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Validation of an Artificial Intelligence–Based Prediction Model Using 5 External PET/CT Datasets of Diffuse Large B-Cell Lymphoma | Journal of Nuclear Medicine
<!DOCTYPE html> <html lang="en" dir="ltr" xmlns="http://www.w3.org/1999/xhtml" xmlns:mml="http://www.w3.org/1998/Math/MathML"> <head prefix="og: http://ogp.me/ns# article: http://ogp.me/ns/article# book: http://ogp.me/ns/book#" > <!--[if IE]><![endif]--> <link rel="dns-prefetch" href="//securepubads.g.doubleclick.net" /> <link rel="dns-prefetch" href="//scholar.google.com" /> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="shortcut icon" href="https://jnm.snmjournals.org/sites/default/files/images/favicon.ico" type="image/vnd.microsoft.icon" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <link rel="shortlink" href="/node/124185" /> <meta name="article_thumbnail" content="https://jnm.snmjournals.org/content/jnumed/65/11/1802/embed/icon-1.jpg" /> <meta name="issue_cover_image" content="https://jnm.snmjournals.org/sites/default/files/highwire/jnumed/65/11.cover-source.jpg" /> <meta name="type" content="article" /> <meta name="category" content="research-article" /> <meta name="HW.identifier" content="/jnumed/65/11/1802.atom" /> <meta name="HW.pisa" content="jnumed;65/11/1802" /> <meta name="DC.Format" content="text/html" /> <meta name="DC.Language" content="en" /> <meta name="DC.Title" content="Validation of an Artificial Intelligence–Based Prediction Model Using 5 External PET/CT Datasets of Diffuse Large B-Cell Lymphoma" /> <meta name="DC.Identifier" content="10.2967/jnumed.124.268191" /> <meta name="DC.Date" content="2024-11-01" /> <meta name="DC.Publisher" content="Society of Nuclear Medicine" /> <meta name="DC.Rights" content="© 2024 by the Society of Nuclear Medicine and Molecular Imaging." /> <meta name="DC.AccessRights" content="restricted" /> <meta name="DC.Description" content="The aim of this study was to validate a previously developed deep learning model in 5 independent clinical trials. The predictive performance of this model was compared with the international prognostic index (IPI) and 2 models incorporating radiomic PET/CT features (clinical PET and PET models). Methods: In total, 1,132 diffuse large B-cell lymphoma patients were included: 296 for training and 836 for external validation. The primary outcome was 2-y time to progression. The deep learning model was trained on maximum-intensity projections from PET/CT scans. The clinical PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, SUVpeak, age, and performance status. The PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, and SUVpeak. Model performance was assessed using the area under the curve (AUC) and Kaplan–Meier curves. Results: The IPI yielded an AUC of 0.60 on all external data. The deep learning model yielded a significantly higher AUC of 0.66 ( P < 0.01). For each individual clinical trial, the model was consistently better than IPI. Radiomic model AUCs remained higher for all clinical trials. The deep learning and clinical PET models showed equivalent performance (AUC, 0.69; P > 0.05). The PET model yielded the highest AUC of all models (AUC, 0.71; P < 0.05). Conclusion: The deep learning model predicted outcome in all trials with a higher performance than IPI and better survival curve separation. This model can predict treatment outcome in diffuse large B-cell lymphoma without tumor delineation but at the cost of a lower prognostic performance than with radiomics." /> <meta name="DC.Contributor" content="Maria C. Ferrández" /> <meta name="DC.Contributor" content="Sandeep S.V. Golla" /> <meta name="DC.Contributor" content="Jakoba J. Eertink" /> <meta name="DC.Contributor" content="Sanne E. Wiegers" /> <meta name="DC.Contributor" content="Gerben J.C. Zwezerijnen" /> <meta name="DC.Contributor" content="Martijn W. Heymans" /> <meta name="DC.Contributor" content="Pieternella J. Lugtenburg" /> <meta name="DC.Contributor" content="Lars Kurch" /> <meta name="DC.Contributor" content="Andreas Hüttmann" /> <meta name="DC.Contributor" content="Christine Hanoun" /> <meta name="DC.Contributor" content="Ulrich Dührsen" /> <meta name="DC.Contributor" content="Sally F. Barrington" /> <meta name="DC.Contributor" content="N. George Mikhaeel" /> <meta name="DC.Contributor" content="Luca Ceriani" /> <meta name="DC.Contributor" content="Emanuele Zucca" /> <meta name="DC.Contributor" content="Sándor Czibor" /> <meta name="DC.Contributor" content="Tamás Györke" /> <meta name="DC.Contributor" content="Martine E.D. Chamuleau" /> <meta name="DC.Contributor" content="Josée M. Zijlstra" /> <meta name="DC.Contributor" content="Ronald Boellaard" /> <meta name="article:published_time" content="2024-11-01" /> <meta name="article:section" content="Clinical Investigation" /> <meta name="citation_title" content="Validation of an Artificial Intelligence–Based Prediction Model Using 5 External PET/CT Datasets of Diffuse Large B-Cell Lymphoma" /> <meta name="citation_abstract" lang="en" content="<p>The aim of this study was to validate a