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<meta name="DC.Title" content="Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8280" />
<meta name="DC.Date" content="2024-09-01" />
<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: Feature variability in radiomics studies due to technical and magnet strength parameters is well-known and may be addressed through various preprocessing methods. However, very few studies have evaluated the downstream impact of variable preprocessing on model classification performance in a multiclass setting. We sought to evaluate the impact of Smallest Univalue Segment Assimilating Nucleus (SUSAN) denoising and Combining Batches harmonization on model classification performance.

MATERIALS AND METHODS: A total of 493 cases (410 internal and 83 external data sets) of glioblastoma, intracranial metastatic disease, and primary CNS lymphoma underwent semiautomated 3D-segmentation post-baseline image processing (BIP) consisting of resampling, realignment, coregistration, skull-stripping, and image normalization. Post-BIP, 2 sets were generated, one with and another without SUSAN denoising. Radiomics features were extracted from both data sets and batch-corrected to produce 4 data sets: (a) BIP, (b) BIP with SUSAN denoising, (c) BIP with Combining Batches, and (d) BIP with both SUSAN denoising and Combining Batches harmonization. Performance was then summarized for models using a combination of 6 feature-selection techniques and 6 machine learning models across 4 mask-sequence combinations with features derived from 1 to 3 (multiparametric) MRI sequences.

RESULTS: Most top-performing models on the external test set used BIP+SUSAN denoising–derived features. Overall, the use of SUSAN denoising and Combining Batches harmonization led to a slight but generally consistent improvement in model performance on the external test set.

CONCLUSIONS: The use of image-preprocessing steps such as SUSAN denoising and Combining Batches harmonization may be more useful in a multi-institutional setting to improve model generalizability. Models derived from only T1 contrast-enhanced images showed comparable performance to models derived from multiparametric MRI.

BIP
:   baseline image processing
CE
:   contrast-enhanced
ComBat
:   Combining Batches
ET
:   enhancing tumor
GB
:   glioblastoma
ICC
:   intraclass correlation coefficient
IMD
:   intracranial metastatic disease
mAUC
:   multiclass area under the receiver operating characteristic curve
ML
:   machine learning
PCNSL
:   primary central nervous system lymphomas
PTR
:   peritumoral region
SD
:   SUSAN denoising
SUSAN
:   Smallest Univalue Segment Assimilating Nucleus" />
<meta name="DC.Contributor" content="Girish Bathla" />
<meta name="DC.Contributor" content="Neetu Soni" />
<meta name="DC.Contributor" content="Ian T. Mark" />
<meta name="DC.Contributor" content="Yanan Liu" />
<meta name="DC.Contributor" content="Nicholas B. Larson" />
<meta name="DC.Contributor" content="Blake A. Kassmeyer" />
<meta name="DC.Contributor" content="Suyash Mohan" />
<meta name="DC.Contributor" content="John C. Benson" />
<meta name="DC.Contributor" content="Saima Rathore" />
<meta name="DC.Contributor" content="Amit K. Agarwal" />
<meta name="article:published_time" content="2024-09-01" />
<meta name="article:section" content="Artificial Intelligence" />
<meta name="citation_title" content="Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Feature variability in radiomics studies due to technical and magnet strength parameters is well-known and may be addressed through various preprocessing methods. However, very few studies have evaluated the downstream impact of variable preprocessing on model classification performance in a multiclass setting. We sought to evaluate the impact of Smallest Univalue Segment Assimilating Nucleus (SUSAN) denoising and Combining Batches harmonization on model classification performance.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;A total of 493 cases (410 internal and 83 external data sets) of glioblastoma, intracranial metastatic disease, and primary CNS lymphoma underwent semiautomated 3D-segmentation post-baseline image processing (BIP) consisting of resampling, realignment, coregistration, skull-stripping, and image normalization. Post-BIP, 2 sets were generated, one with and another without SUSAN denoising. Radiomics features were extracted from both data sets and batch-corrected to produce 4 data sets: (a) BIP, (b) BIP with SUSAN denoising, (c) BIP with Combining Batches, and (d) BIP with both SUSAN denoising and Combining Batches harmonization. Performance was then summarized for models using a combination of 6 feature-selection techniques and 6 machine learning models across 4 mask-sequence combinations with features derived from 1 to 3 (multiparametric) MRI sequences.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Most top-performing models on the external test set used BIP+SUSAN denoising–derived features. Overall, the use of SUSAN denoising and Combining Batches harmonization led to a slight but generally consistent improvement in model performance on the external test set.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;The use of image-preprocessing steps such as SUSAN denoising and Combining Batches harmonization may be more useful in a multi-institutional setting to improve model generalizability. Models derived from only T1 contrast-enhanced images showed comparable performance to models derived from multiparametric MRI.&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="2024/09/01" />
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<meta name="citation_issn" content="0195-6108" />
<meta name="citation_issn" content="1936-959X" />
<meta name="citation_doi" content="10.3174/ajnr.A8280" />
<meta name="citation_pmid" content="38604733" />
<meta name="citation_volume" content="45" />
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<meta name="citation_section" content="Artificial Intelligence" />
<meta name="citation_firstpage" content="1291" />
<meta name="citation_lastpage" content="1298" />
<meta name="citation_author" content="Girish Bathla" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (G.B., N.S.), University of Iowa Hospitals and Clinics, Iowa City, Iowa" />
<meta name="citation_author_institution" content="bDepartment of Radiology (G.B., I.T.M., J.C.B.), Mayo Clinic, Rochester, Minnesota" />
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<meta name="citation_author" content="Neetu Soni" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology (G.B., N.S.), University of Iowa Hospitals and Clinics, Iowa City, Iowa" />
<meta name="citation_author_institution" content="cDepartment of Radiology (N.S., A.K.A.), Mayo Clinic, Jacksonville, Florida" />
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<meta name="twitter:title" content="Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Feature variability in radiomics studies due to technical and magnet strength parameters is well-known and may be addressed through various preprocessing methods. However, very few studies have evaluated the downstream impact of variable preprocessing on model classification performance in a multiclass setting. We sought to evaluate the impact of Smallest Univalue Segment Assimilating Nucleus (SUSAN) denoising and Combining Batches harmonization on model classification performance.

