Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments
Identifieur interne : 000A47 ( Pmc/Curation ); précédent : 000A46; suivant : 000A48Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments
Auteurs : Arman Rahmim [États-Unis] ; Yousef Salimpour [États-Unis] ; Saurabh Jain [États-Unis] ; Stephan A. L. Blinder [Canada] ; Ivan S. Klyuzhin [Canada] ; Gwenn S. Smith [États-Unis] ; Zoltan Mari [États-Unis] ; Vesna Sossi [Canada]Source :
- NeuroImage : Clinical [ 2213-1582 ] ; 2016.
Abstract
Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected Parkinsonian syndromes. We performed a cross-sectional study to investigate whether assessment of texture in DAT SPECT radiotracer uptake enables enhanced correlations with severity of motor and cognitive symptoms in Parkinson's disease (PD), with the long-term goal of enabling clinical utility of DAT SPECT imaging, beyond standard diagnostic tasks, to tracking of progression in PD. Quantitative analysis in routine DAT SPECT imaging, if performed at all, has been restricted to assessment of mean regional uptake. We applied a framework wherein textural features were extracted from the images. Notably, the framework did not require registration to a common template, and worked in the subject-native space. Image analysis included registration of SPECT images onto corresponding MRI images, automatic region-of-interest (ROI) extraction on the MRI images, followed by computation of Haralick texture features. We analyzed 141 subjects from the Parkinson's Progressive Marker Initiative (PPMI) database, including 85 PD and 56 healthy controls (HC) (baseline scans with accompanying 3 T MRI images). We performed univariate and multivariate regression analyses between the quantitative metrics and different clinical measures, namely (i) the UPDRS (part III - motor) score, disease duration as measured from (ii) time of diagnosis (DD-diag.) and (iii) time of appearance of symptoms (DD-sympt.), as well as (iv) the Montreal Cognitive Assessment (MoCA) score. For conventional mean uptake analysis in the putamen, we showed significant correlations with clinical measures only when both HC and PD were included (Pearson correlation
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DOI: 10.1016/j.nicl.2016.02.012
PubMed: 27995072
PubMed Central: 5153560
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L." last="Blinder">Stephan A. L. Blinder</name><affiliation wicri:level="1"><nlm:aff id="af0025">Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Pacific Parkinson's Research Centre, University of British Columbia, Vancouver</wicri:regionArea></affiliation></author><author><name sortKey="Klyuzhin, Ivan S" sort="Klyuzhin, Ivan S" uniqKey="Klyuzhin I" first="Ivan S." last="Klyuzhin">Ivan S. Klyuzhin</name><affiliation wicri:level="1"><nlm:aff id="af0030">Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Physics & Astronomy, University of British Columbia, Vancouver</wicri:regionArea></affiliation></author><author><name sortKey="Smith, Gwenn S" sort="Smith, Gwenn S" uniqKey="Smith G" first="Gwenn S." last="Smith">Gwenn S. Smith</name><affiliation wicri:level="1"><nlm:aff id="af0035">Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD</wicri:regionArea></affiliation></author><author><name sortKey="Mari, Zoltan" sort="Mari, Zoltan" uniqKey="Mari Z" first="Zoltan" last="Mari">Zoltan Mari</name><affiliation wicri:level="1"><nlm:aff id="af0015">Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD, United States</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD</wicri:regionArea></affiliation></author><author><name sortKey="Sossi, Vesna" sort="Sossi, Vesna" uniqKey="Sossi V" first="Vesna" last="Sossi">Vesna Sossi</name><affiliation wicri:level="1"><nlm:aff id="af0030">Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Physics & Astronomy, University of British Columbia, Vancouver</wicri:regionArea></affiliation></author></analytic><series><title level="j">NeuroImage : Clinical</title><idno type="eISSN">2213-1582</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected Parkinsonian syndromes. We performed a cross-sectional study to investigate whether assessment of texture in DAT SPECT radiotracer uptake enables enhanced