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<meta name="DC.Title" content="Qualifying Certainty in Radiology Reports through Deep Learning–Based Natural Language Processing" />
<meta name="DC.Identifier" content="10.3174/ajnr.A7241" />
<meta name="DC.Date" content="2021-10-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2021 by American Journal of Neuroradiology" />
<meta name="DC.AccessRights" content="restricted" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Communication gaps exist between radiologists and referring physicians in conveying diagnostic certainty. We aimed to explore deep learning–based bidirectional contextual language models for automatically assessing diagnostic certainty expressed in the radiology reports to facilitate the precision of communication.

MATERIALS AND METHODS: We randomly sampled 594 head MR imaging reports from an academic medical center. We asked 3 board-certified radiologists to read sentences from the Impression section and assign each sentence 1 of the 4 certainty categories: “Non-Definitive,” “Definitive-Mild,” “Definitive-Strong,” “Other.” Using the annotated 2352 sentences, we developed and validated a natural language-processing system based on the start-of-the-art bidirectional encoder representations from transformers (BERT), which can capture contextual uncertainty semantics beyond the lexicon level. Finally, we evaluated 3 BERT variant models and reported standard metrics including sensitivity, specificity, and area under the curve.

RESULTS: A κ score of 0.74 was achieved for interannotator agreement on uncertainty interpretations among 3 radiologists. For the 3 BERT variant models, the biomedical variant (BioBERT) achieved the best macro-average area under the curve of 0.931 (compared with 0.928 for the BERT-base and 0.925 for the clinical variant [ClinicalBERT]) on the validation data. All 3 models yielded high macro-average specificity (93.13%–93.65%), while the BERT-base obtained the highest macro-average sensitivity of 79.46% (compared with 79.08% for BioBERT and 78.52% for ClinicalBERT). The BioBERT model showed great generalizability on the heldout test data with a macro-average sensitivity of 77.29%, specificity of 92.89%, and area under the curve of 0.93.

CONCLUSIONS: A deep transfer learning model can be developed to reliably assess the level of uncertainty communicated in a radiology report.

AUC
:   area under the receiver operating characteristic curve
BERT
:   bidirectional encoder representations from transformers
BioBERT
:   biomedical variant of BERT
ClinicalBERT
:   clinical variant of BERT
NLP
:   natural language processing
QC-RAD
:   qualifying certainty in radiology reports" />
<meta name="DC.Contributor" content="F. Liu" />
<meta name="DC.Contributor" content="P. Zhou" />
<meta name="DC.Contributor" content="S.J. Baccei" />
<meta name="DC.Contributor" content="M.J. Masciocchi" />
<meta name="DC.Contributor" content="N. Amornsiripanitch" />
<meta name="DC.Contributor" content="C.I. Kiefe" />
<meta name="DC.Contributor" content="M.P. Rosen" />
<meta name="article:published_time" content="2021-10-01" />
<meta name="article:section" content="Practice Perspectives" />
<meta name="citation_title" content="Qualifying Certainty in Radiology Reports through Deep Learning–Based Natural Language Processing" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Communication gaps exist between radiologists and referring physicians in conveying diagnostic certainty. We aimed to explore deep learning–based bidirectional contextual language models for automatically assessing diagnostic certainty expressed in the radiology reports to facilitate the precision of communication.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;We randomly sampled 594 head MR imaging reports from an academic medical center. We asked 3 board-certified radiologists to read sentences from the Impression section and assign each sentence 1 of the 4 certainty categories: “Non-Definitive,” “Definitive-Mild,” “Definitive-Strong,” “Other.” Using the annotated 2352 sentences, we developed and validated a natural language-processing system based on the start-of-the-art bidirectional encoder representations from transformers (BERT), which can capture contextual uncertainty semantics beyond the lexicon level. Finally, we evaluated 3 BERT variant models and reported standard metrics including sensitivity, specificity, and area under the curve.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;A κ score of 0.74 was achieved for interannotator agreement on uncertainty interpretations among 3 radiologists. For the 3 BERT variant models, the biomedical variant (BioBERT) achieved the best macro-average area under the curve of 0.931 (compared with 0.928 for the BERT-base and 0.925 for the clinical variant [ClinicalBERT]) on the validation data. All 3 models yielded high macro-average specificity (93.13%–93.65%), while the BERT-base obtained the highest macro-average sensitivity of 79.46% (compared with 79.08% for BioBERT and 78.52% for ClinicalBERT). The BioBERT model showed great generalizability on the heldout test data with a macro-average sensitivity of 77.29%, specificity of 92.89%, and area under the curve of 0.93.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;A deep transfer learning model can be developed to reliably assess the level of uncertainty communicated in a radiology report.&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="2021/10/01" />
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<meta name="citation_author" content="F. Liu" />
<meta name="citation_author_institution" content="aFrom the Department of Population and Quantitative Health Sciences (F.L., C.I.K.), University of Massachusetts Medical School, Worcester, Massachusetts" />
<meta name="citation_author_institution" content="bDepartment of Radiology (F.L., P.Z., S.J.B., M.J.M., N.A., M.P.R.), University of Massachusetts Medical School, Worcester, Massachusetts" />
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<meta name="citation_author" content="P. Zhou" />
<meta name="citation_author_institution" content="bDepartment of Radiology (F.L., P.Z., S.J.B., M.J.M., N.A., M.P.R.), University of Massachusetts Medical School, Worcester, Massachusetts" />
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<meta name="citation_author" content="S.J. Baccei" />
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<meta name="twitter:title" content="Qualifying Certainty in Radiology Reports through Deep Learning–Based Natural Language Processing" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Communication gaps exist between radiologists and referring physicians in conveying diagnostic certainty. We aimed to explore deep learning–based bidirectional contextual language models for automatically assessing diagnostic certainty expressed in the radiology reports to facilitate the precision of communication.