previously developed deep learning model in 5 independent clinical trials. The predictive performance of this model was compared with the international prognostic index (IPI) and 2 models incorporating radiomic PET/CT features (clinical PET and PET models). <b>Methods:</b> In total, 1,132 diffuse large B-cell lymphoma patients were included: 296 for training and 836 for external validation. The primary outcome was 2-y time to progression. The deep learning model was trained on maximum-intensity projections from PET/CT scans. The clinical PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, SUV<sub>peak</sub>, age, and performance status. The PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, and SUV<sub>peak</sub>. Model performance was assessed using the area under the curve (AUC) and Kaplan–Meier curves. <b>Results:</b> The IPI yielded an AUC of 0.60 on all external data. The deep learning model yielded a significantly higher AUC of 0.66 (<i>P</i> &lt; 0.01). For each individual clinical trial, the model was consistently better than IPI. Radiomic model AUCs remained higher for all clinical trials. The deep learning and clinical PET models showed equivalent performance (AUC, 0.69; <i>P</i> &gt; 0.05). The PET model yielded the highest AUC of all models (AUC, 0.71; <i>P</i> &lt; 0.05). <b>Conclusion:</b> The deep learning model predicted outcome in all trials with a higher performance than IPI and better survival curve separation. This model can predict treatment outcome in diffuse large B-cell lymphoma without tumor delineation but at the cost of a lower prognostic performance than with radiomics.</p>" /> <meta name="citation_abstract" lang="en" scheme="graphical" content="<h3>Visual Abstract</h3> " /> <meta name="citation_journal_title" content="Journal of Nuclear Medicine" /> <meta name="citation_publisher" content="Society of Nuclear Medicine" /> <meta name="citation_publication_date" content="2024/11/01" /> <meta name="citation_mjid" content="jnumed;65/11/1802" /> <meta name="citation_id" content="65/11/1802" /> <meta name="citation_public_url" content="https://jnm.snmjournals.org/content/65/11/1802" /> <meta name="citation_abstract_html_url" content="https://jnm.snmjournals.org/content/65/11/1802.abstract" /> <meta name="citation_full_html_url" content="https://jnm.snmjournals.org/content/65/11/1802.full" /> <meta name="citation_pdf_url" content="https://jnm.snmjournals.org/content/jnumed/65/11/1802.full.pdf" /> <meta name="citation_issn" content="0161-5505" /> <meta name="citation_issn" content="2159-662X" /> <meta name="citation_doi" content="10.2967/jnumed.124.268191" /> <meta name="citation_pmid" content="39362767" /> <meta name="citation_volume" content="65" /> <meta name="citation_issue" content="11" /> <meta name="citation_num_pages" content="6" /> <meta name="citation_article_type" content="Research Article" /> <meta name="citation_section" content="Clinical Investigation" /> <meta name="citation_firstpage" content="1802" /> <meta name="citation_lastpage" content="1807" /> <meta name="citation_author" content="Maria C. Ferrández" /> <meta name="citation_author_institution" content="Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author_institution" content="Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author" content="Sandeep S.V. Golla" /> <meta name="citation_author_institution" content="Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author_institution" content="Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author" content="Jakoba J. Eertink" /> <meta name="citation_author_institution" content="Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author_institution" content="Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author" content="Sanne E. Wiegers" /> <meta name="citation_author_institution" content="Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author_institution" content="Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author" content="Gerben J.C. Zwezerijnen" /> <meta name="citation_author_institution" content="Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author_institution" content="Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author" content="Martijn W. Heymans" /> <meta name="citation_author_institution" content="Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author" content="Pieternella J. Lugtenburg" /> <meta name="citation_author_institution" content="Department of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands;" /> <meta name="citation_author" content="Lars Kurch" /> <meta name="citation_author_institution" content="Clinic and Polyclinic for Nuclear Medicine, Department of Nuclear Medicine, University of Leipzig, Leipzig, Germany;" /> <meta name="citation_author" content="Andreas Hüttmann" /> <meta name="citation_author_institution" content="Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg–Essen, Essen, Germany;" /> <meta name="citation_author" content="Christine Hanoun" /> <meta name="citation_author_institution" content="Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg–Essen, Essen, Germany;" /> <meta