Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.
Identifieur interne : 000195 ( PubMed/Corpus ); précédent : 000194; suivant : 000196Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.
Auteurs : C. Salvatore ; A. Cerasa ; I. Castiglioni ; F. Gallivanone ; A. Augimeri ; M. Lopez ; G. Arabia ; M. Morelli ; M C Gilardi ; A. QuattroneSource :
- Journal of neuroscience methods ; 2014.
English descriptors
- KwdEn :
- Aged, Algorithms, Artificial Intelligence, Brain (pathology), Corpus Callosum (pathology), Diagnosis, Differential, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Mesencephalon (pathology), Parkinson Disease (diagnosis), Parkinson Disease (pathology), Pons (pathology), Principal Component Analysis, Retrospective Studies, Sensitivity and Specificity, Support Vector Machine, Supranuclear Palsy, Progressive (diagnosis), Supranuclear Palsy, Progressive (pathology), Thalamus (pathology).
- MESH :
- diagnosis : Parkinson Disease, Supranuclear Palsy, Progressive.
- pathology : Brain, Corpus Callosum, Mesencephalon, Parkinson Disease, Pons, Supranuclear Palsy, Progressive, Thalamus.
- Aged, Algorithms, Artificial Intelligence, Diagnosis, Differential, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Principal Component Analysis, Retrospective Studies, Sensitivity and Specificity, Support Vector Machine.
Abstract
Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP).
DOI: 10.1016/j.jneumeth.2013.11.016
PubMed: 24286700
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pubmed:24286700Le document en format XML
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Castiglioni</name><affiliation><nlm:affiliation>Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), via F.lli Cervi 93, 20090 Segrate, MI, Italy. Electronic address: isabella.castiglioni@ibfm.cnr.it.</nlm:affiliation></affiliation></author><author><name sortKey="Gallivanone, F" sort="Gallivanone, F" uniqKey="Gallivanone F" first="F" last="Gallivanone">F. Gallivanone</name><affiliation><nlm:affiliation>Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), via F.lli Cervi 93, 20090 Segrate, MI, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Augimeri, A" sort="Augimeri, A" uniqKey="Augimeri A" first="A" last="Augimeri">A. Augimeri</name><affiliation><nlm:affiliation>Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Germaneto, CZ, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Lopez, M" sort="Lopez, M" uniqKey="Lopez M" first="M" last="Lopez">M. Lopez</name><affiliation><nlm:affiliation>DITEN, University of Genoa, Via Opera Pia 11A, 16145 Genoa, Italy. Electronic address: miriamlp@ugr.es.</nlm:affiliation></affiliation></author><author><name sortKey="Arabia, G" sort="Arabia, G" uniqKey="Arabia G" first="G" last="Arabia">G. Arabia</name><affiliation><nlm:affiliation>Institute of Neurology, University "Magna Graecia", Germaneto, CZ, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Morelli, M" sort="Morelli, M" uniqKey="Morelli M" first="M" last="Morelli">M. Morelli</name><affiliation><nlm:affiliation>Institute of Neurology, University "Magna Graecia", Germaneto, CZ, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Gilardi, M C" sort="Gilardi, M C" uniqKey="Gilardi M" first="M C" last="Gilardi">M C Gilardi</name><affiliation><nlm:affiliation>Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), via F.lli Cervi 93, 20090 Segrate, MI, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Quattrone, A" sort="Quattrone, A" uniqKey="Quattrone A" first="A" last="Quattrone">A. Quattrone</name><affiliation><nlm:affiliation>Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Germaneto, CZ, Italy; Institute of Neurology, University "Magna Graecia", Germaneto, CZ, Italy.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2014">2014</date><idno type="RBID">pubmed:24286700</idno><idno type="pmid">24286700</idno><idno type="doi">10.1016/j.jneumeth.2013.11.016</idno><idno type="wicri:Area/PubMed/Corpus">000195</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.</title><author><name sortKey="Salvatore, C" sort="Salvatore, C" uniqKey="Salvatore C" first="C" last="Salvatore">C. Salvatore</name><affiliation><nlm:affiliation>Department of Physics, University of Milan - Bicocca, Piazza della Scienza 3, 20126 Milan, Italy. Electronic address: christian.salvatore@unimib.it.</nlm:affiliation></affiliation></author><author><name sortKey="Cerasa, A" sort="Cerasa, A" uniqKey="Cerasa A" first="A" last="Cerasa">A. Cerasa</name><affiliation><nlm:affiliation>Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Germaneto, CZ, Italy. Electronic address: a.cerasa@unicz.it.</nlm:affiliation></affiliation></author><author><name sortKey="Castiglioni, I" sort="Castiglioni, I" uniqKey="Castiglioni I" first="I" last="Castiglioni">I. Castiglioni</name><affiliation><nlm:affiliation>Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), via F.lli Cervi 93, 20090 Segrate, MI, Italy. Electronic address: isabella.castiglioni@ibfm.cnr.it.