Coronary CT angiography–derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-05

AUTHORS

Philipp L. von Knebel Doeberitz, Carlo N. De Cecco, U. Joseph Schoepf, Taylor M. Duguay, Moritz H. Albrecht, Marly van Assen, Maximilian J. Bauer, Rock H. Savage, J. Trent Pannell, Domenico De Santis, Addison A. Johnson, Akos Varga-Szemes, Richard R. Bayer, Stefan O. Schönberg, John W. Nance, Christian Tesche

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. More... »

PAGES

2378-2387

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00330-018-5834-z

DOI

http://dx.doi.org/10.1007/s00330-018-5834-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110421655

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30523456


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Cardiorespiratory Medicine and Haematology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University Medical Centre Mannheim", 
          "id": "https://www.grid.ac/institutes/grid.411778.c", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA", 
            "Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "von Knebel Doeberitz", 
        "givenName": "Philipp L.", 
        "id": "sg:person.012647526207.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012647526207.77"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical University of South Carolina", 
          "id": "https://www.grid.ac/institutes/grid.259828.c", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "De Cecco", 
        "givenName": "Carlo N.", 
        "id": "sg:person.01205213567.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01205213567.30"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical University of South Carolina", 
          "id": "https://www.grid.ac/institutes/grid.259828.c", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA", 
            "Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA", 
            "Heart & Vascular Center, Ashley River Tower, Medical University of South Carolina, 25 Courtenay Drive, 29425-2260, Charleston, SC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Schoepf", 
        "givenName": "U. Joseph", 
        "id": "sg:person.0601357112.86", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601357112.86"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical University of South Carolina", 
          "id": "https://www.grid.ac/institutes/grid.259828.c", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Duguay", 
        "givenName": "Taylor M.", 
        "id": "sg:person.014332301401.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014332301401.27"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical University of South Carolina", 
          "id": "https://www.grid.ac/institutes/grid.259828.c", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA", 
            "Center for Medical Imaging North East Netherlands, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Albrecht", 
        "givenName": "Moritz H.", 
        "id": "sg:person.01051044624.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01051044624.85"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University Hospital Frankfurt", 
          "id": "https://www.grid.ac/institutes/grid.411088.4", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA", 
            "Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "van Assen", 
        "givenName": "Marly", 
        "id": "sg:person.013152265557.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013152265557.77"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical University of South Carolina", 
          "id": "https://www.grid.ac/institutes/grid.259828.c", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bauer", 
        "givenName": "Maximilian J.", 
        "id": "sg:person.010547760310.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010547760310.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical University of South Carolina", 
          "id": "https://www.grid.ac/institutes/grid.259828.c", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Savage", 
        "givenName": "Rock H.", 
        "id": "sg:person.013300603300.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013300603300.85"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical University of South Carolina", 
          "id": "https://www.grid.ac/institutes/grid.259828.c", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pannell", 
        "givenName": "J. Trent", 
        "id": "sg:person.015727657537.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015727657537.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Sapienza University of Rome", 
          "id": "https://www.grid.ac/institutes/grid.7841.a", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA", 
            "Department of Radiological Sciences, Oncology and Pathology, University of Rome \u201cSapienza\u201d, Rome, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "De Santis", 
        "givenName": "Domenico", 
        "id": "sg:person.07442564431.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07442564431.29"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical University of South Carolina", 
          "id": "https://www.grid.ac/institutes/grid.259828.c", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Johnson", 
        "givenName": "Addison A.", 
        "id": "sg:person.016402221010.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016402221010.51"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical University of South Carolina", 
          "id": "https://www.grid.ac/institutes/grid.259828.c", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Varga-Szemes", 
        "givenName": "Akos", 
        "id": "sg:person.01163476023.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01163476023.48"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical University of South Carolina", 
          "id": "https://www.grid.ac/institutes/grid.259828.c", 
          "name": [
            "Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bayer", 
        "givenName": "Richard R.", 
        "id": "sg:person.01233115614.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01233115614.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University Medical Centre Mannheim", 
          "id": "https://www.grid.ac/institutes/grid.411778.c", 
          "name": [
            "Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sch\u00f6nberg", 
        "givenName": "Stefan O.", 
        "id": "sg:person.011300565151.36", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011300565151.36"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical University of South Carolina", 
          "id": "https://www.grid.ac/institutes/grid.259828.c", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nance", 
        "givenName": "John W.", 
        "id": "sg:person.01225105741.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01225105741.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical University of South Carolina", 
          "id": "https://www.grid.ac/institutes/grid.259828.c", 
          "name": [
            "Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA", 
            "Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tesche", 
        "givenName": "Christian", 
        "id": "sg:person.01225030177.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01225030177.19"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1093/eurheartj/eht296", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003024186"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/001316447503500301", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004954099"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/001316447503500301", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004954099"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcmg.2012.03.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006283869"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2016.01.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006978803"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/clc.22076", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008780439"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcmg.2014.11.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011001441"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2016.03.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018537125"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rti.0000000000000236", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018811596"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rti.0000000000000236", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018811596"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2003.09.053", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021555807"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2003.09.053", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021555807"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2003.09.053", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021555807"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2016.04.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025328519"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcmg.2011.03.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027708901"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2015141648", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029195366"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2011.06.066", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032139249"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-010-9613-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033586565", 
          "https://doi.org/10.1007/s10554-010-9613-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-010-9613-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033586565", 
          "https://doi.org/10.1007/s10554-010-9613-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/eurheartj/ehv690", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034822555"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.99.17.2345", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037897669"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.amjcard.2016.11.030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038498954"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2007.03.044", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042918285"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circulationaha.107.699579", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043867491"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circinterventions.113.000978", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044501711"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circinterventions.113.000978", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044501711"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejrad.2015.04.024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044795744"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa0807611", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044990023"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2013.11.043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049479733"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.113.000297", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051102364"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.113.000297", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051102364"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.atherosclerosis.2010.02.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052153593"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2016.07.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052348244"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2014.07.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052752907"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/japplphysiol.00752.2015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063198930"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.08.1277", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069299621"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2531595", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069977037"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1077191575", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.14140992", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1078989089"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rti.0000000000000289", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091235515"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rti.0000000000000289", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091235515"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2017162641", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091858533"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.21037/cdt.2016.11.06", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092306275"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s13550-017-0342-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092988070", 
          "https://doi.org/10.1186/s13550-017-0342-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-017-5223-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100480206", 
          "https://doi.org/10.1007/s00330-017-5223-z"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-05", 
    "datePublishedReg": "2019-05-01", 
    "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.\nMETHODS: Eighty-four patients (61\u2009\u00b1\u200910\u00a0years, 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.\nRESULTS: 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\u2009=\u20090.037), non-calcified plaque volume (OR 1.02, p\u2009=\u20090.007), napkin-ring sign (OR 5.97, p\u2009=\u20090.014), and CT-FFR (OR 0.81, p\u2009<\u20090.0001). A receiver operating characteristics analysis showed the benefit of identifying plaque markers over cCTA stenosis grading alone, with AUCs increasing from 0.61 with \u2265\u200950% 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).\nCONCLUSION: 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.\nKEY POINTS: \u2022 Coronary CT angiography (cCTA)-derived quantitative plaque markers of atherosclerosis portend high discriminatory power to identify lesion-specific ischemia. \u2022 Coronary CT angiography-derived fractional flow reserve (CT-FFR) shows superior diagnostic performance over cCTA alone in detecting lesion-specific ischemia. \u2022 A combination of plaque markers with CT-FFR provides incremental discriminatory value for detecting flow-limiting stenosis.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00330-018-5834-z", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1289120", 
        "issn": [
          "0938-7994", 
          "1432-1084"
        ], 
        "name": "European Radiology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "29"
      }
    ], 
    "name": "Coronary CT angiography\u2013derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia", 
    "pagination": "2378-2387", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "d27b4b3d2aad135308bad6f659c441c86c60551cf583bdd9a821ab0a77368a78"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30523456"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9114774"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00330-018-5834-z"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1110421655"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00330-018-5834-z", 
      "https://app.dimensions.ai/details/publication/pub.1110421655"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T14:20", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000372_0000000372/records_117123_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00330-018-5834-z"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00330-018-5834-z'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00330-018-5834-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00330-018-5834-z'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00330-018-5834-z'


