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<meta name="DC.Language" content="en" />
<meta name="DC.Title" content="DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8351" />
<meta name="DC.Date" content="2024-10-01" />
<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: Stereotactic radiosurgery is a key treatment modality for cerebral AVMs, particularly for small lesions and those located in eloquent brain regions. Predicting obliteration remains challenging due to evolving treatment paradigms and complex AVM presentations. With digital subtraction angiography (DSA) being the gold standard for outcome evaluation, radiomic approaches offer potential for more objective and detailed analysis. We aimed to develop machine learning modeling using DSA quantitative features for post-SRS obliteration prediction.

MATERIALS AND METHODS: A prospective registry of patients with cerebral AVMs was screened to include patients with digital prestereotactic radiosurgery DSA. Anterior-posterior and lateral views were retrieved and manually segmented. Quantitative features were computed from the lesion ROI. Following feature selection, machine learning models were developed to predict unsuccessful 2-year total obliteration using processed radiomics features in comparison with clinical and radiosurgical features. When we evaluated through area under the receiver operating characteristic curve (AUROC), accuracy, area under the precision-recall curve F1, recall, and precision, the best performing model predictions on the test set were interpreted using the Shapley additive explanations approach.

RESULTS: DSA images of 100 included patients were retrieved and analyzed. The best-performing clinical radiosurgical model was a gradient boosting classifier with an AUROC of 68% and a recall of 67%. When we used radiomics variables as input, the AdaBoost classifier had the best evaluation metrics with an AUROC of 79% and a recall of 75%. The most important clinico-radiosurgical features, ranked by model contribution, were lesion volume, patient age, treatment dose rate, the presence of seizure at presentation, and prior resection. The most important ranked radiomics features were the following: gray-level size zone matrix, gray-level nonuniformity, kurtosis, sphericity, skewness, and gray-level dependence matrix dependence nonuniformity.

CONCLUSIONS: The combination of radiomics with machine learning is a promising approach for predicting cerebral AVM obliteration status following stereotactic radiosurgery. DSA could enhance prognostication of stereotactic radiosurgery–treated AVMs due to its high spatial resolution. Model interpretation is essential for building transparent models and establishing clinically valid radiomic signatures.

AUROC
:   area under the receiver operating characteristic curve
BED
:   biologic effective dose
BOT
:   beam-on time
GLDM
:   gray-level dependence matrix
GLSZM
:   gray-level size zone matrix
ML
:   machine learning
SHAP
:   Shapley additive explanations
SRS
:   stereotactic radiosurgery
TDR
:   treatment dose rate" />
<meta name="DC.Contributor" content="Mohamed Sobhi Jabal" />
<meta name="DC.Contributor" content="Marwa A. Mohammed" />
<meta name="DC.Contributor" content="Cody L. Nesvick" />
<meta name="DC.Contributor" content="Hassan Kobeissi" />
<meta name="DC.Contributor" content="Christopher S. Graffeo" />
<meta name="DC.Contributor" content="Bruce E. Pollock" />
<meta name="DC.Contributor" content="Waleed Brinjikji" />
<meta name="article:published_time" content="2024-10-01" />
<meta name="article:section" content="Artificial Intelligence" />
<meta name="citation_title" content="DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE: &lt;/h3&gt;
&lt;p&gt;Stereotactic radiosurgery is a key treatment modality for cerebral AVMs, particularly for small lesions and those located in eloquent brain regions. Predicting obliteration remains challenging due to evolving treatment paradigms and complex AVM presentations. With digital subtraction angiography (DSA) being the gold standard for outcome evaluation, radiomic approaches offer potential for more objective and detailed analysis. We aimed to develop machine learning modeling using DSA quantitative features for post-SRS obliteration prediction.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;A prospective registry of patients with cerebral AVMs was screened to include patients with digital prestereotactic radiosurgery DSA. Anterior-posterior and lateral views were retrieved and manually segmented. Quantitative features were computed from the lesion ROI. Following feature selection, machine learning models were developed to predict unsuccessful 2-year total obliteration using processed radiomics features in comparison with clinical and radiosurgical features. When we evaluated through area under the receiver operating characteristic curve (AUROC), accuracy, area under the precision-recall curve F1, recall, and precision, the best performing model predictions on the test set were interpreted using the Shapley additive explanations approach.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;DSA images of 100 included patients were retrieved and analyzed. The best-performing clinical radiosurgical model was a gradient boosting classifier with an AUROC of 68% and a recall of 67%. When we used radiomics variables as input, the AdaBoost classifier had the best evaluation metrics with an AUROC of 79% and a recall of 75%. The most important clinico-radiosurgical features, ranked by model contribution, were lesion volume, patient age, treatment dose rate, the presence of seizure at presentation, and prior resection. The most important ranked radiomics features were the following: gray-level size zone matrix, gray-level nonuniformity, kurtosis, sphericity, skewness, and gray-level dependence matrix dependence nonuniformity.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;The combination of radiomics with machine learning is a promising approach for predicting cerebral AVM obliteration status following stereotactic radiosurgery. DSA could enhance prognostication of stereotactic radiosurgery–treated AVMs due to its high spatial resolution. Model interpretation is essential for building transparent models and establishing clinically valid radiomic signatures.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
<meta name="citation_publisher" content="American Journal of Neuroradiology" />
<meta name="citation_publication_date" content="2024/10/01" />
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<meta name="citation_doi" content="10.3174/ajnr.A8351" />
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<meta name="citation_section" content="Artificial Intelligence" />
<meta name="citation_firstpage" content="1521" />
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<meta name="citation_author" content="Mohamed Sobhi Jabal" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota" />
<meta name="citation_author_institution" content="bDepartment of Computer and Information Science (M.S.J.), University of Pennsylvania, Philadelphia, Pennsylvania" />
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<meta name="citation_author" content="Marwa A. Mohammed" />
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<meta name="citation_author_orcid" content="https://orcid.org/0000-0001-5524-369X" />
<meta name="citation_author" content="Cody L. Nesvick" />
<meta name="citation_author_institution" content="cDepartment of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota" />
<meta name="citation_author_orcid" content="https://orcid.org/0000-0002-2249-5054" />
<meta name="citation_author" content="Hassan Kobeissi" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota" />
<meta name="citation_author_orcid" content="https://orcid.org/0000-0001-7446-624X" />
<meta name="citation_author" content="Christopher S. Graffeo" />
<meta name="citation_author_institution" content="cDepartment of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota" />
<meta name="citation_author_orcid" content="https://orcid.org/0000-0001-5314-1067" />
<meta name="citation_author" content="Bruce E. Pollock" />
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<meta name="citation_author_orcid" content="https://orcid.org/0000-0002-6148-9079" />
<meta name="citation_author" content="Waleed Brinjikji" />
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<meta name="twitter:title" content="DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Stereotactic radiosurgery is a key treatment modality for cerebral AVMs, particularly for small lesions and those located in eloquent brain regions. Predicting obliteration remains challenging due to evolving treatment paradigms and complex AVM presentations. With digital subtraction angiography (DSA) being the gold standard for outcome evaluation, radiomic approaches offer potential for more objective and detailed analysis. We aimed to develop machine learning modeling using DSA quantitative features for post-SRS obliteration prediction.