</nlm:affiliation></affiliation></author><author><name sortKey="Gallivanone, F" sort="Gallivanone, F" uniqKey="Gallivanone F" first="F" last="Gallivanone">F. Gallivanone</name><affiliation><nlm:affiliation>Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), via F.lli Cervi 93, 20090 Segrate, MI, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Augimeri, A" sort="Augimeri, A" uniqKey="Augimeri A" first="A" last="Augimeri">A. Augimeri</name><affiliation><nlm:affiliation>Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Germaneto, CZ, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Lopez, M" sort="Lopez, M" uniqKey="Lopez M" first="M" last="Lopez">M. Lopez</name><affiliation><nlm:affiliation>DITEN, University of Genoa, Via Opera Pia 11A, 16145 Genoa, Italy. Electronic address: miriamlp@ugr.es.</nlm:affiliation></affiliation></author><author><name sortKey="Arabia, G" sort="Arabia, G" uniqKey="Arabia G" first="G" last="Arabia">G. Arabia</name><affiliation><nlm:affiliation>Institute of Neurology, University "Magna Graecia", Germaneto, CZ, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Morelli, M" sort="Morelli, M" uniqKey="Morelli M" first="M" last="Morelli">M. Morelli</name><affiliation><nlm:affiliation>Institute of Neurology, University "Magna Graecia", Germaneto, CZ, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Gilardi, M C" sort="Gilardi, M C" uniqKey="Gilardi M" first="M C" last="Gilardi">M C Gilardi</name><affiliation><nlm:affiliation>Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), via F.lli Cervi 93, 20090 Segrate, MI, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Quattrone, A" sort="Quattrone, A" uniqKey="Quattrone A" first="A" last="Quattrone">A. Quattrone</name><affiliation><nlm:affiliation>Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Germaneto, CZ, Italy; Institute of Neurology, University "Magna Graecia", Germaneto, CZ, Italy.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Journal of neuroscience methods</title><idno type="e-ISSN">1872-678X</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged</term><term>Algorithms</term><term>Artificial Intelligence</term><term>Brain (pathology)</term><term>Corpus Callosum (pathology)</term><term>Diagnosis, Differential</term><term>Female</term><term>Humans</term><term>Image Interpretation, Computer-Assisted</term><term>Magnetic Resonance Imaging</term><term>Male</term><term>Mesencephalon (pathology)</term><term>Parkinson Disease (diagnosis)</term><term>Parkinson Disease (pathology)</term><term>Pons (pathology)</term><term>Principal Component Analysis</term><term>Retrospective Studies</term><term>Sensitivity and Specificity</term><term>Support Vector Machine</term><term>Supranuclear Palsy, Progressive (diagnosis)</term><term>Supranuclear Palsy, Progressive (pathology)</term><term>Thalamus (pathology)</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Parkinson Disease</term><term>Supranuclear Palsy, Progressive</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Brain</term><term>Corpus Callosum</term><term>Mesencephalon</term><term>Parkinson Disease</term><term>Pons</term><term>Supranuclear Palsy, Progressive</term><term>Thalamus</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Algorithms</term><term>Artificial Intelligence</term><term>Diagnosis, Differential</term><term>Female</term><term>Humans</term><term>Image Interpretation, Computer-Assisted</term><term>Magnetic Resonance Imaging</term><term>Male</term><term>Principal Component Analysis</term><term>Retrospective Studies</term><term>Sensitivity and Specificity</term><term>Support Vector Machine</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP).</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">24286700</PMID><DateCreated><Year>2014</Year><Month>01</Month><Day>09</Day></DateCreated><DateCompleted><Year>2014</Year><Month>10</Month><Day>06</Day></DateCompleted><DateRevised><Year>2015</Year><Month>11</Month><Day>19</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1872-678X</ISSN><JournalIssue CitedMedium="Internet"><Volume>222</Volume><PubDate><Year>2014</Year><Month>Jan</Month><Day>30</Day></PubDate></JournalIssue><Title>Journal of neuroscience methods</Title><ISOAbbreviation>J. Neurosci. Methods</ISOAbbreviation></Journal><ArticleTitle>Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.</ArticleTitle><Pagination><MedlinePgn>230-7</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.jneumeth.2013.11.016</ELocationID><ELocationID EIdType="pii" ValidYN="Y">S0165-0270(13)00399-3</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP).</AbstractText><AbstractText Label="METHOD" NlmCategory="METHODS">Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP.</AbstractText><AbstractText Label="COMPARISON WITH EXISTING METHODS" NlmCategory="METHODS">Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice.</AbstractText><CopyrightInformation>Copyright © 2013 Elsevier B.V. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Salvatore</LastName><ForeName>C</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Department of Physics, University of Milan - Bicocca, Piazza della Scienza 3, 20126 Milan, Italy. Electronic address: christian.salvatore@unimib.it.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cerasa</LastName><ForeName>A</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Germaneto, CZ, Italy. Electronic address: a.cerasa@unicz.it.