Early identification of MCI converting to AD: a FDG PET study
Purpose Mild cognitive impairment (MCI) is a transitional pathological stage between normal ageing (NA) and Alzheimer’s disease (AD). Although subjects with MCI show a decline at different rates, some individuals remain stable or even show an improvement in their cognitive level after some years. We...
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creator | Pagani, Marco Nobili, Flavio Morbelli, Silvia Arnaldi, Dario Giuliani, Alessandro Öberg, Johanna Girtler, Nicola Brugnolo, Andrea Picco, Agnese Bauckneht, Matteo Piva, Roberta Chincarini, Andrea Sambuceti, Gianmario Jonsson, Cathrine De Carli, Fabrizio |
description | Purpose
Mild cognitive impairment (MCI) is a transitional pathological stage between normal ageing (NA) and Alzheimer’s disease (AD). Although subjects with MCI show a decline at different rates, some individuals remain stable or even show an improvement in their cognitive level after some years. We assessed the accuracy of FDG PET in discriminating MCI patients who converted to AD from those who did not.
Methods
FDG PET was performed in 42 NA subjects, 27 MCI patients who had not converted to AD at 5 years (nc-MCI; mean follow-up time 7.5 ± 1.5 years), and 95 MCI patients who converted to AD within 5 years (MCI-AD; mean conversion time 1.8 ± 1.1 years). Relative FDG uptake values in 26 meta-volumes of interest were submitted to ANCOVA and support vector machine analyses to evaluate regional differences and discrimination accuracy.
Results
The MCI-AD group showed significantly lower FDG uptake values in the temporoparietal cortex than the other two groups. FDG uptake values in the nc-MCI group were similar to those in the NA group. Support vector machine analysis discriminated nc-MCI from MCI-AD patients with an accuracy of 89% (AUC 0.91), correctly detecting 93% of the nc-MCI patients.
Conclusion
In MCI patients not converting to AD within a minimum follow-up time of 5 years and MCI patients converting within 5 years, baseline FDG PET and volume-based analysis identified those who converted with an accuracy of 89%. However, further analysis is needed in patients with amnestic MCI who convert to a dementia other than AD. |
doi_str_mv | 10.1007/s00259-017-3761-x |
format | Article |
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Mild cognitive impairment (MCI) is a transitional pathological stage between normal ageing (NA) and Alzheimer’s disease (AD). Although subjects with MCI show a decline at different rates, some individuals remain stable or even show an improvement in their cognitive level after some years. We assessed the accuracy of FDG PET in discriminating MCI patients who converted to AD from those who did not.
Methods
FDG PET was performed in 42 NA subjects, 27 MCI patients who had not converted to AD at 5 years (nc-MCI; mean follow-up time 7.5 ± 1.5 years), and 95 MCI patients who converted to AD within 5 years (MCI-AD; mean conversion time 1.8 ± 1.1 years). Relative FDG uptake values in 26 meta-volumes of interest were submitted to ANCOVA and support vector machine analyses to evaluate regional differences and discrimination accuracy.
Results
The MCI-AD group showed significantly lower FDG uptake values in the temporoparietal cortex than the other two groups. FDG uptake values in the nc-MCI group were similar to those in the NA group. Support vector machine analysis discriminated nc-MCI from MCI-AD patients with an accuracy of 89% (AUC 0.91), correctly detecting 93% of the nc-MCI patients.