name="citation_author" content="Ulrich Dührsen" /> <meta name="citation_author_institution" content="Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg–Essen, Essen, Germany;" /> <meta name="citation_author" content="Sally F. Barrington" /> <meta name="citation_author_institution" content="School of Biomedical Engineering and Imaging Sciences, King’s College London and Guy’s and St Thomas’ PET Centre, King’s Health Partners, King’s College London, London, United Kingdom;" /> <meta name="citation_author" content="N. George Mikhaeel" /> <meta name="citation_author_institution" content="Department of Clinical Oncology, Guy’s Cancer Centre and School of Cancer and Pharmaceutical Sciences, King’s College London, London, United Kingdom;" /> <meta name="citation_author" content="Luca Ceriani" /> <meta name="citation_author_institution" content="Department of Nuclear Medicine and PET/CT Centre, Imaging Institute of Southern Switzerland–EOC, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland;" /> <meta name="citation_author_institution" content="SAKK Swiss Group for Clinical Cancer Research, Bern, Switzerland;" /> <meta name="citation_author" content="Emanuele Zucca" /> <meta name="citation_author_institution" content="SAKK Swiss Group for Clinical Cancer Research, Bern, Switzerland;" /> <meta name="citation_author_institution" content="Department of Oncology, Oncology Institute of Southern Switzerland–EOC, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Bellinzona, Switzerland;" /> <meta name="citation_author_institution" content="Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland; and" /> <meta name="citation_author" content="Sándor Czibor" /> <meta name="citation_author_institution" content="Department of Nuclear Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary" /> <meta name="citation_author" content="Tamás Györke" /> <meta name="citation_author_institution" content="Department of Nuclear Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary" /> <meta name="citation_author" content="Martine E.D. 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Zijlstra" /> <meta name="citation_author_institution" content="Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author_institution" content="Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author" content="Ronald Boellaard" /> <meta name="citation_author_institution" content="Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_author_institution" content="Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands;" /> <meta name="citation_reference" content="citation_journal_title=Eur J Nucl Med Mol Imaging.;citation_author=R. Boellaard;citation_author=R. Delgado-Bolton;citation_author=WJ. 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Accessed September 18, 2024." /> <meta name="twitter:title" content="Validation of an Artificial Intelligence–Based Prediction Model Using 5 External PET/CT Datasets of Diffuse Large B-Cell Lymphoma" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:image" content="https://jnm.snmjournals.org/sites/default/files/highwire/jnumed/65/11.cover-source.jpg" /> <meta name="twitter:description" content="The aim of this study was to validate a previously developed deep learning model in 5 independent clinical trials. The predictive performance of this model was compared with the international prognostic index (IPI) and 2 models incorporating radiomic PET/CT features (clinical PET and PET models). Methods: In total, 1,132 diffuse large B-cell lymphoma patients were included: 296 for training and 836 for external validation. The primary outcome was 2-y time to progression. The deep learning model was trained on maximum-intensity projections from PET/CT scans. The clinical PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, SUVpeak, age, and performance status. The PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, and SUVpeak. Model performance was assessed using the area under the curve (AUC) and Kaplan–Meier curves. Results: The IPI yielded an AUC of 0.60 on all external data. The deep learning model yielded a significantly higher AUC of 0.66 ( P < 0.01). For each individual clinical trial, the model was consistently better than IPI. Radiomic model AUCs remained higher for all clinical trials. The deep learning and clinical PET models showed equivalent performance (AUC, 0.69; P > 0.05). The PET model yielded the highest AUC of all models (AUC, 0.71; P < 0.05). Conclusion: The deep learning model predicted outcome in all trials with a higher performance than IPI and better survival curve separation. This model can predict treatment outcome in diffuse large B-cell lymphoma without tumor delineation but at the cost of a lower prognostic performance than with radiomics." /> <meta name="og-title" property="og:title" content="Validation of an Artificial Intelligence–Based Prediction Model Using 5 External PET/CT Datasets of Diffuse Large B-Cell Lymphoma" /> <meta name="og-url" property="og:url" content="https://jnm.snmjournals.org/content/65/11/1802" /> <meta