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Castiglioni</LastName><ForeName>I</ForeName><Initials>I</Initials><AffiliationInfo><Affiliation>Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), via F.lli Cervi 93, 20090 Segrate, MI, Italy. Electronic address: isabella.castiglioni@ibfm.cnr.it.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gallivanone</LastName><ForeName>F</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), via F.lli Cervi 93, 20090 Segrate, MI, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Augimeri</LastName><ForeName>A</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Germaneto, CZ, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lopez</LastName><ForeName>M</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>DITEN, University of Genoa, Via Opera Pia 11A, 16145 Genoa, Italy. Electronic address: miriamlp@ugr.es.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Arabia</LastName><ForeName>G</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Institute of Neurology, University "Magna Graecia", Germaneto, CZ, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Morelli</LastName><ForeName>M</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Institute of Neurology, University "Magna Graecia", Germaneto, CZ, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gilardi</LastName><ForeName>M C</ForeName><Initials>MC</Initials><AffiliationInfo><Affiliation>Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), via F.lli Cervi 93, 20090 Segrate, MI, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Quattrone</LastName><ForeName>A</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Germaneto, CZ, Italy; Institute of Neurology, University "Magna Graecia", Germaneto, CZ, Italy.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D003160">Comparative Study</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>11</Month><Day>26</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>J Neurosci Methods</MedlineTA><NlmUniqueID>7905558</NlmUniqueID><ISSNLinking>0165-0270</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000368">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D001185">Artificial Intelligence</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001921">Brain</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000473">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D003337">Corpus Callosum</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000473">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D003937">Diagnosis, Differential</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D005260">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D007090">Image Interpretation, Computer-Assisted</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D008279">Magnetic Resonance Imaging</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D008297">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D008636">Mesencephalon</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000473">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D010300">Parkinson Disease</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000175">diagnosis</QualifierName><QualifierName MajorTopicYN="N" UI="Q000473">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D011149">Pons</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000473">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D025341">Principal Component Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012189">Retrospective Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012680">Sensitivity and Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D060388">Support Vector Machine</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D013494">Supranuclear Palsy, Progressive</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000175">diagnosis</QualifierName><QualifierName MajorTopicYN="N" UI="Q000473">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D013788">Thalamus</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000473">pathology</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Machine learning</Keyword><Keyword MajorTopicYN="N">Magnetic resonance imaging (MRI)</Keyword><Keyword MajorTopicYN="N">Parkinson's disease (PD)</Keyword><Keyword MajorTopicYN="N">Progressive Supranuclear Palsy (PSP)</Keyword><Keyword MajorTopicYN="N">Support Vector Machine (SVM)</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2013</Year><Month>7</Month><Day>1</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2013</Year><Month>11</Month><Day>14</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2013</Year><Month>11</Month><Day>17</Day></PubMedPubDate><PubMedPubDate PubStatus="aheadofprint"><Year>2013</Year><Month>11</Month><Day>26</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2013</Year><Month>11</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2013</Year><Month>11</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>10</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">24286700</ArticleId><ArticleId IdType="pii">S0165-0270(13)00399-3</ArticleId><ArticleId IdType="doi">10.1016/j.jneumeth.2013.11.016</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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