Conclusion
In MCI patients not converting to AD within a minimum follow-up time of 5 years and MCI patients converting within 5 years, baseline FDG PET and volume-based analysis identified those who converted with an accuracy of 89%. However, further analysis is needed in patients with amnestic MCI who convert to a dementia other than AD.</description><identifier>ISSN: 1619-7070</identifier><identifier>EISSN: 1619-7089</identifier><identifier>DOI: 10.1007/s00259-017-3761-x</identifier><identifier>PMID: 28664464</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accuracy ; Alzheimer Disease - complications ; Alzheimer's disease ; Cardiology ; Case-Control Studies ; Cognitive ability ; Cognitive Dysfunction - complications ; Cognitive Dysfunction - diagnostic imaging ; Conversion ; Dementia disorders ; Early Diagnosis ; Female ; Fluorodeoxyglucose F18 ; Humans ; Image Processing, Computer-Assisted ; Imaging ; Male ; Medical diagnosis ; Medicine ; Medicine & Public Health ; Neurodegenerative diseases ; Nuclear Medicine ; Oncology ; Original Article ; Orthopedics ; Patients ; Positron emission tomography ; Radiology ; Regional analysis ; Support Vector Machine ; Tomography</subject><ispartof>European journal of nuclear medicine and molecular imaging, 2017-11, Vol.44 (12), p.2042-2052</ispartof><rights>Springer-Verlag GmbH Germany 2017</rights><rights>European Journal of Nuclear Medicine and Molecular Imaging is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-511d5934fa470042f6aa13d5324aad3a4aa7ac5b319e6f4152b4cd7fa312dc263</citedby><cites>FETCH-LOGICAL-c415t-511d5934fa470042f6aa13d5324aad3a4aa7ac5b319e6f4152b4cd7fa312dc263</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00259-017-3761-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00259-017-3761-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,41493,42562,51324</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28664464$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pagani, Marco</creatorcontrib><creatorcontrib>Nobili, Flavio</creatorcontrib><creatorcontrib>Morbelli, Silvia</creatorcontrib><creatorcontrib>Arnaldi, Dario</creatorcontrib><creatorcontrib>Giuliani, Alessandro</creatorcontrib><creatorcontrib>Öberg, Johanna</creatorcontrib><creatorcontrib>Girtler, Nicola</creatorcontrib><creatorcontrib>Brugnolo, Andrea</creatorcontrib><creatorcontrib>Picco, Agnese</creatorcontrib><creatorcontrib>Bauckneht, Matteo</creatorcontrib><creatorcontrib>Piva, Roberta</creatorcontrib><creatorcontrib>Chincarini, Andrea</creatorcontrib><creatorcontrib>Sambuceti, Gianmario</creatorcontrib><creatorcontrib>Jonsson, Cathrine</creatorcontrib><creatorcontrib>De Carli, Fabrizio</creatorcontrib><title>Early identification of MCI converting to AD: a FDG PET study</title><title>European journal of nuclear medicine and molecular imaging</title><addtitle>Eur J Nucl Med Mol Imaging</addtitle><addtitle>Eur J Nucl Med Mol Imaging</addtitle><description>Purpose
Mild cognitive impairment (MCI) is a transitional pathological stage between normal ageing (NA) and Alzheimer’s disease (AD). Although subjects with MCI show a decline at different rates, some individuals remain stable or even show an improvement in their cognitive level after some years. We assessed the accuracy of FDG PET in discriminating MCI patients who converted to AD from those who did not.
Methods
FDG PET was performed in 42 NA subjects, 27 MCI patients who had not converted to AD at 5 years (nc-MCI; mean follow-up time 7.5 ± 1.5 years), and 95 MCI patients who converted to AD within 5 years (MCI-AD; mean conversion time 1.8 ± 1.1 years). Relative FDG uptake values in 26 meta-volumes of interest were submitted to ANCOVA and support vector machine analyses to evaluate regional differences and discrimination accuracy.
Results
The MCI-AD group showed significantly lower FDG uptake values in the temporoparietal cortex than the other two groups. FDG uptake values in the nc-MCI group were similar to those in the NA group. Support vector machine analysis discriminated nc-MCI from MCI-AD patients with an accuracy of 89% (AUC 0.91), correctly detecting 93% of the nc-MCI patients.
