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<meta name="DC.Title" content="Prediction of Glioma Recurrence Using Dynamic 18F-Fluoroethyltyrosine PET" />
<meta name="DC.Identifier" content="10.3174/ajnr.A3980" />
<meta name="DC.Date" content="2014-10-01" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Inter- and intratumor heterogeneity and the variable course of disease in patients with glioma motivate the investigation of new prognostic factors to optimize individual treatment. Here we explore the usefulness of standard static and more sophisticated dynamic 18F-fluoroethyltyrosine-PET imaging for the assessment of patient prognosis.

MATERIALS AND METHODS: Thirty-four consecutive patients with untreated, first-diagnosed, histologically proved glioma were included in this retrospective study. All patients underwent dynamic PET scans before surgery (± standard treatment) and were followed up clinically and by MR imaging. Static and dynamic tumor-to-background ratio, TTP, and slope-to-peak were obtained and correlated with progression-free survival.

RESULTS: Twenty of 34 patients experienced progression, with a median progression-free survival of 28.0 ± 11.1 months. Dynamic TTP was highly prognostic for recurrent disease, showing a strong correlation with progression-free survival (hazard ratio, 6.050; 95% CI, 2.11–17.37; P &lt; .001). Most interesting, this correlation also proved significant in the subgroup of low-grade glioma (hazard ratio, 5.347; 95% CI, 1.05–27.20; P = .044), but not when using established static imaging parameters, such as maximum tumor-to-background ratio and mean tumor-to-background ratio. In the high-grade glioma subgroup, both dynamic and static parameters correlated with progression-free survival. The best results were achieved by defining ROIs around “hot spots” in earlier timeframes, underlining the concept of intratumor heterogeneity.

CONCLUSIONS: 18F-fluoroethyltyrosine-PET can predict recurrence in patients with glioma, with dynamic analysis showing advantages over static imaging, especially in the low-grade subgroup.

FET
:   18F-fluoroethyltyrosine
LGG
:   low-grade glioma
HGG
:   high-grade glioma
HR
:   hazard ratio
PFS
:   progression-free survival
ROC
:   receiver operating characteristic analysis
TBR
:   tumor-to-background ratio" />
<meta name="DC.Contributor" content="T. Pyka" />
<meta name="DC.Contributor" content="J. Gempt" />
<meta name="DC.Contributor" content="F. Ringel" />
<meta name="DC.Contributor" content="S. Hüttinger" />
<meta name="DC.Contributor" content="S. van Marwick" />
<meta name="DC.Contributor" content="S. Nekolla" />
<meta name="DC.Contributor" content="H.-J. Wester" />
<meta name="DC.Contributor" content="M. Schwaiger" />
<meta name="DC.Contributor" content="S. Förster" />
<meta name="article:published_time" content="2014-10-01" />
<meta name="article:section" content="Brain" />
<meta name="citation_title" content="Prediction of Glioma Recurrence Using Dynamic 18F-Fluoroethyltyrosine PET" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Inter- and intratumor heterogeneity and the variable course of disease in patients with glioma motivate the investigation of new prognostic factors to optimize individual treatment. Here we explore the usefulness of standard static and more sophisticated dynamic &lt;sup&gt;18&lt;/sup&gt;F-fluoroethyltyrosine-PET imaging for the assessment of patient prognosis.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Thirty-four consecutive patients with untreated, first-diagnosed, histologically proved glioma were included in this retrospective study. All patients underwent dynamic PET scans before surgery (± standard treatment) and were followed up clinically and by MR imaging. Static and dynamic tumor-to-background ratio, TTP, and slope-to-peak were obtained and correlated with progression-free survival.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Twenty of 34 patients experienced progression, with a median progression-free survival of 28.0 ± 11.1 months. Dynamic TTP was highly prognostic for recurrent disease, showing a strong correlation with progression-free survival (hazard ratio, 6.050; 95% CI, 2.11–17.37; &lt;i&gt;P&lt;/i&gt; &amp;lt; .001). Most interesting, this correlation also proved significant in the subgroup of low-grade glioma (hazard ratio, 5.347; 95% CI, 1.05–27.20; &lt;i&gt;P&lt;/i&gt; = .044), but not when using established static imaging parameters, such as maximum tumor-to-background ratio and mean tumor-to-background ratio. In the high-grade glioma subgroup, both dynamic and static parameters correlated with progression-free survival. The best results were achieved by defining ROIs around “hot spots” in earlier timeframes, underlining the concept of intratumor heterogeneity.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;&lt;sup&gt;18&lt;/sup&gt;F-fluoroethyltyrosine-PET can predict recurrence in patients with glioma, with dynamic analysis showing advantages over static imaging, especially in the low-grade subgroup.&lt;/p&gt;" />
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<meta name="twitter:title" content="Prediction of Glioma Recurrence Using Dynamic 18F-Fluoroethyltyrosine PET" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Inter- and intratumor heterogeneity and the variable course of disease in patients with glioma motivate the investigation of new prognostic factors to optimize individual treatment. Here we explore the usefulness of standard static and more sophisticated dynamic 18F-fluoroethyltyrosine-PET imaging for the assessment of patient prognosis.

MATERIALS AND METHODS: Thirty-four consecutive patients with untreated, first-diagnosed, histologically proved glioma were included in this retrospective study. All patients underwent dynamic PET scans before surgery (± standard treatment) and were followed up clinically and by MR imaging. Static and dynamic tumor-to-background ratio, TTP, and slope-to-peak were obtained and correlated with progression-free survival.

