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<meta name="DC.Title" content="Preoperative Assessment of Meningioma Consistency Using a Combination of MR Elastography and DTI" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8385" />
<meta name="DC.Date" content="2024-11-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2024 by American Journal of Neuroradiology" />
<meta name="DC.AccessRights" content="restricted" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Preoperative assessment of meningioma consistency is beneficial for optimizing surgical strategy and prognosis of patients. We aim to develop a noninvasive prediction model for meningioma consistency utilizing MR elastography and DTI.

MATERIALS AND METHODS: Ninety-four patients (52 ± 22 years old, 69 women, 25 men) diagnosed with meningioma were recruited in the study. Each patient underwent preoperative T1WI, T2WI, DTI, and MR elastography. Combined MR elastography–DTI model was developed based on multiple logistic regression. Intraoperative tumor descriptions served as clinical criteria for evaluating meningioma consistency. The diagnostic efficacy in determining meningioma consistency was evaluated by using a receiver operating characteristic curve. Further validation was conducted in 27 stereotactic biopsies by using indentation tests and underlying mechanism was investigated by histologic analysis.

RESULTS: Among all the imaging modalities, MR elastography demonstrated the highest efficacy with the shear modulus magnitude (|G*|) achieving an area under the curve (AUC) of 0.81 (95% CI: 0.699–0.929). When combined with DTI, the diagnostic accuracy further increased (AUC: 0.88, 95% CI: 0.784–0.971), surpassing any technique alone. Indentation measurement based on stereotactic biopsies further demonstrated that the MR elastography–DTI model was suitable for predicting intratumor consistency. Histologic analysis suggested that meningioma consistency may be correlated with tumor cell density and fibrous content.

CONCLUSIONS: The MR elastography–DTI combined model is effective in noninvasive prediction of meningioma consistency.

AUC
:   area under the curve
FA
:   fractional anisotropy
MD
:   mean diffusivity
ROC
:   receiver operating characteristic
SI
:   signal intensity
|G*|
:   shear modulus magnitude" />
<meta name="DC.Contributor" content="Yuting Bao" />
<meta name="DC.Contributor" content="Suhao Qiu" />
<meta name="DC.Contributor" content="Zhenyu Li" />
<meta name="DC.Contributor" content="Guangzhong Yang" />
<meta name="DC.Contributor" content="Yuan Feng" />
<meta name="DC.Contributor" content="Qi Yue" />
<meta name="article:published_time" content="2024-11-01" />
<meta name="article:section" content="Brain Tumor Imaging" />
<meta name="citation_title" content="Preoperative Assessment of Meningioma Consistency Using a Combination of MR Elastography and DTI" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Preoperative assessment of meningioma consistency is beneficial for optimizing surgical strategy and prognosis of patients. We aim to develop a noninvasive prediction model for meningioma consistency utilizing MR elastography and DTI.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Ninety-four patients (52 ± 22 years old, 69 women, 25 men) diagnosed with meningioma were recruited in the study. Each patient underwent preoperative T1WI, T2WI, DTI, and MR elastography. Combined MR elastography–DTI model was developed based on multiple logistic regression. Intraoperative tumor descriptions served as clinical criteria for evaluating meningioma consistency. The diagnostic efficacy in determining meningioma consistency was evaluated by using a receiver operating characteristic curve. Further validation was conducted in 27 stereotactic biopsies by using indentation tests and underlying mechanism was investigated by histologic analysis.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Among all the imaging modalities, MR elastography demonstrated the highest efficacy with the shear modulus magnitude (|G*|) achieving an area under the curve (AUC) of 0.81 (95% CI: 0.699–0.929). When combined with DTI, the diagnostic accuracy further increased (AUC: 0.88, 95% CI: 0.784–0.971), surpassing any technique alone. Indentation measurement based on stereotactic biopsies further demonstrated that the MR elastography–DTI model was suitable for predicting intratumor consistency. Histologic analysis suggested that meningioma consistency may be correlated with tumor cell density and fibrous content.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;The MR elastography–DTI combined model is effective in noninvasive prediction of meningioma consistency.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
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<meta name="citation_author" content="Zhenyu Li" />
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<meta name="twitter:title" content="Preoperative Assessment of Meningioma Consistency Using a Combination of MR Elastography and DTI" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Preoperative assessment of meningioma consistency is beneficial for optimizing surgical strategy and prognosis of patients. We aim to develop a noninvasive prediction model for meningioma consistency utilizing MR elastography and DTI.

