Coronary CT angiography–derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia
Objectives We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)–derived plaque markers combined with deep machine learning–based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard. Methods Eighty-four pa...
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| Veröffentlicht in: | European radiology 2019-05, Vol.29 (5), p.2378-2387 |
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| creator | von Knebel Doeberitz, Philipp L. De Cecco, Carlo N. Schoepf, U. Joseph Duguay, Taylor M. Albrecht, Moritz H. van Assen, Marly Bauer, Maximilian J. Savage, Rock H. Pannell, J. Trent De Santis, Domenico Johnson, Addison A. Varga-Szemes, Akos Bayer, Richard R. Schönberg, Stefan O. Nance, John W. Tesche, Christian |
| description | Objectives
We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)–derived plaque markers combined with deep machine learning–based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard.
Methods
Eighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning–based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard.
Results
One hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15,
p
= 0.037), non-calcified plaque volume (OR 1.02,
p
= 0.007), napkin-ring sign (OR 5.97,
p
= 0.014), and CT-FFR (OR 0.81,
p
|
| doi_str_mv | 10.1007/s00330-018-5834-z |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2155929135</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2150941330</sourcerecordid><originalsourceid>FETCH-LOGICAL-c415t-eaee8a8f95baffbade134c20c5c0b1bc4d523e60746f8c2652386031b162ced3</originalsourceid><addsrcrecordid>eNp1kc1u3CAUhVGVqpmkfYBuKqRsuqG9GPDYy2qUn0qRupk9wvgyQ-QxDtiJklXeIW-QR-uTBGfSH1XqCrj3uwcOh5CPHL5wgOXXBCAEMOAVU5WQ7P4NWXApCsahkgdkAbWo2LKu5SE5SukKAGoul-_IoQBVCKnKBXlahRh6E-_oak1Nv_FhE82wvfv58Nhi9DfY0qEz1xPS68n0o3femtGHnt76cUtNfKl401Hfj9h1foO9xVnLRWNnMLdcF25pxITxBqkLkY5bpL7Fv-WCox2mvGNpQDvXqU92iztv3pO3znQJP7yux2R9drpeXbDLH-ffV98umZVcjQwNYmUqV6vGONeYFrmQtgCrLDS8sbLNnrGEpSxdZYsyn6oSBG94WVhsxTH5vJcdYsh-06h3-QXZk-kxTEkXXKm6qLlQGT35B70KU8xWXyioJc-xZIrvKRtDShGdHqLf5a_WHPScn97np3N-es5P3-eZT6_KU7PD9vfEr8AyUOyBlFv9BuOfq_-v-gzNPquJ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2150941330</pqid></control><display><type>article</type><title>Coronary CT angiography–derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>von Knebel Doeberitz, Philipp L. ; De Cecco, Carlo N. ; Schoepf, U. Joseph ; Duguay, Taylor M. ; Albrecht, Moritz H. ; van Assen, Marly ; Bauer, Maximilian J. ; Savage, Rock H. ; Pannell, J. Trent ; De Santis, Domenico ; Johnson, Addison A. ; Varga-Szemes, Akos ; Bayer, Richard R. ; Schönberg, Stefan O. ; Nance, John W. ; Tesche, Christian</creator><creatorcontrib>von Knebel Doeberitz, Philipp L. ; De Cecco, Carlo N. ; Schoepf, U. Joseph ; Duguay, Taylor M. ; Albrecht, Moritz H. ; van Assen, Marly ; Bauer, Maximilian J. ; Savage, Rock H. ; Pannell, J. Trent ; De Santis, Domenico ; Johnson, Addison A. ; Varga-Szemes, Akos ; Bayer, Richard R. ; Schönberg, Stefan O. ; Nance, John W. ; Tesche, Christian</creatorcontrib><description>Objectives
We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)–derived plaque markers combined with deep machine learning–based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard.
Methods
Eighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning–based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard.