RESULTS: Twenty of 34 patients experienced progression, with a median progression-free survival of 28.0 ± 11.1 months. Dynamic TTP was highly prognostic for recurrent disease, showing a strong correlation with progression-free survival (hazard ratio, 6.050; 95% CI, 2.11–17.37; P &lt; .001). Most interesting, this correlation also proved significant in the subgroup of low-grade glioma (hazard ratio, 5.347; 95% CI, 1.05–27.20; P = .044), but not when using established static imaging parameters, such as maximum tumor-to-background ratio and mean tumor-to-background ratio. In the high-grade glioma subgroup, both dynamic and static parameters correlated with progression-free survival. The best results were achieved by defining ROIs around “hot spots” in earlier timeframes, underlining the concept of intratumor heterogeneity.

CONCLUSIONS: 18F-fluoroethyltyrosine-PET can predict recurrence in patients with glioma, with dynamic analysis showing advantages over static imaging, especially in the low-grade subgroup.

FET
:   18F-fluoroethyltyrosine
LGG
:   low-grade glioma
HGG
:   high-grade glioma
HR
:   hazard ratio
PFS
:   progression-free survival
ROC
:   receiver operating characteristic analysis
TBR
:   tumor-to-background ratio" />
<meta name="og-title" property="og:title" content="Prediction of Glioma Recurrence Using Dynamic 18F-Fluoroethyltyrosine PET" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/35/10/1924" />
<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Inter- and intratumor heterogeneity and the variable course of disease in patients with glioma motivate the investigation of new prognostic factors to optimize individual treatment. Here we explore the usefulness of standard static and more sophisticated dynamic 18F-fluoroethyltyrosine-PET imaging for the assessment of patient prognosis.

MATERIALS AND METHODS: Thirty-four consecutive patients with untreated, first-diagnosed, histologically proved glioma were included in this retrospective study. All patients underwent dynamic PET scans before surgery (± standard treatment) and were followed up clinically and by MR imaging. Static and dynamic tumor-to-background ratio, TTP, and slope-to-peak were obtained and correlated with progression-free survival.

RESULTS: Twenty of 34 patients experienced progression, with a median progression-free survival of 28.0 ± 11.1 months. Dynamic TTP was highly prognostic for recurrent disease, showing a strong correlation with progression-free survival (hazard ratio, 6.050; 95% CI, 2.11–17.37; P &lt; .001). Most interesting, this correlation also proved significant in the subgroup of low-grade glioma (hazard ratio, 5.347; 95% CI, 1.05–27.20; P = .044), but not when using established static imaging parameters, such as maximum tumor-to-background ratio and mean tumor-to-background ratio. In the high-grade glioma subgroup, both dynamic and static parameters correlated with progression-free survival. The best results were achieved by defining ROIs around “hot spots” in earlier timeframes, underlining the concept of intratumor heterogeneity.

CONCLUSIONS: 18F-fluoroethyltyrosine-PET can predict recurrence in patients with glioma, with dynamic analysis showing advantages over static imaging, especially in the low-grade subgroup.

FET
:   18F-fluoroethyltyrosine
LGG
:   low-grade glioma
HGG
:   high-grade glioma
HR
:   hazard ratio
PFS
:   progression-free survival
ROC
:   receiver operating characteristic analysis
TBR
:   tumor-to-background ratio" />
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">T. Pyka</span>, <span class="highwire-citation-author" data-delta="1">J. Gempt</span>, <span class="highwire-citation-author" data-delta="2">F. Ringel</span>, <span class="highwire-citation-author" data-delta="3">S. Hüttinger</span>, <span class="highwire-citation-author" data-delta="4">S. van Marwick</span>, <span class="highwire-citation-author" data-delta="5">S. Nekolla</span>, <span class="highwire-citation-author" data-delta="6">H.-J. Wester</span>, <span class="highwire-citation-author" data-delta="7">M. Schwaiger</span> and <span class="highwire-citation-author" data-delta="8">S. Förster</span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">October 2014,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">35 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1924-1929; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A3980 </span></div>
  
  
  
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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 35</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 10 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Oct 2014 </div></div>
      
      
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      <div class="highwire-cite-title">Prediction of Glioma Recurrence Using Dynamic <sup>18</sup>F-Fluoroethyltyrosine PET</div>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">T.</span> <span class="nlm-surname">Pyka</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Gempt</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">F.</span> <span class="nlm-surname">Ringel</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">S.</span> <span class="nlm-surname">Hüttinger</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">S.</span> <span class="nlm-surname">van Marwick</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">S.</span> <span class="nlm-surname">Nekolla</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">H.-J.</span> <span class="nlm-surname">Wester</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">M.</span> <span class="nlm-surname">Schwaiger</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">S.</span> <span class="nlm-surname">Förster</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Oct 2014, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">35 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1924-1929; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A3980 </span></div>
  
  
  
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      <div class="highwire-cite-title">Prediction of Glioma Recurrence Using Dynamic <sup>18</sup>F-Fluoroethyltyrosine PET</div>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">T.</span> <span class="nlm-surname">Pyka</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Gempt</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">F.</span> <span class="nlm-surname">Ringel</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">S.</span> <span class="nlm-surname">Hüttinger</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">S.</span> <span class="nlm-surname">van Marwick</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">S.</span> <span class="nlm-surname">Nekolla</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">H.-J.</span> <span class="nlm-surname">Wester</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">M.</span> <span class="nlm-surname">Schwaiger</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">S.</span> <span class="nlm-surname">Förster</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Oct 2014, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">35 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1924-1929; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A3980 </span></div>
  
  
  
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