correlations with severity of motor and cognitive symptoms in Parkinson's disease (PD), with the long-term goal of enabling clinical utility of DAT SPECT imaging, beyond standard diagnostic tasks, to tracking of progression in PD. Quantitative analysis in routine DAT SPECT imaging, if performed at all, has been restricted to assessment of mean regional uptake. We applied a framework wherein textural features were extracted from the images. Notably, the framework did not require registration to a common template, and worked in the subject-native space. Image analysis included registration of SPECT images onto corresponding MRI images, automatic region-of-interest (ROI) extraction on the MRI images, followed by computation of Haralick texture features. We analyzed 141 subjects from the Parkinson's Progressive Marker Initiative (PPMI) database, including 85 PD and 56 healthy controls (HC) (baseline scans with accompanying 3 T MRI images). We performed univariate and multivariate regression analyses between the quantitative metrics and different clinical measures, namely (i) the UPDRS (part III - motor) score, disease duration as measured from (ii) time of diagnosis (DD-diag.) and (iii) time of appearance of symptoms (DD-sympt.), as well as (iv) the Montreal Cognitive Assessment (MoCA) score. For conventional mean uptake analysis in the putamen, we showed significant correlations with clinical measures only when both HC and PD were included (Pearson correlation <italic>r</italic> = − 0.74, p-value < 0.001). However, this was not significant when applied to PD subjects only (<italic>r</italic> = − 0.19, p-value = 0.084), and no such correlations were observed in the caudate. By contrast, for the PD subjects, significant correlations were observed in the caudate when including texture metrics, with (i) UPDRS (p-values < 0.01), (ii) DD-diag. (p-values < 0.001), (iii) DD-sympt (p-values < 0.05), and (iv) MoCA (p-values < 0.01), while no correlations were observed for conventional analysis (p-values = 0.94, 0.34, 0.88 and 0.96, respectively). Our results demonstrated the ability to capture valuable information using advanced texture metrics from striatal DAT SPECT, enabling significant correlations of striatal DAT binding with clinical, motor and cognitive outcomes, and suggesting that textural features hold potential as biomarkers of PD severity and progression.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Aerts, H J W L" uniqKey="Aerts H">H.J.W.L. Aerts</name></author><author><name sortKey="Velazquez, E R" uniqKey="Velazquez E">E.R. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Neuroimage Clin</journal-id><journal-id journal-id-type="iso-abbrev">Neuroimage Clin</journal-id><journal-title-group><journal-title>NeuroImage : Clinical</journal-title></journal-title-group><issn pub-type="epub">2213-1582</issn><publisher><publisher-name>Elsevier</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">27995072</article-id><article-id pub-id-type="pmc">5153560</article-id><article-id pub-id-type="publisher-id">S2213-1582(16)30034-1</article-id><article-id pub-id-type="doi">10.1016/j.nicl.2016.02.012</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Rahmim</surname><given-names>Arman</given-names></name><email>arahmim1@jhmi.edu</email><xref rid="af0005" ref-type="aff">a</xref><xref rid="af0010" ref-type="aff">b</xref><xref rid="cr0005" ref-type="corresp">⁎</xref></contrib><contrib contrib-type="author"><name><surname>Salimpour</surname><given-names>Yousef</given-names></name><xref rid="af0015" ref-type="aff">c</xref></contrib><contrib contrib-type="author"><name><surname>Jain</surname><given-names>Saurabh</given-names></name><xref rid="af0020" ref-type="aff">d</xref></contrib><contrib contrib-type="author"><name><surname>Blinder</surname><given-names>Stephan A.L.</given-names></name><xref rid="af0025" ref-type="aff">e</xref></contrib><contrib contrib-type="author"><name><surname>Klyuzhin</surname><given-names>Ivan S.</given-names></name><xref rid="af0030" ref-type="aff">f</xref></contrib><contrib contrib-type="author"><name><surname>Smith</surname><given-names>Gwenn S.