MATERIALS AND METHODS: We randomly sampled 594 head MR imaging reports from an academic medical center. We asked 3 board-certified radiologists to read sentences from the Impression section and assign each sentence 1 of the 4 certainty categories: “Non-Definitive,” “Definitive-Mild,” “Definitive-Strong,” “Other.” Using the annotated 2352 sentences, we developed and validated a natural language-processing system based on the start-of-the-art bidirectional encoder representations from transformers (BERT), which can capture contextual uncertainty semantics beyond the lexicon level. Finally, we evaluated 3 BERT variant models and reported standard metrics including sensitivity, specificity, and area under the curve.

RESULTS: A κ score of 0.74 was achieved for interannotator agreement on uncertainty interpretations among 3 radiologists. For the 3 BERT variant models, the biomedical variant (BioBERT) achieved the best macro-average area under the curve of 0.931 (compared with 0.928 for the BERT-base and 0.925 for the clinical variant [ClinicalBERT]) on the validation data. All 3 models yielded high macro-average specificity (93.13%–93.65%), while the BERT-base obtained the highest macro-average sensitivity of 79.46% (compared with 79.08% for BioBERT and 78.52% for ClinicalBERT). The BioBERT model showed great generalizability on the heldout test data with a macro-average sensitivity of 77.29%, specificity of 92.89%, and area under the curve of 0.93.

CONCLUSIONS: A deep transfer learning model can be developed to reliably assess the level of uncertainty communicated in a radiology report.

AUC
:   area under the receiver operating characteristic curve
BERT
:   bidirectional encoder representations from transformers
BioBERT
:   biomedical variant of BERT
ClinicalBERT
:   clinical variant of BERT
NLP
:   natural language processing
QC-RAD
:   qualifying certainty in radiology reports" />
<meta name="og-title" property="og:title" content="Qualifying Certainty in Radiology Reports through Deep Learning–Based Natural Language Processing" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/42/10/1755" />
<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: Communication gaps exist between radiologists and referring physicians in conveying diagnostic certainty. We aimed to explore deep learning–based bidirectional contextual language models for automatically assessing diagnostic certainty expressed in the radiology reports to facilitate the precision of communication.

MATERIALS AND METHODS: We randomly sampled 594 head MR imaging reports from an academic medical center. We asked 3 board-certified radiologists to read sentences from the Impression section and assign each sentence 1 of the 4 certainty categories: “Non-Definitive,” “Definitive-Mild,” “Definitive-Strong,” “Other.” Using the annotated 2352 sentences, we developed and validated a natural language-processing system based on the start-of-the-art bidirectional encoder representations from transformers (BERT), which can capture contextual uncertainty semantics beyond the lexicon level. Finally, we evaluated 3 BERT variant models and reported standard metrics including sensitivity, specificity, and area under the curve.