name="og-site-name" property="og:site_name" content="Journal of Nuclear Medicine" /> <meta name="og-description" property="og:description" content="The aim of this study was to validate a previously developed deep learning model in 5 independent clinical trials. The predictive performance of this model was compared with the international prognostic index (IPI) and 2 models incorporating radiomic PET/CT features (clinical PET and PET models). Methods: In total, 1,132 diffuse large B-cell lymphoma patients were included: 296 for training and 836 for external validation. The primary outcome was 2-y time to progression. The deep learning model was trained on maximum-intensity projections from PET/CT scans. The clinical PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, SUVpeak, age, and performance status. The PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, and SUVpeak. Model performance was assessed using the area under the curve (AUC) and Kaplan–Meier curves. Results: The IPI yielded an AUC of 0.60 on all external data. The deep learning model yielded a significantly higher AUC of 0.66 ( P < 0.01). For each individual clinical trial, the model was consistently better than IPI. Radiomic model AUCs remained higher for all clinical trials. The deep learning and clinical PET models showed equivalent performance (AUC, 0.69; P > 0.05). The PET model yielded the highest AUC of all models (AUC, 0.71; P < 0.05). Conclusion: The deep learning model predicted outcome in all trials with a higher performance than IPI and better survival curve separation. This model can predict treatment outcome in diffuse large B-cell lymphoma without tumor delineation but at the cost of a lower prognostic performance than with radiomics." /> <meta name="og-type" property="og:type" content="article" /> <meta name="og-image" property="og:image" content="https://jnm.snmjournals.org/sites/default/files/highwire/jnumed/65/11.cover-source.jpg" /> <link rel="canonical" href="https://jnm.snmjournals.org/content/65/11/1802" /> <meta name="Generator" content="Drupal 7 (http://drupal.org)" /> <link rel="alternate" type="application/pdf" title="Full Text (PDF)" href="/content/65/11/1802.full.pdf" /> <link rel="alternate" type="text/plain" title="Full Text (Plain)" href="/content/65/11/1802.full.txt" /> <link rel="alternate" type="application/vnd.ms-powerpoint" title="Powerpoint" href="/content/65/11/1802.ppt" /> <title>Validation of an Artificial Intelligence–Based Prediction Model Using 5 External PET/CT Datasets of Diffuse Large B-Cell Lymphoma | Journal of 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style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1008px;padding-left:18px;">Hausa</option><option value="en|haw" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1488px;padding-left:18px;">Hawaiian</option><option value="en|iw" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -416px;padding-left:18px;">Hebrew</option><option value="en|hi" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -208px;padding-left:18px;">Hindi</option><option value="en|hmn" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1024px;padding-left:18px;">Hmong</option><option value="en|hu" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 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style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -240px;padding-left:18px;">Japanese</option><option value="en|jw" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1056px;padding-left:18px;">Javanese</option><option value="en|kn" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1072px;padding-left:18px;">Kannada</option><option value="en|kk" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1328px;padding-left:18px;">Kazakh</option><option value="en|km" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1088px;padding-left:18px;">Khmer</option><option value="en|ko" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -256px;padding-left:18px;">Korean</option><option value="en|ku" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1504px;padding-left:18px;">Kurdish (Kurmanji)</option><option value="en|ky" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1520px;padding-left:18px;">Kyrgyz</option><option value="en|lo" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1104px;padding-left:18px;">Lao</option><option value="en|la" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1120px;padding-left:18px;">Latin</option><option value="en|lv" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -448px;padding-left:18px;">Latvian</option><option value="en|lt" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -464px;padding-left:18px;">Lithuanian</option><option value="en|lb" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1536px;padding-left:18px;">Luxembourgish</option><option value="en|mk" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -800px;padding-left:18px;">Macedonian</option><option value="en|mg" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1344px;padding-left:18px;">Malagasy</option><option