Results
One hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15,
p
= 0.037), non-calcified plaque volume (OR 1.02,
p
= 0.007), napkin-ring sign (OR 5.97,
p
= 0.014), and CT-FFR (OR 0.81,
p
< 0.0001). A receiver operating characteristics analysis showed the benefit of identifying plaque markers over cCTA stenosis grading alone, with AUCs increasing from 0.61 with ≥ 50% stenosis to 0.83 with addition of plaque markers to detect lesion-specific ischemia. Further incremental benefit was realized with the addition of CT-FFR (AUC 0.93).
Conclusion
Coronary CTA–derived plaque markers portend predictive value to identify lesion-specific ischemia when compared to cCTA stenosis grading alone. The addition of CT-FFR to plaque markers shows incremental discriminatory power.
Key Points
• Coronary CT angiography (cCTA)–derived quantitative plaque markers of atherosclerosis portend high discriminatory power to identify lesion-specific ischemia.
• Coronary CT angiography–derived fractional flow reserve (CT-FFR) shows superior diagnostic performance over cCTA alone in detecting lesion-specific ischemia.
• A combination of plaque markers with CT-FFR provides incremental discriminatory value for detecting flow-limiting stenosis.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-018-5834-z</identifier><identifier>PMID: 30523456</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Angiography ; Arteriosclerosis ; Artificial intelligence ; Atherosclerosis ; Blood vessels ; Cardiac ; Computed tomography ; Computed Tomography Angiography - methods ; Coronary Angiography - methods ; Coronary Stenosis - diagnosis ; Coronary Stenosis - etiology ; Coronary Stenosis - physiopathology ; Diagnosis, Computer-Assisted - methods ; Diagnostic Radiology ; Diagnostic systems ; Evaluation ; Female ; Fractional Flow Reserve, Myocardial - physiology ; Grading ; Humans ; Identification methods ; Imaging ; Internal Medicine ; Interventional Radiology ; Ischemia ; Learning algorithms ; Lesions ; Machine Learning ; Male ; Markers ; Medical imaging ; Medicine ; Medicine & Public Health ; Middle Aged ; Multivariate analysis ; Neuroradiology ; Plaque, Atherosclerotic - complications ; Plaque, Atherosclerotic - diagnosis ; Plaque, Atherosclerotic - physiopathology ; Radiology ; Retrospective Studies ; Risk analysis ; ROC Curve ; Stenosis ; Ultrasound</subject><ispartof>European radiology, 2019-05, Vol.29 (5), p.2378-2387</ispartof><rights>European Society of Radiology 2018</rights><rights>European Radiology is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-eaee8a8f95baffbade134c20c5c0b1bc4d523e60746f8c2652386031b162ced3</citedby><cites>FETCH-LOGICAL-c415t-eaee8a8f95baffbade134c20c5c0b1bc4d523e60746f8c2652386031b162ced3</cites><orcidid>0000-0002-6164-5641</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-018-5834-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-018-5834-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30523456$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>von Knebel Doeberitz, Philipp L.</creatorcontrib><creatorcontrib>De Cecco, Carlo N.</creatorcontrib><creatorcontrib>Schoepf, U. Joseph</creatorcontrib><creatorcontrib>Duguay, Taylor M.</creatorcontrib><creatorcontrib>Albrecht, Moritz H.</creatorcontrib><creatorcontrib>van Assen, Marly</creatorcontrib><creatorcontrib>Bauer, Maximilian J.</creatorcontrib><creatorcontrib>Savage, Rock H.</creatorcontrib><creatorcontrib>Pannell, J. Trent</creatorcontrib><creatorcontrib>De Santis, Domenico</creatorcontrib><creatorcontrib>Johnson, Addison A.</creatorcontrib><creatorcontrib>Varga-Szemes, Akos</creatorcontrib><creatorcontrib>Bayer, Richard R.</creatorcontrib><creatorcontrib>Schönberg, Stefan O.</creatorcontrib><creatorcontrib>Nance, John W.</creatorcontrib><creatorcontrib>Tesche, Christian</creatorcontrib><title>Coronary CT angiography–derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)–derived plaque markers combined with deep machine learning–based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard.