RESULTS: A κ score of 0.74 was achieved for interannotator agreement on uncertainty interpretations among 3 radiologists. For the 3 BERT variant models, the biomedical variant (BioBERT) achieved the best macro-average area under the curve of 0.931 (compared with 0.928 for the BERT-base and 0.925 for the clinical variant [ClinicalBERT]) on the validation data. All 3 models yielded high macro-average specificity (93.13%–93.65%), while the BERT-base obtained the highest macro-average sensitivity of 79.46% (compared with 79.08% for BioBERT and 78.52% for ClinicalBERT). The BioBERT model showed great generalizability on the heldout test data with a macro-average sensitivity of 77.29%, specificity of 92.89%, and area under the curve of 0.93.

CONCLUSIONS: A deep transfer learning model can be developed to reliably assess the level of uncertainty communicated in a radiology report.

AUC
:   area under the receiver operating characteristic curve
BERT
:   bidirectional encoder representations from transformers
BioBERT
:   biomedical variant of BERT
ClinicalBERT
:   clinical variant of BERT
NLP
:   natural language processing
QC-RAD
:   qualifying certainty in radiology reports" />
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      <h1 class="highwire-cite-title" id="page-title">Qualifying Certainty in Radiology Reports through Deep Learning–Based Natural Language Processing</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">F. Liu</span>, <span class="highwire-citation-author" data-delta="1">P. Zhou</span>, <span class="highwire-citation-author" data-delta="2">S.J. Baccei</span>, <span class="highwire-citation-author" data-delta="3">M.J. Masciocchi</span>, <span class="highwire-citation-author" data-delta="4">N. Amornsiripanitch</span>, <span class="highwire-citation-author" data-delta="5">C.I. Kiefe</span> and <span class="highwire-citation-author" data-delta="6">M.P. Rosen</span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">October 2021,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">42 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1755-1761; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A7241 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-93292--6622907872" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">F. Liu </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 Population and Quantitative Health Sciences (F.L., C.I.K.), University of Massachusetts Medical School, Worcester, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (F.L., P.Z., S.J.B., M.J.M., N.A., M.P.R.), University of Massachusetts Medical School, Worcester, 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=F.%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%20F&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%3AF.%2BLiu%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/0000-0003-0881-6365" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for F. Liu</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">P. Zhou </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (F.L., P.Z., S.J.B., M.J.M., N.A., M.P.R.), University of Massachusetts Medical School, Worcester, 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=P.%2BZhou%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=Zhou%20P&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%3AP.%2BZhou%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/0000-0003-3736-1611" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for P. Zhou</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">S.J. Baccei </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (F.L., P.Z., S.J.B., M.J.M., N.A., M.P.R.), University of Massachusetts Medical School, Worcester, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Radiology (S.J.B., M.J.M., N.A., M.P.R.), UMass Memorial Medical Center, Worcester, 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=S.J.%2BBaccei%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=Baccei%20S&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AS.J.%2BBaccei%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/0000-0002-1859-0833" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for S.J. Baccei</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">M.J. Masciocchi </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (F.L., P.Z., S.J.B., M.J.M., N.A., M.P.R.), University of Massachusetts Medical School, Worcester, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Radiology (S.J.B., M.J.M., N.A., M.P.R.), UMass Memorial Medical Center, Worcester, 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=M.J.%2BMasciocchi%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=Masciocchi%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.J.%2BMasciocchi%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/0000-0003-2960-357X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M.J. Masciocchi</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">N. Amornsiripanitch </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (F.L., P.Z., S.J.B., M.J.M., N.A., M.P.R.), University of Massachusetts Medical School, Worcester, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Radiology (S.J.B., M.J.M., N.A., M.P.R.), UMass Memorial Medical Center, Worcester, 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=N.%2BAmornsiripanitch%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=Amornsiripanitch%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%3AN.%2BAmornsiripanitch%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/0000-0003-3851-8537" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for N. 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Kiefe </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 Population and Quantitative Health Sciences (F.L., C.I.K.), University of Massachusetts Medical School, Worcester, 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=C.I.%2BKiefe%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=Kiefe%20C&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%3AC.I.%2BKiefe%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/0000-0001-8719-6963" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for C.I. 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Rosen </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (F.L., P.Z., S.J.B., M.J.M., N.A., M.P.R.), University of Massachusetts Medical School, Worcester, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Radiology (S.J.B., M.J.M., N.A., M.P.R.), UMass Memorial Medical Center, Worcester, 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=M.P.%2BRosen%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=Rosen%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.P.%2BRosen%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/0000-0002-5266-0364" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M.P. 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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 42</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 10 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Oct 2021 </div></div>
      
      
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