value="en|ms" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -704px;padding-left:18px;">Malay</option><option value="en|ml" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1360px;padding-left:18px;">Malayalam</option><option value="en|mt" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -624px;padding-left:18px;">Maltese</option><option value="en|mi" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1136px;padding-left:18px;">Maori</option><option value="en|mr" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1152px;padding-left:18px;">Marathi</option><option value="en|mn" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1168px;padding-left:18px;">Mongolian</option><option value="en|my" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1296px;padding-left:18px;">Myanmar (Burmese)</option><option value="en|ne" 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style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -480px;padding-left:18px;">Serbian</option><option value="en|st" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1392px;padding-left:18px;">Sesotho</option><option value="en|sn" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1600px;padding-left:18px;">Shona</option><option value="en|sd" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1616px;padding-left:18px;">Sindhi</option><option value="en|si" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 -1376px;padding-left:18px;">Sinhala</option><option value="en|sk" style="background:url('/sites/all/modules/contrib/gtranslate/gtranslate-files/16l.png') no-repeat scroll 0 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container-30"> </div> </header> <section id="section-content" class="section section-content"> <div id="zone-content" class="zone zone-content clearfix container-30"> <div class="grid-28 suffix-1 prefix-1 region region-content" id="region-content"> <div class="region-inner region-content-inner"> <a id="main-content"></a> <div class="block block-system block-main block-system-main odd block-without-title" id="block-system-main"> <div class="block-inner clearfix"> <div class="content clearfix"> <div class="panel-display panels-960-layout jcore-2col-layout" > <div class="panel-row-wrapper panel-row-first clearfix"> <div class="top-wrapper"> <div class="panel-panel panel-region-top"> <div class="inside"><div class="panel-pane pane-highwire-article-citation" > <div class="pane-content"> <div class="highwire-article-citation highwire-citation-type-highwire-article node124185--5" data-node-nid="124185" id="top-node-124185--6489817720" data-pisa="jnumed;65/11/1802" data-pisa-master="jnumed;jnumed.124.268191" data-apath="/jnumed/65/11/1802.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">Clinical Investigation</span></span></div> <div class="highwire-cite-access"><span class="highwire-citation-access highwire-citation-access-check" data-pisa-id="jnumed;jnumed.124.268191" data-atom-uri="/jnumed/65/11/1802.atom" data-request-view="full"></span></div> <h1 class="highwire-cite-title" id="page-title">Validation of an Artificial Intelligence–Based Prediction Model Using 5 External PET/CT Datasets of Diffuse Large B-Cell Lymphoma</h1> <div class="highwire-cite-authors"><span class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Maria C. Ferrández</span>, <span class="highwire-citation-author" data-delta="1">Sandeep S.V. Golla</span>, <span class="highwire-citation-author" data-delta="2">Jakoba J. Eertink</span>, <span class="highwire-citation-author" data-delta="3">Sanne E. Wiegers</span>, <span class="highwire-citation-author" data-delta="4">Gerben J.C. Zwezerijnen</span>, <span class="highwire-citation-author" data-delta="5">Martijn W. Heymans</span>, <span class="highwire-citation-author" data-delta="6">Pieternella J. Lugtenburg</span>, <span class="highwire-citation-author" data-delta="7">Lars Kurch</span>, <span class="highwire-citation-author" data-delta="8">Andreas Hüttmann</span>, <span class="highwire-citation-author" data-delta="9">Christine Hanoun</span>, <span class="highwire-citation-author" data-delta="10">Ulrich Dührsen</span>, <span class="highwire-citation-author" data-delta="11">Sally F. Barrington</span>, <span class="highwire-citation-author" data-delta="12">N. George Mikhaeel</span>, <span class="highwire-citation-author" data-delta="13">Luca Ceriani</span>, <span class="highwire-citation-author" data-delta="14">Emanuele Zucca</span>, <span class="highwire-citation-author" data-delta="15">Sándor Czibor</span>, <span class="highwire-citation-author" data-delta="16">Tamás Györke</span>, <span class="highwire-citation-author" data-delta="17">Martine E.D. Chamuleau</span>, <span class="highwire-citation-author" data-delta="18">Josée M. Zijlstra</span> and <span class="highwire-citation-author" data-delta="19">Ronald Boellaard; <span class="nlm-on-behalf-of">on behalf of the PETRA Consortium</span></span></span></div> <div class="highwire-cite-metadata"><span class="highwire-cite-metadata-journal highwire-cite-metadata">Journal of Nuclear Medicine </span><span class="highwire-cite-metadata-date highwire-cite-metadata">November 