Conclusion
In MCI patients not converting to AD within a minimum follow-up time of 5 years and MCI patients converting within 5 years, baseline FDG PET and volume-based analysis identified those who converted with an accuracy of 89%. However, further analysis is needed in patients with amnestic MCI who convert to a dementia other than AD.</description><subject>Accuracy</subject><subject>Alzheimer Disease - complications</subject><subject>Alzheimer's disease</subject><subject>Cardiology</subject><subject>Case-Control Studies</subject><subject>Cognitive ability</subject><subject>Cognitive Dysfunction - complications</subject><subject>Cognitive Dysfunction - diagnostic imaging</subject><subject>Conversion</subject><subject>Dementia disorders</subject><subject>Early Diagnosis</subject><subject>Female</subject><subject>Fluorodeoxyglucose F18</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Imaging</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neurodegenerative diseases</subject><subject>Nuclear Medicine</subject><subject>Oncology</subject><subject>Original Article</subject><subject>Orthopedics</subject><subject>Patients</subject><subject>Positron emission tomography</subject><subject>Radiology</subject><subject>Regional analysis</subject><subject>Support Vector Machine</subject><subject>Tomography</subject><issn>1619-7070</issn><issn>1619-7089</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kF1LwzAUhoMoTqc_wBsJeONNNSdfbQUvxr4cTPRiXoesTUbH1s6kle3fm9E5RPAmJ3Ce9014ELoB8gCExI-eECrSiEAcsVhCtD1BFyAhjWKSpKfHe0w66NL7JSGQ0CQ9Rx2aSMm55BfoeajdaoeL3JR1YYtM10VV4sri1_4EZ1X5ZVxdlAtcV7g3eMIajwZj_D6cYV83-e4KnVm98ub6MLvoYzSc9V-i6dt40u9No4yDqCMBkIuUcat5TAinVmoNLBeMcq1zpsMZ60zMGaRG2hChc57lsdUMaJ5Rybrovu3duOqzMb5W68JnZrXSpakaryAFwTijlAT07g-6rBpXht8FKgEpGGEsUNBSmau8d8aqjSvW2u0UELV3q1q3KrhVe7dqGzK3h-Zmvjb5MfEjMwC0BXxYlQvjfj39b-s3if6BTw</recordid><startdate>20171101</startdate><enddate>20171101</enddate><creator>Pagani, Marco</creator><creator>Nobili, Flavio</creator><creator>Morbelli, Silvia</creator><creator>Arnaldi, Dario</creator><creator>Giuliani, Alessandro</creator><creator>Öberg, Johanna</creator><creator>Girtler, Nicola</creator><creator>Brugnolo, Andrea</creator><creator>Picco, Agnese</creator><creator>Bauckneht, Matteo</creator><creator>Piva, Roberta</creator><creator>Chincarini, Andrea</creator><creator>Sambuceti, Gianmario</creator><creator>Jonsson, Cathrine</creator><creator>De Carli, Fabrizio</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope></search><sort><creationdate>20171101</creationdate><title>Early identification of MCI converting to AD: a FDG PET study</title><author>Pagani, Marco ; Nobili, Flavio ; Morbelli, Silvia ; Arnaldi, Dario ; Giuliani, Alessandro ; Öberg, Johanna ; Girtler, Nicola ; Brugnolo, Andrea ; Picco, Agnese ; Bauckneht, Matteo ; Piva, Roberta ; Chincarini, Andrea ; Sambuceti, Gianmario ; Jonsson, Cathrine ; De Carli, Fabrizio</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c415t-511d5934fa470042f6aa13d5324aad3a4aa7ac5b319e6f4152b4cd7fa312dc263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Accuracy</topic><topic>Alzheimer Disease - complications</topic><topic>Alzheimer's disease</topic><topic>Cardiology</topic><topic>Case-Control Studies</topic><topic>Cognitive ability</topic><topic>Cognitive Dysfunction - complications</topic><topic>Cognitive Dysfunction - diagnostic imaging</topic><topic>Conversion</topic><topic>Dementia disorders</topic><topic>Early Diagnosis</topic><topic>Female</topic><topic>Fluorodeoxyglucose F18</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted</topic><topic>Imaging</topic><topic>Male</topic><topic>Medical diagnosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neurodegenerative diseases</topic><topic>Nuclear Medicine</topic><topic>Oncology</topic><topic>Original Article</topic><topic>Orthopedics</topic><topic>Patients</topic><topic>Positron emission tomography</topic><topic>Radiology</topic><topic>Regional analysis</topic><topic>Support Vector