Methods
Eighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning–based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard.
Results
One hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15,
p
= 0.037), non-calcified plaque volume (OR 1.02,
p
= 0.007), napkin-ring sign (OR 5.97,
p
= 0.014), and CT-FFR (OR 0.81,
p
< 0.0001). A receiver operating characteristics analysis showed the benefit of identifying plaque markers over cCTA stenosis grading alone, with AUCs increasing from 0.61 with ≥ 50% stenosis to 0.83 with addition of plaque markers to detect lesion-specific ischemia. Further incremental benefit was realized with the addition of CT-FFR (AUC 0.93).
Conclusion
Coronary CTA–derived plaque markers portend predictive value to identify lesion-specific ischemia when compared to cCTA stenosis grading alone. The addition of CT-FFR to plaque markers shows incremental discriminatory power.
Key Points
• Coronary CT angiography (cCTA)–derived quantitative plaque markers of atherosclerosis portend high discriminatory power to identify lesion-specific ischemia.
• Coronary CT angiography–derived fractional flow reserve (CT-FFR) shows superior diagnostic performance over cCTA alone in detecting lesion-specific ischemia.
• A combination of plaque markers with CT-FFR provides incremental discriminatory value for detecting flow-limiting stenosis.</description><subject>Angiography</subject><subject>Arteriosclerosis</subject><subject>Artificial intelligence</subject><subject>Atherosclerosis</subject><subject>Blood vessels</subject><subject>Cardiac</subject><subject>Computed tomography</subject><subject>Computed Tomography Angiography - methods</subject><subject>Coronary Angiography - methods</subject><subject>Coronary Stenosis - diagnosis</subject><subject>Coronary Stenosis - etiology</subject><subject>Coronary Stenosis - physiopathology</subject><subject>Diagnosis, Computer-Assisted - methods</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Evaluation</subject><subject>Female</subject><subject>Fractional Flow Reserve, Myocardial - physiology</subject><subject>Grading</subject><subject>Humans</subject><subject>Identification methods</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Ischemia</subject><subject>Learning algorithms</subject><subject>Lesions</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Markers</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Multivariate analysis</subject><subject>Neuroradiology</subject><subject>Plaque, Atherosclerotic - complications</subject><subject>Plaque, Atherosclerotic - diagnosis</subject><subject>Plaque, Atherosclerotic - physiopathology</subject><subject>Radiology</subject><subject>Retrospective Studies</subject><subject>Risk analysis</subject><subject>ROC Curve</subject><subject>Stenosis</subject><subject>Ultrasound</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp1kc1u3CAUhVGVqpmkfYBuKqRsuqG9GPDYy2qUn0qRupk9wvgyQ-QxDtiJklXeIW-QR-uTBGfSH1XqCrj3uwcOh5CPHL5wgOXXBCAEMOAVU5WQ7P4NWXApCsahkgdkAbWo2LKu5SE5SukKAGoul-_IoQBVCKnKBXlahRh6E-_oak1Nv_FhE82wvfv58Nhi9DfY0qEz1xPS68n0o3femtGHnt76cUtNfKl401Hfj9h1foO9xVnLRWNnMLdcF25pxITxBqkLkY5bpL7Fv-WCox2mvGNpQDvXqU92iztv3pO3znQJP7yux2R9drpeXbDLH-ffV98umZVcjQwNYmUqV6vGONeYFrmQtgCrLDS8sbLNnrGEpSxdZYsyn6oSBG94WVhsxTH5vJcdYsh-06h3-QXZk-kxTEkXXKm6qLlQGT35B70KU8xWXyioJc-xZIrvKRtDShGdHqLf5a_WHPScn97np3N-es5P3-eZT6_KU7PD9vfEr8AyUOyBlFv9BuOfq_-v-gzNPquJ</recordid><startdate>20190501</startdate><enddate>20190501</enddate><creator>von Knebel Doeberitz, Philipp L.</creator><creator>De Cecco, Carlo N.</creator><creator>Schoepf, U. Joseph</creator><creator>Duguay, Taylor M.</creator><creator>Albrecht, Moritz H.</creator><creator>van Assen, Marly</creator><creator>Bauer, Maximilian J.</creator><creator>Savage, Rock H.</creator><creator>Pannell, J. Trent</creator><creator>De Santis, Domenico</creator><creator>Johnson, Addison A.</creator><creator>Varga-Szemes, Akos</creator><creator>Bayer, Richard R.</creator><creator>Schönberg, Stefan O.