2024, </span><span class="highwire-cite-metadata-volume highwire-cite-metadata">65 </span><span class="highwire-cite-metadata-issue highwire-cite-metadata">(11) </span><span class="highwire-cite-metadata-pages highwire-cite-metadata">1802-1807; </span><span class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.2967/jnumed.124.268191 </span></div> <div class="highwire-cite-extras"><span class="highwire-foxycart-add-to-cart-ahah highwire-foxycart-add-to-cart-ahah" data-text="Add to Cart (%short-price)" data-apath="/jnumed/65/11/1802.atom" data-type="link" data-font-icon="" data-parent-id="124170"></span></div> </div> <div id="hw-article-author-popups-top-node-124185--6489817720" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Maria C. Ferrández </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span>Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;</div><div class='author-affiliation'><span class='nlm-sup'>2</span>Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands;</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Maria%2BC.%2BFerr%C3%A1ndez%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=Ferr%C3%A1ndez%20MC&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%3AMaria%2BC.%2BFerr%25C3%25A1ndez%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-1"><div class="author-tooltip-name">Sandeep S.V. Golla </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span>Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;</div><div class='author-affiliation'><span class='nlm-sup'>2</span>Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands;</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Sandeep%2BS.V.%2BGolla%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=Golla%20SS&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%3ASandeep%2BS.V.%2BGolla%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">Jakoba J. 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Barrington </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>8</span>School of Biomedical Engineering and Imaging Sciences, King’s College London and Guy’s and St Thomas’ PET Centre, King’s Health Partners, King’s College London, London, United Kingdom;</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Sally%2BF.%2BBarrington%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=Barrington%20SF&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%3ASally%2BF.%2BBarrington%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-12"><div class="author-tooltip-name">N. George Mikhaeel </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>9</span>Department of Clinical Oncology, Guy’s Cancer Centre and School of Cancer and Pharmaceutical Sciences, King’s College London, London, United Kingdom;</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=N.%2BGeorge%2BMikhaeel%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=Mikhaeel%20NG&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%3AN.%2BGeorge%2BMikhaeel%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-13"><div class="author-tooltip-name">Luca Ceriani </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>10</span>Department of Nuclear Medicine and PET/CT Centre, Imaging Institute of Southern Switzerland–EOC, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland;</div><div class='author-affiliation'><span class='nlm-sup'>11</span>SAKK Swiss Group for Clinical Cancer Research, Bern, Switzerland;</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Luca%2BCeriani%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=Ceriani%20L&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%3ALuca%2BCeriani%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-14"><div class="author-tooltip-name">Emanuele Zucca </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>11</span>SAKK Swiss Group for Clinical Cancer Research, Bern, Switzerland;</div><div class='author-affiliation'><span class='nlm-sup'>12</span>Department of Oncology, Oncology Institute of Southern Switzerland–EOC, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Bellinzona, Switzerland;</div><div class='author-affiliation'><span class='nlm-sup'>13</span>Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland; and</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Emanuele%2BZucca%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=Zucca%20E&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%3AEmanuele%2BZucca%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-15"><div class="author-tooltip-name">Sándor Czibor </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>14</span>Department of Nuclear Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=S%C3%A1ndor%2BCzibor%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=Czibor%20S&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%3AS%25C3%25A1ndor%2BCzibor%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-16"><div class="author-tooltip-name">Tamás Györke </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>14</span>Department of Nuclear Medicine, Medical Imaging Centre, Semmelweis University, Budapest, Hungary</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Tam%C3%A1s%2BGy%C3%B6rke%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=Gy%C3%B6rke%20T&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%3ATam%25C3%25A1s%2BGy%25C3%25B6rke%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-17"><div class="author-tooltip-name">Martine E.D. Chamuleau </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>2</span>Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands;</div><div class='author-affiliation'><span class='nlm-sup'>3</span>Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Martine%2BE.D.