MATERIALS AND METHODS: A total of 493 cases (410 internal and 83 external data sets) of glioblastoma, intracranial metastatic disease, and primary CNS lymphoma underwent semiautomated 3D-segmentation post-baseline image processing (BIP) consisting of resampling, realignment, coregistration, skull-stripping, and image normalization. Post-BIP, 2 sets were generated, one with and another without SUSAN denoising. Radiomics features were extracted from both data sets and batch-corrected to produce 4 data sets: (a) BIP, (b) BIP with SUSAN denoising, (c) BIP with Combining Batches, and (d) BIP with both SUSAN denoising and Combining Batches harmonization. Performance was then summarized for models using a combination of 6 feature-selection techniques and 6 machine learning models across 4 mask-sequence combinations with features derived from 1 to 3 (multiparametric) MRI sequences.

RESULTS: Most top-performing models on the external test set used BIP+SUSAN denoising–derived features. Overall, the use of SUSAN denoising and Combining Batches harmonization led to a slight but generally consistent improvement in model performance on the external test set.

CONCLUSIONS: The use of image-preprocessing steps such as SUSAN denoising and Combining Batches harmonization may be more useful in a multi-institutional setting to improve model generalizability. Models derived from only T1 contrast-enhanced images showed comparable performance to models derived from multiparametric MRI.

BIP
:   baseline image processing
CE
:   contrast-enhanced
ComBat
:   Combining Batches
ET
:   enhancing tumor
GB
:   glioblastoma
ICC
:   intraclass correlation coefficient
IMD
:   intracranial metastatic disease
mAUC
:   multiclass area under the receiver operating characteristic curve
ML
:   machine learning
PCNSL
:   primary central nervous system lymphomas
PTR
:   peritumoral region
SD
:   SUSAN denoising
SUSAN
:   Smallest Univalue Segment Assimilating Nucleus" />
<meta name="og-title" property="og:title" content="Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/45/9/1291" />
<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: Feature variability in radiomics studies due to technical and magnet strength parameters is well-known and may be addressed through various preprocessing methods. However, very few studies have evaluated the downstream impact of variable preprocessing on model classification performance in a multiclass setting. We sought to evaluate the impact of Smallest Univalue Segment Assimilating Nucleus (SUSAN) denoising and Combining Batches harmonization on model classification performance.

MATERIALS AND METHODS: A total of 493 cases (410 internal and 83 external data sets) of glioblastoma, intracranial metastatic disease, and primary CNS lymphoma underwent semiautomated 3D-segmentation post-baseline image processing (BIP) consisting of resampling, realignment, coregistration, skull-stripping, and image normalization. Post-BIP, 2 sets were generated, one with and another without SUSAN denoising. Radiomics features were extracted from both data sets and batch-corrected to produce 4 data sets: (a) BIP, (b) BIP with SUSAN denoising, (c) BIP with Combining Batches, and (d) BIP with both SUSAN denoising and Combining Batches harmonization. Performance was then summarized for models using a combination of 6 feature-selection techniques and 6 machine learning models across 4 mask-sequence combinations with features derived from 1 to 3 (multiparametric) MRI sequences.