</given-names></name><xref rid="af0035" ref-type="aff">g</xref></contrib><contrib contrib-type="author"><name><surname>Mari</surname><given-names>Zoltan</given-names></name><xref rid="af0015" ref-type="aff">c</xref></contrib><contrib contrib-type="author"><name><surname>Sossi</surname><given-names>Vesna</given-names></name><xref rid="af0030" ref-type="aff">f</xref></contrib></contrib-group><aff id="af0005"><label>a</label>Department of Radiology, Johns Hopkins University, Baltimore, MD, United States</aff><aff id="af0010"><label>b</label>Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States</aff><aff id="af0015"><label>c</label>Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD, United States</aff><aff id="af0020"><label>d</label>Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States</aff><aff id="af0025"><label>e</label>Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada</aff><aff id="af0030"><label>f</label>Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada</aff><aff id="af0035"><label>g</label>Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States</aff><author-notes><corresp id="cr0005"><label>⁎</label>Corresponding author at: JHOC Building Room 3245, 601 N. Caroline St., Baltimore, MD 21287, United States.JHOC Building Room 3245601 N. Caroline St.BaltimoreMD21287United States <email>arahmim1@jhmi.edu</email></corresp></author-notes><pub-date pub-type="pmc-release"><day>23</day><month>2</month><year>2016</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="collection"><year>2016</year></pub-date><pub-date pub-type="epub"><day>23</day><month>2</month><year>2016</year></pub-date><volume>12</volume><fpage>e1</fpage><lpage>e9</lpage><history><date date-type="received"><day>29</day><month>1</month><year>2016</year></date><date date-type="rev-recd"><day>18</day><month>2</month><year>2016</year></date><date date-type="accepted"><day>19</day><month>2</month><year>2016</year></date></history><permissions><copyright-statement>© 2016 The Authors</copyright-statement><copyright-year>2016</copyright-year><license license-type="CC BY" xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</license-p></license></permissions><abstract id="ab0005"><p>Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected Parkinsonian syndromes. We performed a cross-sectional study to investigate whether assessment of texture in DAT SPECT radiotracer uptake enables enhanced correlations with severity of motor and cognitive symptoms in Parkinson's disease (PD), with the long-term goal of enabling clinical utility of DAT SPECT imaging, beyond standard diagnostic tasks, to tracking of progression in PD. Quantitative analysis in routine DAT SPECT imaging, if performed at all, has been restricted to assessment of mean regional uptake. We applied a framework wherein textural features were extracted from the images. Notably, the framework did not require registration to a common template, and worked in the subject-native space. Image analysis included registration of SPECT images onto corresponding MRI images, automatic region-of-interest (ROI) extraction on the MRI images, followed by computation of Haralick texture features. We analyzed 141 subjects from the Parkinson's Progressive Marker Initiative (PPMI) database, including 85 PD and 56 healthy controls (HC) (baseline scans with accompanying 3 T MRI images). We performed univariate and multivariate regression analyses between the quantitative metrics and different clinical measures, namely (i) the UPDRS (part III - motor) score, disease duration as measured from (ii) time of diagnosis (DD-diag.) and (iii) time of appearance of symptoms (DD-sympt.), as well as (iv) the Montreal Cognitive Assessment (MoCA) score. For conventional mean uptake analysis in the putamen, we showed significant correlations with clinical measures only when both HC and PD were included (Pearson correlation <italic>r</italic> = − 0.74, p-value < 0.001). However, this was not significant when applied to PD subjects only (<italic>r</italic> = − 0.19, p-value = 0.084), and no such correlations were observed in the caudate. By contrast, for the PD subjects, significant correlations were observed in the caudate when including texture metrics, with (i) UPDRS (p-values < 0.01), (ii) DD-diag. (p-values < 0.001), (iii) DD-sympt (p-values < 0.05), and (iv) MoCA (p-values < 0.01), while no correlations were observed for conventional analysis (p-values = 0.94, 0.34, 0.88 and 0.96, respectively). Our results demonstrated the ability to capture valuable information using advanced texture metrics from striatal DAT SPECT, enabling significant correlations of striatal DAT binding with clinical, motor and cognitive outcomes, and suggesting that textural features hold potential as biomarkers of PD severity and progression.