MATERIALS AND METHODS: A prospective registry of patients with cerebral AVMs was screened to include patients with digital prestereotactic radiosurgery DSA. Anterior-posterior and lateral views were retrieved and manually segmented. Quantitative features were computed from the lesion ROI. Following feature selection, machine learning models were developed to predict unsuccessful 2-year total obliteration using processed radiomics features in comparison with clinical and radiosurgical features. When we evaluated through area under the receiver operating characteristic curve (AUROC), accuracy, area under the precision-recall curve F1, recall, and precision, the best performing model predictions on the test set were interpreted using the Shapley additive explanations approach.

RESULTS: DSA images of 100 included patients were retrieved and analyzed. The best-performing clinical radiosurgical model was a gradient boosting classifier with an AUROC of 68% and a recall of 67%. When we used radiomics variables as input, the AdaBoost classifier had the best evaluation metrics with an AUROC of 79% and a recall of 75%. The most important clinico-radiosurgical features, ranked by model contribution, were lesion volume, patient age, treatment dose rate, the presence of seizure at presentation, and prior resection. The most important ranked radiomics features were the following: gray-level size zone matrix, gray-level nonuniformity, kurtosis, sphericity, skewness, and gray-level dependence matrix dependence nonuniformity.

CONCLUSIONS: The combination of radiomics with machine learning is a promising approach for predicting cerebral AVM obliteration status following stereotactic radiosurgery. DSA could enhance prognostication of stereotactic radiosurgery–treated AVMs due to its high spatial resolution. Model interpretation is essential for building transparent models and establishing clinically valid radiomic signatures.

AUROC
:   area under the receiver operating characteristic curve
BED
:   biologic effective dose
BOT
:   beam-on time
GLDM
:   gray-level dependence matrix
GLSZM
:   gray-level size zone matrix
ML
:   machine learning
SHAP
:   Shapley additive explanations
SRS
:   stereotactic radiosurgery
TDR
:   treatment dose rate" />
<meta name="og-title" property="og:title" content="DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/45/10/1521" />
<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: Stereotactic radiosurgery is a key treatment modality for cerebral AVMs, particularly for small lesions and those located in eloquent brain regions. Predicting obliteration remains challenging due to evolving treatment paradigms and complex AVM presentations. With digital subtraction angiography (DSA) being the gold standard for outcome evaluation, radiomic approaches offer potential for more objective and detailed analysis. We aimed to develop machine learning modeling using DSA quantitative features for post-SRS obliteration prediction.