%2BChamuleau%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=Chamuleau%20ME&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%3AMartine%2BE.D.%2BChamuleau%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-18"><div class="author-tooltip-name">Josée M. Zijlstra </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>2</span>Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands;</div><div class='author-affiliation'><span class='nlm-sup'>3</span>Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jos%C3%A9e%2BM.%2BZijlstra%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=Zijlstra%20JM&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%3AJos%25C3%25A9e%2BM.%2BZijlstra%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-19"><div class="author-tooltip-name">Ronald Boellaard </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span>Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;</div><div class='author-affiliation'><span class='nlm-sup'>2</span>Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, The Netherlands;</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Ronald%2BBoellaard%2B" target="_blank" class="" data-icon-position="" 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class="">Visual Abstract</h2><div id="F1" class="fig pos-float type-featured graphic-abstract odd"><div class="highwire-figure"><div class="fig-inline-img-wrapper"><div class="fig-inline-img"><a href="https://jnm.snmjournals.org/content/jnumed/65/11/1802/F1.large.jpg?width=800&height=600&carousel=1" title="" class="highwire-fragment fragment-images colorbox-load" rel="gallery-fragment-images-794404999" data-figure-caption="<div class="highwire-markup"></div>" data-icon-position="" data-hide-link-title="0"><span class="hw-responsive-img"><img class="highwire-fragment fragment-image lazyload" alt="Figure" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="https://jnm.snmjournals.org/content/jnumed/65/11/1802/F1.medium.gif" width="440" height="213"/><noscript><img class="highwire-fragment fragment-image" alt="Figure" src="https://jnm.snmjournals.org/content/jnumed/65/11/1802/F1.medium.gif" width="440" height="213"/></noscript></span></a></div></div><ul class="highwire-figure-links inline"><li class="download-fig first"><a href="https://jnm.snmjournals.org/content/jnumed/65/11/1802/F1.large.jpg?download=true" class="highwire-figure-link highwire-figure-link-download" title="Download Figure1" data-icon-position="" data-hide-link-title="0">Download figure</a></li><li class="new-tab"><a href="https://jnm.snmjournals.org/content/jnumed/65/11/1802/F1.large.jpg" class="highwire-figure-link highwire-figure-link-newtab" target="_blank" data-icon-position="" data-hide-link-title="0">Open in new tab</a></li><li class="download-ppt last"><a href="/highwire/powerpoint/124207" class="highwire-figure-link highwire-figure-link-ppt" data-icon-position="" data-hide-link-title="0">Download powerpoint</a></li></ul></div></div><p id="p-2"></p></div><div class="section abstract" id="abstract-2"><h2>Abstract</h2><p id="p-3">The aim of this study was to validate a previously developed deep learning model in 5 independent clinical trials. The predictive performance of this model was compared with the international prognostic index (IPI) and 2 models incorporating radiomic PET/CT features (clinical PET and PET models). <strong>Methods:</strong> In total, 1,132 diffuse large B-cell lymphoma patients were included: 296 for training and 836 for external validation. The primary outcome was 2-y time to progression. The deep learning model was trained on maximum-intensity projections from PET/CT scans. The clinical PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, SUV<sub>peak</sub>, age, and performance status. The PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, and SUV<sub>peak</sub>. Model performance was assessed using the area under the curve (AUC) and Kaplan–Meier curves. <strong>Results:</strong> The IPI yielded an AUC of 0.60 on all external data. The deep learning model yielded a significantly higher AUC of 0.66 (<em>P</em> < 0.01). For each individual clinical trial, the model was consistently better than IPI. Radiomic model AUCs remained higher for all clinical trials. The deep learning and clinical PET models showed equivalent performance (AUC, 0.69; <em>P</em> > 0.05). The PET model yielded the highest AUC of all models (AUC, 0.71; <em>P</em> < 0.05). <strong>Conclusion:</strong> The deep learning model predicted outcome in all trials with a higher performance than IPI and better survival curve separation. This model can predict treatment outcome in diffuse large B-cell lymphoma without tumor delineation but at the cost of a lower prognostic performance than with radiomics.