MATERIALS AND METHODS: Ninety-four patients (52 ± 22 years old, 69 women, 25 men) diagnosed with meningioma were recruited in the study. Each patient underwent preoperative T1WI, T2WI, DTI, and MR elastography. Combined MR elastography–DTI model was developed based on multiple logistic regression. Intraoperative tumor descriptions served as clinical criteria for evaluating meningioma consistency. The diagnostic efficacy in determining meningioma consistency was evaluated by using a receiver operating characteristic curve. Further validation was conducted in 27 stereotactic biopsies by using indentation tests and underlying mechanism was investigated by histologic analysis.

RESULTS: Among all the imaging modalities, MR elastography demonstrated the highest efficacy with the shear modulus magnitude (|G*|) achieving an area under the curve (AUC) of 0.81 (95% CI: 0.699–0.929). When combined with DTI, the diagnostic accuracy further increased (AUC: 0.88, 95% CI: 0.784–0.971), surpassing any technique alone. Indentation measurement based on stereotactic biopsies further demonstrated that the MR elastography–DTI model was suitable for predicting intratumor consistency. Histologic analysis suggested that meningioma consistency may be correlated with tumor cell density and fibrous content.

CONCLUSIONS: The MR elastography–DTI combined model is effective in noninvasive prediction of meningioma consistency.

AUC
:   area under the curve
FA
:   fractional anisotropy
MD
:   mean diffusivity
ROC
:   receiver operating characteristic
SI
:   signal intensity
|G*|
:   shear modulus magnitude" />
<meta name="og-title" property="og:title" content="Preoperative Assessment of Meningioma Consistency Using a Combination of MR Elastography and DTI" />
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<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: Preoperative assessment of meningioma consistency is beneficial for optimizing surgical strategy and prognosis of patients. We aim to develop a noninvasive prediction model for meningioma consistency utilizing MR elastography and DTI.

MATERIALS AND METHODS: Ninety-four patients (52 ± 22 years old, 69 women, 25 men) diagnosed with meningioma were recruited in the study. Each patient underwent preoperative T1WI, T2WI, DTI, and MR elastography. Combined MR elastography–DTI model was developed based on multiple logistic regression. Intraoperative tumor descriptions served as clinical criteria for evaluating meningioma consistency. The diagnostic efficacy in determining meningioma consistency was evaluated by using a receiver operating characteristic curve. Further validation was conducted in 27 stereotactic biopsies by using indentation tests and underlying mechanism was investigated by histologic analysis.

RESULTS: Among all the imaging modalities, MR elastography demonstrated the highest efficacy with the shear modulus magnitude (|G*|) achieving an area under the curve (AUC) of 0.81 (95% CI: 0.699–0.929). When combined with DTI, the diagnostic accuracy further increased (AUC: 0.88, 95% CI: 0.784–0.971), surpassing any technique alone. Indentation measurement based on stereotactic biopsies further demonstrated that the MR elastography–DTI model was suitable for predicting intratumor consistency. Histologic analysis suggested that meningioma consistency may be correlated with tumor cell density and fibrous content.

CONCLUSIONS: The MR elastography–DTI combined model is effective in noninvasive prediction of meningioma consistency.