MATERIALS AND METHODS: A prospective registry of patients with cerebral AVMs was screened to include patients with digital prestereotactic radiosurgery DSA. Anterior-posterior and lateral views were retrieved and manually segmented. Quantitative features were computed from the lesion ROI. Following feature selection, machine learning models were developed to predict unsuccessful 2-year total obliteration using processed radiomics features in comparison with clinical and radiosurgical features. When we evaluated through area under the receiver operating characteristic curve (AUROC), accuracy, area under the precision-recall curve F1, recall, and precision, the best performing model predictions on the test set were interpreted using the Shapley additive explanations approach.

RESULTS: DSA images of 100 included patients were retrieved and analyzed. The best-performing clinical radiosurgical model was a gradient boosting classifier with an AUROC of 68% and a recall of 67%. When we used radiomics variables as input, the AdaBoost classifier had the best evaluation metrics with an AUROC of 79% and a recall of 75%. The most important clinico-radiosurgical features, ranked by model contribution, were lesion volume, patient age, treatment dose rate, the presence of seizure at presentation, and prior resection. The most important ranked radiomics features were the following: gray-level size zone matrix, gray-level nonuniformity, kurtosis, sphericity, skewness, and gray-level dependence matrix dependence nonuniformity.

CONCLUSIONS: The combination of radiomics with machine learning is a promising approach for predicting cerebral AVM obliteration status following stereotactic radiosurgery. DSA could enhance prognostication of stereotactic radiosurgery–treated AVMs due to its high spatial resolution. Model interpretation is essential for building transparent models and establishing clinically valid radiomic signatures.

AUROC
:   area under the receiver operating characteristic curve
BED
:   biologic effective dose
BOT
:   beam-on time
GLDM
:   gray-level dependence matrix
GLSZM
:   gray-level size zone matrix
ML
:   machine learning
SHAP
:   Shapley additive explanations
SRS
:   stereotactic radiosurgery
TDR
:   treatment dose rate" />
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      <h1 class="highwire-cite-title" id="page-title">DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Mohamed Sobhi Jabal</span>, <span class="highwire-citation-author" data-delta="1">Marwa A. Mohammed</span>, <span class="highwire-citation-author" data-delta="2">Cody L. Nesvick</span>, <span class="highwire-citation-author" data-delta="3">Hassan Kobeissi</span>, <span class="highwire-citation-author" data-delta="4">Christopher S. Graffeo</span>, <span class="highwire-citation-author" data-delta="5">Bruce E. Pollock</span> and <span class="highwire-citation-author" data-delta="6">Waleed Brinjikji</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 2024,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1521-1527; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8351 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-110817--61912574371" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Mohamed Sobhi Jabal </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 Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Computer and Information Science (M.S.J.), University of Pennsylvania, Philadelphia, Pennsylvania</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=Mohamed%2BSobhi%2BJabal%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=Jabal%20MS&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%3AMohamed%2BSobhi%2BJabal%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/0000-0003-2891-8633" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Mohamed Sobhi Jabal</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Marwa A. Mohammed </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 Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota</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=Marwa%2BA.%2BMohammed%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=Mohammed%20MA&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%3AMarwa%2BA.%2BMohammed%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/0000-0001-5524-369X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Marwa A. Mohammed</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Cody L. Nesvick </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota</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=Cody%2BL.%2BNesvick%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=Nesvick%20CL&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%3ACody%2BL.%2BNesvick%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/0000-0002-2249-5054" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Cody L. Nesvick</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Hassan Kobeissi </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 Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota</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=Hassan%2BKobeissi%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=Kobeissi%20H&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%3AHassan%2BKobeissi%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/0000-0001-7446-624X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Hassan Kobeissi</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">Christopher S. Graffeo </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota</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=Christopher%2BS.%2BGraffeo%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=Graffeo%20CS&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%3AChristopher%2BS.%2BGraffeo%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/0000-0001-5314-1067" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Christopher S. Graffeo</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">Bruce E. Pollock </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota</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=Bruce%2BE.%2BPollock%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=Pollock%20BE&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%3ABruce%2BE.%2BPollock%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/0000-0002-6148-9079" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Bruce E. Pollock</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">Waleed Brinjikji </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 Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Neurological Surgery (C.L.N., C.S.G., B.E.P., W.B.), Mayo Clinic, Rochester, Minnesota</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=Waleed%2BBrinjikji%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=Brinjikji%20W&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%3AWaleed%2BBrinjikji%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/0000-0001-5271-5524" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Waleed Brinjikji</a></li></ul></div></div></div>  </div>

  
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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 10 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Oct 2024 </div></div>
      
      
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      <div class="highwire-cite-title">DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery</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">Mohamed Sobhi</span> <span class="nlm-surname">Jabal</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Marwa A.</span> <span class="nlm-surname">Mohammed</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Cody L.</span> <span class="nlm-surname">Nesvick</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Hassan</span> <span class="nlm-surname">Kobeissi</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Christopher S.</span> <span class="nlm-surname">Graffeo</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Bruce E.</span> <span class="nlm-surname">Pollock</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Waleed</span> <span class="nlm-surname">Brinjikji</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 2024, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1521-1527; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8351 </span></div>
  
  
  
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