RESULTS: Most top-performing models on the external test set used BIP+SUSAN denoising–derived features. Overall, the use of SUSAN denoising and Combining Batches harmonization led to a slight but generally consistent improvement in model performance on the external test set.

CONCLUSIONS: The use of image-preprocessing steps such as SUSAN denoising and Combining Batches harmonization may be more useful in a multi-institutional setting to improve model generalizability. Models derived from only T1 contrast-enhanced images showed comparable performance to models derived from multiparametric MRI.

BIP
:   baseline image processing
CE
:   contrast-enhanced
ComBat
:   Combining Batches
ET
:   enhancing tumor
GB
:   glioblastoma
ICC
:   intraclass correlation coefficient
IMD
:   intracranial metastatic disease
mAUC
:   multiclass area under the receiver operating characteristic curve
ML
:   machine learning
PCNSL
:   primary central nervous system lymphomas
PTR
:   peritumoral region
SD
:   SUSAN denoising
SUSAN
:   Smallest Univalue Segment Assimilating Nucleus" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node110103--5" data-node-nid="110103" id="top-node-110103--6951009454" data-pisa="ajnr;45/9/1291" data-pisa-master="ajnr;ajnr.A8280" data-apath="/ajnr/45/9/1291.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-jcore-article-title-complete clearfix has-author-tooltip highwire-citation-highwire-article-top-a">
  
  
      <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">Artificial Intelligence</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Girish Bathla</span>, <span class="highwire-citation-author" data-delta="1">Neetu Soni</span>, <span class="highwire-citation-author" data-delta="2">Ian T. Mark</span>, <span class="highwire-citation-author" data-delta="3">Yanan Liu</span>, <span class="highwire-citation-author" data-delta="4">Nicholas B. Larson</span>, <span class="highwire-citation-author" data-delta="5">Blake A. Kassmeyer</span>, <span class="highwire-citation-author" data-delta="6">Suyash Mohan</span>, <span class="highwire-citation-author" data-delta="7">John C. Benson</span>, <span class="highwire-citation-author" data-delta="8">Saima Rathore</span> and <span class="highwire-citation-author" data-delta="9">Amit K. Agarwal</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">September 2024,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(9) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1291-1298; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8280 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-110103--6951009454" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Girish Bathla </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 (G.B., N.S.), University of Iowa Hospitals and Clinics, Iowa City, Iowa</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (G.B., I.T.M., J.C.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=Girish%2BBathla%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=Bathla%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"><a href="/search/author1%3AGirish%2BBathla%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/0000-0003-3170-242X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Girish Bathla</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Neetu Soni </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 (G.B., N.S.), University of Iowa Hospitals and Clinics, Iowa City, Iowa</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Radiology (N.S., A.K.A.), Mayo Clinic, Jacksonville, Florida</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=Neetu%2BSoni%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=Soni%20N&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%3ANeetu%2BSoni%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/0000-0003-2082-1634" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Neetu Soni</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Ian T. Mark </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (G.B., I.T.M., J.C.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=Ian%2BT.%2BMark%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=Mark%20IT&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%3AIan%2BT.%2BMark%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/0000-0002-4036-2992" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Ian T. Mark</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Yanan Liu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Advanced Pulmonary Physiomic Imaging Laboratory (Y.L.), University of Iowa, Iowa City, Iowa</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=Yanan%2BLiu%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=Liu%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 last"><a href="/search/author1%3AYanan%2BLiu%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">Nicholas B. Larson </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>e</span>Division of Clinical Trials and Biostatistics (N.B.L., B.A.K.), Department of Quantitative Health Sciences, Mayo Clinic, Rochester, 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=Nicholas%2BB.%2BLarson%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=Larson%20NB&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%3ANicholas%2BB.%2BLarson%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/0000-0002-3468-4215" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Nicholas B. 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Benson </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (G.B., I.T.M., J.C.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=John%2BC.%2BBenson%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=Benson%20JC&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%3AJohn%2BC.%2BBenson%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/0000-0002-4038-5422" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for John C. 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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 9 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Sep 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">Sep 2024, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(9) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1291-1298; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8280 </span></div>
  
  
  
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">Girish</span> <span class="nlm-surname">Bathla</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Neetu</span> <span class="nlm-surname">Soni</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Ian T.</span> <span class="nlm-surname">Mark</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Yanan</span> <span class="nlm-surname">Liu</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Nicholas B.</span> <span class="nlm-surname">Larson</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Blake A.</span> <span class="nlm-surname">Kassmeyer</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Suyash</span> <span class="nlm-surname">Mohan</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">John C.</span> <span class="nlm-surname">Benson</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Saima</span> <span class="nlm-surname">Rathore</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Amit K.</span> <span class="nlm-surname">Agarwal</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">Sep 2024, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(9) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1291-1298; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8280 </span></div>
  
  
  
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