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<meta name="DC.Title" content="Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma" />
<meta name="DC.Identifier" content="10.3174/ajnr.A7488" />
<meta name="DC.Date" content="2022-05-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2022 by American Journal of Neuroradiology" />
<meta name="DC.AccessRights" content="restricted" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of treatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma ( n  = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites ( n  = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites ( n  = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points.

RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715).

CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

AUC
:   area under the curve
C-index
:   concordance index
ET
:   enhancing tumor
IQR
:   interquartile range
OS
:   overall survival
PFS
:   progression-free survival
PD-1
:   programmed cell death protein 1
PD-L1
:   programmed death-ligand 1
WT
:   whole tumor" />
<meta name="DC.Contributor" content="E. George" />
<meta name="DC.Contributor" content="E. Flagg" />
<meta name="DC.Contributor" content="K. Chang" />
<meta name="DC.Contributor" content="H.X. Bai" />
<meta name="DC.Contributor" content="H.J. Aerts" />
<meta name="DC.Contributor" content="M. Vallières" />
<meta name="DC.Contributor" content="D.A. Reardon" />
<meta name="DC.Contributor" content="R.Y. Huang" />
<meta name="article:published_time" content="2022-05-01" />
<meta name="article:section" content="Adult Brain" />
<meta name="citation_title" content="Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of treatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (&lt;i&gt;n&lt;/i&gt; = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (&lt;i&gt;n&lt;/i&gt; = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (&lt;i&gt;n&lt;/i&gt; = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715).&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
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<meta name="citation_author" content="E. George" />
<meta name="citation_author_institution" content="aFrom the Department of Radiology and Biomedical Imaging (E.G.), University of California San Francisco, San Francisco, California" />
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<meta name="citation_author" content="E. Flagg" />
<meta name="citation_author_institution" content="bDepartment of Radiology (E.F., R.Y.H.), Brigham and Women’s Hospital, Boston, Massachusetts" />
<meta name="citation_author_orcid" content="https://orcid.org/0000-0003-4111-9609" />
<meta name="citation_author" content="K. Chang" />
<meta name="citation_author_institution" content="cMassachusetts Institute of Technology (K.C.), Cambridge, Massachusetts" />
<meta name="citation_author_orcid" content="https://orcid.org/0000-0001-6956-5059" />
<meta name="citation_author" content="H.X. Bai" />
<meta name="citation_author_institution" content="dDepartment of Diagnostic Imaging (H.X.B.), Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, Rhode Island" />
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<meta name="twitter:title" content="Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma" />
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<meta name="twitter:image" content="http://ajnr-stage2.stage.highwire.org/sites/default/files/highwire/ajnr/43/5.cover-source.jpg" />
<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of treatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma ( n  = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites ( n  = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites ( n  = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points.

RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715).

CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

AUC
:   area under the curve
C-index
:   concordance index
ET
:   enhancing tumor
IQR
:   interquartile range
OS
:   overall survival
PFS
:   progression-free survival
PD-1
:   programmed cell death protein 1
PD-L1
:   programmed death-ligand 1
WT
:   whole tumor" />
<meta name="og-title" property="og:title" content="Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/43/5/675" />
<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: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of treatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma ( n  = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites ( n  = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites ( n  = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points.

RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715).

CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.

AUC
:   area under the curve
C-index
:   concordance index
ET
:   enhancing tumor
IQR
:   interquartile range
OS
:   overall survival
PFS
:   progression-free survival
PD-1
:   programmed cell death protein 1
PD-L1
:   programmed death-ligand 1
WT
:   whole tumor" />
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      <h1 class="highwire-cite-title" id="page-title">Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">E. George</span>, <span class="highwire-citation-author" data-delta="1">E. Flagg</span>, <span class="highwire-citation-author" data-delta="2">K. Chang</span>, <span class="highwire-citation-author" data-delta="3">H.X. Bai</span>, <span class="highwire-citation-author" data-delta="4">H.J. Aerts</span>, <span class="highwire-citation-author" data-delta="5">M. Vallières</span>, <span class="highwire-citation-author" data-delta="6">D.A. Reardon</span> and <span class="highwire-citation-author" data-delta="7">R.Y. Huang</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">May 2022,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">43 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(5) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">675-681; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A7488 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-96958--61864475383" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">E. George </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 and Biomedical Imaging (E.G.), University of California San Francisco, San Francisco, California</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=E.%2BGeorge%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=George%20E&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%3AE.%2BGeorge%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/0000-0003-3141-5738" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for E. George</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">E. Flagg </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (E.F., R.Y.H.), Brigham and Women’s Hospital, Boston, Massachusetts</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=E.%2BFlagg%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=Flagg%20E&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%3AE.%2BFlagg%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/0000-0003-4111-9609" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for E. Flagg</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">K. Chang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Massachusetts Institute of Technology (K.C.), Cambridge, Massachusetts</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=K.%2BChang%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=Chang%20K&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%3AK.%2BChang%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/0000-0001-6956-5059" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for K. Chang</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">H.X. Bai </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Department of Diagnostic Imaging (H.X.B.), Rhode Island Hospital and Warren Alpert Medical School of Brown University, Providence, Rhode Island</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=H.X.%2BBai%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=Bai%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%3AH.X.%2BBai%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/0000-0002-7460-8866" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for H.X. Bai</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">H.J. Aerts </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>e</span>Artificial Intelligence in Medicine Program (H.J.A.), Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>f</span>Departments of Radiation Oncology and Radiology (H.J.A.), Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts</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=H.J.%2BAerts%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=Aerts%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%3AH.J.%2BAerts%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/0000-0002-2122-2003" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for H.J. Aerts</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">M. Vallières </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>g</span>Department of Computer Science (M.V.), Université de Sherbrooke, Sherbrooke, Quebec, Canada</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=M.%2BValli%C3%A8res%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=Valli%C3%A8res%20M&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%3AM.%2BValli%25C3%25A8res%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/0000-0001-7639-8172" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M. 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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 43</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 5 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 May 2022 </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">May 2022, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">43 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(5) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">675-681; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A7488 </span></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">May 2022, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">43 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(5) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">675-681; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A7488 </span></div>
  
  
  
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