</p></abstract><abstract abstract-type="graphical" id="ab0010"><title>Graphical abstract</title><p>Improved correlation of heterogeneity metrics (vs. conventional mean-uptake) against clinical assessments.<fig id="f0035" position="anchor"><graphic xlink:href="fx1"></graphic></fig></p></abstract><abstract abstract-type="author-highlights" id="ab0015"><title>Highlights</title><p><list list-type="simple"><list-item id="u0005"><label>•</label><p>Aim to enable image-based tracking of progression in Parkinson's disease</p></list-item><list-item id="u0010"><label>•</label><p>Texture analysis of clinical dopamine transporter (DAT) SPECT images (DaTscans)</p></list-item><list-item id="u0015"><label>•</label><p>Significant correlations with clinical, motor and cognitive outcomes</p></list-item></list></p></abstract><kwd-group id="ks0005"><title>Keywords</title><kwd>DAT SPECT</kwd><kwd>Heterogeneity</kwd><kwd>Textural features</kwd><kwd>Disease progression</kwd><kwd>Parkinson's disease</kwd></kwd-group></article-meta></front><floats-group><fig id="f0005"><label>Fig. 1</label><caption><p>Examples of transaxial, coronal and sagittal slices through the DaT SPECT images for a HC subject (A) and a PD subject (B), also showing segmentation for caudate and putamen (B).</p></caption><graphic xlink:href="gr1"></graphic></fig><fig id="f0010"><label>Fig. 2</label><caption><p>Plots of normalized mean ROI uptake (y-axis) vs. UPDRS, when including both PD (n = 85; blue ‘o’) and HC (n = 56; orange) (A), and when only including PD subjects (B). The results are shown for the more affected putamen side.</p></caption><graphic xlink:href="gr2"></graphic></fig><fig id="f0015"><label>Fig. 3</label><caption><p>Plots of metric values vs. UPDRS, for conventional mean uptake (A), as well as five Haralick texture metrics (B-F). The correlation values were − 0.008, 0.28, − 0.36, − 0.37, 0.33 and 0.28. The more affected caudate side is shown, for PD subjects (n = 85).</p></caption><graphic xlink:href="gr3"></graphic></fig><fig id="f0020"><label>Fig. 4</label><caption><p>Plots of metric values vs. DD (from diagnosis), for conventional mean uptake (A), as well as five Haralick texture metrics (B-F). The correlation values were − 0.016, 0.32, − 0.39, − 0.35, 0.44 and 0.47. The more affected caudate side is shown, for PD subjects (n = 85).</p></caption><graphic xlink:href="gr4"></graphic></fig><fig id="f0025"><label>Fig. 5</label><caption><p>Plots of metric values vs. DD (from symptoms), for conventional mean uptake (A), as well as four Haralick texture metrics (B–E). The correlation values were 0.11, − 0.25, − 0.24, 0.25 and 0.22. The more affected caudate side is shown, for PD subjects (n = 85).</p></caption><graphic xlink:href="gr5"></graphic></fig><fig id="f0030"><label>Fig. 6</label><caption><p>Plots of metric values vs. MoCA, for conventional mean uptake (A), as well as four Haralick texture metrics (B-D). The correlation values were − 0.005, 0.33, − 0.36, − 0.31 and − 0.31. The more affected caudate side is shown, for PD subjects (n = 85).</p></caption><graphic xlink:href="gr6"></graphic></fig><table-wrap id="t0005" position="float"><label>Table 1</label><caption><p>Univariate (U) and multivariate (M) analysis of correlation between textural features and clinical scores in the More affected caudate.</p></caption><table frame="hsides" rules="groups"><thead><tr><th rowspan="3" align="left">Parameters</th><th colspan="8" align="left">p-values<hr></hr></th></tr><tr><th colspan="2" align="left">UPDRS III<hr></hr></th><th colspan="2" align="left">DD-diag.<hr></hr></th><th colspan="2" align="left">DD-sympt.