</creator><creator>Nance, John W.</creator><creator>Tesche, Christian</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6164-5641</orcidid></search><sort><creationdate>20190501</creationdate><title>Coronary CT angiography–derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia</title><author>von Knebel Doeberitz, Philipp L. ; De Cecco, Carlo N. ; Schoepf, U. Joseph ; Duguay, Taylor M. ; Albrecht, Moritz H. ; van Assen, Marly ; Bauer, Maximilian J. ; Savage, Rock H. ; Pannell, J. Trent ; De Santis, Domenico ; Johnson, Addison A. ; Varga-Szemes, Akos ; Bayer, Richard R. ; Schönberg, Stefan O. ; Nance, John W. ; Tesche, Christian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c415t-eaee8a8f95baffbade134c20c5c0b1bc4d523e60746f8c2652386031b162ced3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Angiography</topic><topic>Arteriosclerosis</topic><topic>Artificial intelligence</topic><topic>Atherosclerosis</topic><topic>Blood vessels</topic><topic>Cardiac</topic><topic>Computed tomography</topic><topic>Computed Tomography Angiography - methods</topic><topic>Coronary Angiography - methods</topic><topic>Coronary Stenosis - diagnosis</topic><topic>Coronary Stenosis - etiology</topic><topic>Coronary Stenosis - physiopathology</topic><topic>Diagnosis, Computer-Assisted - methods</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Evaluation</topic><topic>Female</topic><topic>Fractional Flow Reserve, Myocardial - physiology</topic><topic>Grading</topic><topic>Humans</topic><topic>Identification methods</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Ischemia</topic><topic>Learning algorithms</topic><topic>Lesions</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Markers</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Multivariate analysis</topic><topic>Neuroradiology</topic><topic>Plaque, Atherosclerotic - complications</topic><topic>Plaque, Atherosclerotic - diagnosis</topic><topic>Plaque, Atherosclerotic - physiopathology</topic><topic>Radiology</topic><topic>Retrospective Studies</topic><topic>Risk analysis</topic><topic>ROC Curve</topic><topic>Stenosis</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>von Knebel Doeberitz, Philipp L.</creatorcontrib><creatorcontrib>De Cecco, Carlo N.</creatorcontrib><creatorcontrib>Schoepf, U. Joseph</creatorcontrib><creatorcontrib>Duguay, Taylor M.</creatorcontrib><creatorcontrib>Albrecht, Moritz H.</creatorcontrib><creatorcontrib>van Assen, Marly</creatorcontrib><creatorcontrib>Bauer, Maximilian J.</creatorcontrib><creatorcontrib>Savage, Rock H.</creatorcontrib><creatorcontrib>Pannell, J. Trent</creatorcontrib><creatorcontrib>De Santis, Domenico</creatorcontrib><creatorcontrib>Johnson, Addison A.</creatorcontrib><creatorcontrib>Varga-Szemes, Akos</creatorcontrib><creatorcontrib>Bayer, Richard R.</creatorcontrib><creatorcontrib>Schönberg, Stefan O.</creatorcontrib><creatorcontrib>Nance, John W.</creatorcontrib><creatorcontrib>Tesche, Christian</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>Natural Science Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>von Knebel Doeberitz, Philipp L.</au><au>De Cecco, Carlo N.</au><au>Schoepf, U. Joseph</au><au>Duguay, Taylor M.</au><au>Albrecht, Moritz H.</au><au>van Assen, Marly</au><au>Bauer, Maximilian J.</au><au>Savage, Rock H.</au><au>Pannell, J. Trent</au><au>De Santis, Domenico</au><au>Johnson, Addison A.</au><au>Varga-Szemes, Akos</au><au>Bayer, Richard R.</au><au>Schönberg, Stefan O.</au><au>Nance, John W.</au><au>Tesche, Christian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Coronary CT angiography–derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2019-05-01</date><risdate>2019</risdate><volume>29</volume><issue>5</issue><spage>2378</spage><epage>2387</epage><pages>2378-2387</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)–derived plaque markers combined with deep machine learning–based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard.
