eric organization: Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA – sequence: 6 givenname: James M. surname: Mountz fullname: Mountz, James M. organization: Office of the Dean and Department of Neurology, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 7 givenname: Robert D. surname: Nebes fullname: Nebes, Robert D. organization: Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA – sequence: 8 givenname: Judith A. surname: Saxton fullname: Saxton, Judith A. organization: Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA – sequence: 9 givenname: Beth surname: Snitz fullname: Snitz, Beth organization: Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA – sequence: 10 givenname: Steven surname: DeKosky fullname: DeKosky, Steven organization: Office of the Dean and Department of Neurology, University of Virginia, Charlottesville, VA, 22908, USA – sequence: 11 givenname: Jeff surname: Williamson fullname: Williamson, Jeff organization: Sticht Center on Aging, Wake Forest University Baptist Medical Center, Winston-Salem, NC, 27157, USA – sequence: 12 givenname: Oscar L. surname: Lopez fullname: Lopez, Oscar L. organization: Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA – sequence: 13 givenname: Julie C. surname: Price fullname: Price, Julie C. organization: Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA – sequence: 14 givenname: Chester A. surname: Mathis fullname: Mathis, Chester A. organization: Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA – sequence: 15 givenname: William E. surname: Klunk fullname: Klunk, William E. organization: Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
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ContentType	Journal Article
Copyright	2013 Elsevier Inc. Copyright © 2013 Elsevier Inc. All rights reserved. Copyright Elsevier Limited May 1, 2013 2012 Elsevier Inc. All rights reserved. 2012
Copyright_xml	– notice: 2013 Elsevier Inc. – notice: Copyright © 2013 Elsevier Inc. All rights reserved. – notice: Copyright Elsevier Limited May 1, 2013 – notice: 2012 Elsevier Inc. All rights reserved. 2012
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Keywords	Cluster analysis Amyloid Visual read Positron emission tomography Pittsburgh compound B
Language	English
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SubjectTerms	Adult Aged Agreements Alzheimer's disease Amyloid Amyloid - analysis Amyloidosis - diagnostic imaging Aniline Compounds Brain - diagnostic imaging Brain - pathology Cluster Analysis Humans Methods Middle Aged Pittsburgh compound B Positron emission tomography Positron-Emission Tomography - methods Radiopharmaceuticals Retention Studies Thiazoles Visual read
Title	Classification of amyloid-positivity in controls: Comparison of visual read and quantitative approaches
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