<hr></hr></th><th colspan="2" align="left">MoCA<hr></hr></th></tr><tr><th align="left">U</th><th align="left">M</th><th align="left">U</th><th align="left">M</th><th align="left">U</th><th align="left">M</th><th align="left">U</th><th align="left">M</th></tr></thead><tbody><tr><td align="left">Conventional</td><td align="left">0.94<xref rid="tf0005" ref-type="table-fn">a</xref></td><td></td><td align="left">0.88<xref rid="tf0005" ref-type="table-fn">a</xref></td><td></td><td align="left">0.34<xref rid="tf0005" ref-type="table-fn">a</xref></td><td></td><td align="left">0.96<xref rid="tf0005" ref-type="table-fn">a</xref></td><td></td></tr><tr><td align="left">Entropy</td><td align="left">0.0092</td><td></td><td align="left">0.0027</td><td></td><td align="left">–</td><td></td><td align="left">0.0053</td><td></td></tr><tr><td align="left">Homogeneity</td><td align="left">< 0.001</td><td></td><td align="left">< 0.001</td><td></td><td align="left">0.019<xref rid="tf0010" ref-type="table-fn">b</xref></td><td align="left">0.019</td><td align="left">–</td><td></td></tr><tr><td align="left">Agreement</td><td align="left">< 0.001</td><td align="left">< 0.001</td><td align="left">0.0012</td><td></td><td align="left">0.029<xref rid="tf0010" ref-type="table-fn">b</xref></td><td></td><td align="left">–</td><td></td></tr><tr><td align="left">Dissimilarity</td><td align="left">0.0022</td><td></td><td align="left">< 0.001</td><td></td><td align="left">0.021<xref rid="tf0010" ref-type="table-fn">b</xref></td><td></td><td align="left">–</td><td></td></tr><tr><td align="left">Contrast</td><td align="left">0.0085</td><td></td><td align="left">< 0.001</td><td align="left">< 0.001</td><td align="left">0.040<xref rid="tf0010" ref-type="table-fn">b</xref></td><td></td><td align="left">–</td><td></td></tr><tr><td align="left">Energy</td><td align="left">0.031<xref rid="tf0010" ref-type="table-fn">b</xref></td><td></td><td align="left">0.037<xref rid="tf0010" ref-type="table-fn">b</xref></td><td></td><td align="left">–</td><td></td><td align="left">0.041<xref rid="tf0010" ref-type="table-fn">b</xref></td><td></td></tr><tr><td align="left">MaxProbability</td><td align="left">0.029<xref rid="tf0010" ref-type="table-fn">b</xref></td><td></td><td align="left">–</td><td></td><td align="left">–</td><td></td><td align="left">–</td><td></td></tr><tr><td align="left">SumMean</td><td align="left">–</td><td></td><td align="left">–</td><td></td><td align="left">–</td><td></td><td align="left">0.029<xref rid="tf0010" ref-type="table-fn">b</xref></td><td></td></tr><tr><td align="left">Correlation</td><td align="left">–</td><td></td><td align="left">–</td><td></td><td align="left">–</td><td></td><td align="left">0.0021</td><td></td></tr><tr><td align="left">Variance</td><td align="left">–</td><td></td><td align="left">–</td><td></td><td align="left">–</td><td></td><td align="left">< 0.001</td><td align="char">< 0.001</td></tr><tr><td align="left">Cluster Tend.</td><td align="left">–</td><td></td><td align="left">–</td><td></td><td align="left">–</td><td></td><td align="left">0.0034</td><td></td></tr><tr><td align="left">Age</td><td align="left">–</td><td></td><td align="left">–</td><td></td><td align="left">–</td><td></td><td align="left">–</td><td align="char">0.012</td></tr></tbody></table><table-wrap-foot><fn id="sp0040"><p>Notes:</p></fn><fn id="sp0045"><p>(1) Two other Haralick features (cluster shade, inverse variance) were also included in the analyses, but had insignificant contributions, and thus are not shown in this table.</p></fn><fn id="sp0050"><p>(2) We report p-values in the table, while those in univariate analysis that become non-significant after correction for multiple testing (FDR) are indicated using <xref rid="tf0010" ref-type="table-fn">b</xref> (see below).</p></fn></table-wrap-foot><table-wrap-foot><fn id="tf0005"><label>a</label><p id="np0005">p-Values for conventional normalized mean uptake were insignificant.</p></fn></table-wrap-foot><table-wrap-foot><fn id="tf0010"><label>b</label><p id="np0010">p-Value < 0.05 but not significant after correction for multiple testing (p-values > 0.05 are not shown except for conventional analysis).</p></fn></table-wrap-foot></table-wrap></floats-group></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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