</p></div><ul class="kwd-group"><li class="kwd"><a href="/keyword/diffuse-large-b-cell-lymphoma" class="hw-term hw-article-keyword hw-article-keyword-diffuse-large-b-cell-lymphoma" rel="nofollow">diffuse large B-cell lymphoma</a></li><li class="kwd"><a href="/keyword/maximum-intensity-projection" class="hw-term hw-article-keyword hw-article-keyword-maximum-intensity-projection" rel="nofollow">maximum-intensity projection</a></li><li class="kwd"><a href="/keyword/convolutional-neural-networks" class="hw-term hw-article-keyword hw-article-keyword-convolutional-neural-networks" rel="nofollow">convolutional neural networks</a></li><li class="kwd"><a href="/keyword/time-progression" class="hw-term hw-article-keyword hw-article-keyword-time-to-progression" rel="nofollow">time to progression</a></li><li class="kwd"><a href="/keyword/prediction" class="hw-term hw-article-keyword hw-article-keyword-prediction" rel="nofollow">prediction</a></li></ul><div class="section fn-group" id="fn-group-1"><h2>Footnotes</h2><ul><li class="fn" id="fn-1"><p id="p-1">Published online Oct. 3, 2024.</p></li></ul></div><ul class="copyright-statement"><li class="fn" id="copyright-statement-1">© 2024 by the Society of Nuclear Medicine and Molecular Imaging.</li></ul><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div><a href="https://jnm.snmjournals.org/content/65/11/1802.full" class="hw-link hw-link-article-full-text" data-icon-position="" data-hide-link-title="0">View Full Text</a></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-snippet pane-article-access-text" > <div class="pane-content"> <div class="snippet article-access-text" id="article-access-text"> <div class="snippet-content"> <p>This article requires a subscription to view the full text. 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src="https://jnm.snmjournals.org/sites/default/files/styles/medium/public/highwire/jnumed/65/11.cover-source.jpg?itok=fDHXkY3d" alt="Journal of Nuclear Medicine: 65 (11)" /></a></div> <div class="highwire-cite-col"> <a href="/content/65/11" class="highwire-cite-linked-title" data-icon-position="" data-hide-link-title="0"><span class="highwire-cite-title">Journal of Nuclear Medicine</span></a> <div class="highwire-cite-metadata"><span class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 65</span>, <span class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 11 </span><div class="highwire-cite-metadata-pubdate highwire-cite-metadata">November 1, 2024 </div></div> <div class="highwire-cite-extras"><div class='highwire-article-citation-variant-list'><ul class="links variants-list"><li class="toc first"><a href="/content/65/11.toc" class="highwire-variant-link variant-toc " title="Table of Contents" rel="alternate" type="text/html" data-icon-position="" 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George</span> <span class="nlm-surname">Mikhaeel</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">Luca</span> <span class="nlm-surname">Ceriani</span></span>, <span class="highwire-citation-author" data-delta="14"><span class="nlm-given-names">Emanuele</span> <span class="nlm-surname">Zucca</span></span>, <span class="highwire-citation-author" data-delta="15"><span class="nlm-given-names">Sándor</span> <span class="nlm-surname">Czibor</span></span>, <span class="highwire-citation-author" data-delta="16"><span class="nlm-given-names">Tamás</span> <span class="nlm-surname">Györke</span></span>, <span class="highwire-citation-author" data-delta="17"><span class="nlm-given-names">Martine E.D.</span> <span class="nlm-surname">Chamuleau</span></span>, <span class="highwire-citation-author" data-delta="18"><span class="nlm-given-names">Josée M.</span> <span class="nlm-surname">Zijlstra</span></span>, <span class="highwire-citation-author" 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data-delta="19"><span class="nlm-given-names">Ronald</span> <span class="nlm-surname">Boellaard</span></span></span></div> <div class="highwire-cite-metadata"><span class="highwire-cite-metadata-journal highwire-cite-metadata">Journal of Nuclear Medicine </span><span class="highwire-cite-metadata-date highwire-cite-metadata">Nov 2024, </span><span class="highwire-cite-metadata-volume highwire-cite-metadata">65 </span><span class="highwire-cite-metadata-issue highwire-cite-metadata">(11) </span><span class="highwire-cite-metadata-pages highwire-cite-metadata">1802-1807; </span><span class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.2967/jnumed.124.268191 </span></div> </div> </div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-highwire-article-clipboard-copy large-margin-bottom" > <div class="pane-content"> <div class = "clipboard-copy"> <span class="label-url"> <label for="dynamic">Share This Article:</label> 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