AUC
:   area under the curve
FA
:   fractional anisotropy
MD
:   mean diffusivity
ROC
:   receiver operating characteristic
SI
:   signal intensity
|G*|
:   shear modulus magnitude" />
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      <div class="highwire-cite-overline"><span  class="highwire-cite-metadata-article-type highwire-cite-metadata">Research Article</span><span class="separator-pipe"></span><span  class="highwire-cite-metadata-art-cat highwire-cite-metadata"><span class="wrapper">Brain Tumor Imaging</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">Preoperative Assessment of Meningioma Consistency Using a Combination of MR Elastography and DTI</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Yuting Bao</span>, <span class="highwire-citation-author" data-delta="1">Suhao Qiu</span>, <span class="highwire-citation-author" data-delta="2">Zhenyu Li</span>, <span class="highwire-citation-author" data-delta="3">Guangzhong Yang</span>, <span class="highwire-citation-author" data-delta="4">Yuan Feng</span> and <span class="highwire-citation-author" data-delta="5">Qi Yue</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">November 2024,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(11) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1755-1761; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8385 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-111442--61059745513" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Yuting Bao </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Neurosurgery (Y.B., Z.L., Q.Y.), Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>b</span>National Center for Neurological Disorders (Y.B., Z.L., Q.Y.), Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration (Y.B., Z.L., Q.Y.), Fudan University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>d</span>Neurosurgical Institute of Fudan University (Y.B., Z.L., Q.Y.), Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>e</span>Shanghai Clinical Medical Center of Neurosurgery (Y.B., Z.L., Q.Y.), Shanghai, China</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Yuting%2BBao%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Bao%20Y&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AYuting%2BBao%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0009-0000-0308-9950" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Yuting Bao</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Suhao Qiu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>f</span>School of Biomedical Engineering (S.Q., G.Y., Y.F.), Shanghai Jiao Tong University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>g</span>Institute of Medical Robotics (S.Q., G.Y., Y.F.), Shanghai Jiao Tong University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>h</span>National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT) (S.Q., G.Y., Y.F.), Shanghai Jiao Tong University, Shanghai, China</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Suhao%2BQiu%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Qiu%20S&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3ASuhao%2BQiu%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Zhenyu Li </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Neurosurgery (Y.B., Z.L., Q.Y.), Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>b</span>National Center for Neurological Disorders (Y.B., Z.L., Q.Y.), Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration (Y.B., Z.L., Q.Y.), Fudan University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>d</span>Neurosurgical Institute of Fudan University (Y.B., Z.L., Q.Y.), Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>e</span>Shanghai Clinical Medical Center of Neurosurgery (Y.B., Z.L., Q.Y.), Shanghai, China</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Zhenyu%2BLi%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Li%20Z&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AZhenyu%2BLi%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Guangzhong Yang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>f</span>School of Biomedical Engineering (S.Q., G.Y., Y.F.), Shanghai Jiao Tong University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>g</span>Institute of Medical Robotics (S.Q., G.Y., Y.F.), Shanghai Jiao Tong University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>h</span>National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT) (S.Q., G.Y., Y.F.), Shanghai Jiao Tong University, Shanghai, China</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Guangzhong%2BYang%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Yang%20G&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AGuangzhong%2BYang%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">Yuan Feng </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>f</span>School of Biomedical Engineering (S.Q., G.Y., Y.F.), Shanghai Jiao Tong University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>g</span>Institute of Medical Robotics (S.Q., G.Y., Y.F.), Shanghai Jiao Tong University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>h</span>National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT) (S.Q., G.Y., Y.F.), Shanghai Jiao Tong University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>i</span>Department of Radiology (Y.F.), Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Yuan%2BFeng%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Feng%20Y&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AYuan%2BFeng%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0003-1902-3408" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Yuan Feng</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">Qi Yue </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Neurosurgery (Y.B., Z.L., Q.Y.), Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>b</span>National Center for Neurological Disorders (Y.B., Z.L., Q.Y.), Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration (Y.B., Z.L., Q.Y.), Fudan University, Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>d</span>Neurosurgical Institute of Fudan University (Y.B., Z.L., Q.Y.), Shanghai, China</div><div class='author-affiliation'><span class='nlm-sup'>e</span>Shanghai Clinical Medical Center of Neurosurgery (Y.B., Z.L., Q.Y.), Shanghai, China</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a 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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 45</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 11 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Nov 2024 </div></div>
      
      
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    	<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">Nov 2024, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(11) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1755-1761; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8385 </span></div>
  
  
  
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      <div class="highwire-cite-title">Meningioma Consistency via MR Elastography</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">Yuting</span> <span class="nlm-surname">Bao</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Suhao</span> <span class="nlm-surname">Qiu</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Zhenyu</span> <span class="nlm-surname">Li</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Guangzhong</span> <span class="nlm-surname">Yang</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Yuan</span> <span class="nlm-surname">Feng</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Qi</span> <span class="nlm-surname">Yue</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">Nov 2024, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(11) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1755-1761; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8385 </span></div>
  
  
  
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