Machine</topic><topic>Tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pagani, Marco</creatorcontrib><creatorcontrib>Nobili, Flavio</creatorcontrib><creatorcontrib>Morbelli, Silvia</creatorcontrib><creatorcontrib>Arnaldi, Dario</creatorcontrib><creatorcontrib>Giuliani, Alessandro</creatorcontrib><creatorcontrib>Öberg, Johanna</creatorcontrib><creatorcontrib>Girtler, Nicola</creatorcontrib><creatorcontrib>Brugnolo, Andrea</creatorcontrib><creatorcontrib>Picco, Agnese</creatorcontrib><creatorcontrib>Bauckneht, Matteo</creatorcontrib><creatorcontrib>Piva, Roberta</creatorcontrib><creatorcontrib>Chincarini, Andrea</creatorcontrib><creatorcontrib>Sambuceti, Gianmario</creatorcontrib><creatorcontrib>Jonsson, Cathrine</creatorcontrib><creatorcontrib>De Carli, Fabrizio</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>European journal of nuclear medicine and molecular imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pagani, Marco</au><au>Nobili, Flavio</au><au>Morbelli, Silvia</au><au>Arnaldi, Dario</au><au>Giuliani, Alessandro</au><au>Öberg, Johanna</au><au>Girtler, Nicola</au><au>Brugnolo, Andrea</au><au>Picco, Agnese</au><au>Bauckneht, Matteo</au><au>Piva, Roberta</au><au>Chincarini, Andrea</au><au>Sambuceti, Gianmario</au><au>Jonsson, Cathrine</au><au>De Carli, Fabrizio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Early identification of MCI converting to AD: a FDG PET study</atitle><jtitle>European journal of nuclear medicine and molecular imaging</jtitle><stitle>Eur J Nucl Med Mol Imaging</stitle><addtitle>Eur J Nucl Med Mol Imaging</addtitle><date>2017-11-01</date><risdate>2017</risdate><volume>44</volume><issue>12</issue><spage>2042</spage><epage>2052</epage><pages>2042-2052</pages><issn>1619-7070</issn><eissn>1619-7089</eissn><abstract>Purpose
Mild cognitive impairment (MCI) is a transitional pathological stage between normal ageing (NA) and Alzheimer’s disease (AD). Although subjects with MCI show a decline at different rates, some individuals remain stable or even show an improvement in their cognitive level after some years. We assessed the accuracy of FDG PET in discriminating MCI patients who converted to AD from those who did not.
Methods
FDG PET was performed in 42 NA subjects, 27 MCI patients who had not converted to AD at 5 years (nc-MCI; mean follow-up time 7.5 ± 1.5 years), and 95 MCI patients who converted to AD within 5 years (MCI-AD; mean conversion time 1.8 ± 1.1 years). Relative FDG uptake values in 26 meta-volumes of interest were submitted to ANCOVA and support vector machine analyses to evaluate regional differences and discrimination accuracy.
Results
The MCI-AD group showed significantly lower FDG uptake values in the temporoparietal cortex than the other two groups. FDG uptake values in the nc-MCI group were similar to those in the NA group. Support vector machine analysis discriminated nc-MCI from MCI-AD patients with an accuracy of 89% (AUC 0.91), correctly detecting 93% of the nc-MCI patients.
Conclusion
In MCI patients not converting to AD within a minimum follow-up time of 5 years and MCI patients converting within 5 years, baseline FDG PET and volume-based analysis identified those who converted with an accuracy of 89%. However, further analysis is needed in patients with amnestic MCI who convert to a dementia other than AD.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>28664464</pmid><doi>10.1007/s00259-017-3761-x</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Alzheimer Disease - complications Alzheimer's disease Cardiology Case-Control Studies Cognitive ability Cognitive Dysfunction - complications Cognitive Dysfunction - diagnostic imaging Conversion Dementia disorders Early Diagnosis Female Fluorodeoxyglucose F18 Humans Image Processing, Computer-Assisted Imaging Male Medical diagnosis Medicine Medicine & Public Health Neurodegenerative diseases Nuclear Medicine Oncology Original Article Orthopedics Patients Positron emission tomography Radiology Regional analysis Support Vector Machine Tomography |
title | Early identification of MCI converting to AD: a FDG PET study |
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