Methods
Eighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning–based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard.
Results
One hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15,
p
= 0.037), non-calcified plaque volume (OR 1.02,
p
= 0.007), napkin-ring sign (OR 5.97,
p
= 0.014), and CT-FFR (OR 0.81,
p
< 0.0001). A receiver operating characteristics analysis showed the benefit of identifying plaque markers over cCTA stenosis grading alone, with AUCs increasing from 0.61 with ≥ 50% stenosis to 0.83 with addition of plaque markers to detect lesion-specific ischemia. Further incremental benefit was realized with the addition of CT-FFR (AUC 0.93).
Conclusion
Coronary CTA–derived plaque markers portend predictive value to identify lesion-specific ischemia when compared to cCTA stenosis grading alone. The addition of CT-FFR to plaque markers shows incremental discriminatory power.
Key Points
• Coronary CT angiography (cCTA)–derived quantitative plaque markers of atherosclerosis portend high discriminatory power to identify lesion-specific ischemia.
• Coronary CT angiography–derived fractional flow reserve (CT-FFR) shows superior diagnostic performance over cCTA alone in detecting lesion-specific ischemia.
• A combination of plaque markers with CT-FFR provides incremental discriminatory value for detecting flow-limiting stenosis.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>30523456</pmid><doi>10.1007/s00330-018-5834-z</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-6164-5641</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0938-7994 |
| ispartof | European radiology, 2019-05, Vol.29 (5), p.2378-2387 |
| issn | 0938-7994 1432-1084 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2155929135 |
| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Angiography Arteriosclerosis Artificial intelligence Atherosclerosis Blood vessels Cardiac Computed tomography Computed Tomography Angiography - methods Coronary Angiography - methods Coronary Stenosis - diagnosis Coronary Stenosis - etiology Coronary Stenosis - physiopathology Diagnosis, Computer-Assisted - methods Diagnostic Radiology Diagnostic systems Evaluation Female Fractional Flow Reserve, Myocardial - physiology Grading Humans Identification methods Imaging Internal Medicine Interventional Radiology Ischemia Learning algorithms Lesions Machine Learning Male Markers Medical imaging Medicine Medicine & Public Health Middle Aged Multivariate analysis Neuroradiology Plaque, Atherosclerotic - complications Plaque, Atherosclerotic - diagnosis Plaque, Atherosclerotic - physiopathology Radiology Retrospective Studies Risk analysis ROC Curve Stenosis Ultrasound |
| title | Coronary CT angiography–derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-07T21%3A25%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Coronary%20CT%20angiography%E2%80%93derived%20plaque%20quantification%20with%20artificial%20intelligence%20CT%20fractional%20flow%20reserve%20for%20the%20identification%20of%20lesion-specific%20ischemia&rft.jtitle=European%20radiology&rft.au=von%20Knebel%20Doeberitz,%20Philipp%20L.&rft.date=2019-05-01&rft.volume=29&rft.issue=5&rft.spage=2378&rft.epage=2387&rft.pages=2378-2387&rft.issn=0938-7994&rft.eissn=1432-1084&rft_id=info:doi/10.1007/s00330-018-5834-z&rft_dat=%3Cproquest_cross%3E2150941330%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2150941330&rft_id=info:pmid/30523456&rfr_iscdi=true |