 

This table displays all metadata directly associated to this object as RDF triples.

303 TRIPLES      21 PREDICATES      66 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00330-018-5834-z schema:about anzsrc-for:11
2 anzsrc-for:1102
3 schema:author Neae32128b8bf4820af197e49c2f43ee3
4 schema:citation sg:pub.10.1007/s00330-017-5223-z
5 sg:pub.10.1007/s10554-010-9613-4
6 sg:pub.10.1186/s13550-017-0342-8
7 https://app.dimensions.ai/details/publication/pub.1077191575
8 https://doi.org/10.1002/clc.22076
9 https://doi.org/10.1016/j.amjcard.2016.11.030
10 https://doi.org/10.1016/j.atherosclerosis.2010.02.001
11 https://doi.org/10.1016/j.ejrad.2015.04.024
12 https://doi.org/10.1016/j.jacc.2003.09.053
13 https://doi.org/10.1016/j.jacc.2007.03.044
14 https://doi.org/10.1016/j.jacc.2011.06.066
15 https://doi.org/10.1016/j.jacc.2013.11.043
16 https://doi.org/10.1016/j.jcct.2014.07.007
17 https://doi.org/10.1016/j.jcct.2016.01.007
18 https://doi.org/10.1016/j.jcct.2016.03.002
19 https://doi.org/10.1016/j.jcct.2016.04.005
20 https://doi.org/10.1016/j.jcct.2016.07.005
21 https://doi.org/10.1016/j.jcmg.2011.03.006
22 https://doi.org/10.1016/j.jcmg.2012.03.019
23 https://doi.org/10.1016/j.jcmg.2014.11.002
24 https://doi.org/10.1056/nejmoa0807611
25 https://doi.org/10.1093/eurheartj/eht296
26 https://doi.org/10.1093/eurheartj/ehv690
27 https://doi.org/10.1097/rti.0000000000000236
28 https://doi.org/10.1097/rti.0000000000000289
29 https://doi.org/10.1148/radiol.14140992
30 https://doi.org/10.1148/radiol.2015141648
31 https://doi.org/10.1148/radiol.2017162641
32 https://doi.org/10.1152/japplphysiol.00752.2015
33 https://doi.org/10.1161/01.cir.99.17.2345
34 https://doi.org/10.1161/circimaging.113.000297
35 https://doi.org/10.1161/circinterventions.113.000978
36 https://doi.org/10.1161/circulationaha.107.699579
37 https://doi.org/10.1177/001316447503500301
38 https://doi.org/10.21037/cdt.2016.11.06
39 https://doi.org/10.2214/ajr.08.1277
40 https://doi.org/10.2307/2531595
41 schema:datePublished 2019-05
42 schema:datePublishedReg 2019-05-01
43 schema: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.
44 schema:genre research_article
45 schema:inLanguage en
46 schema:isAccessibleForFree false
47 schema:isPartOf N4e46645212984b4f98e0aa2063c743cd
48 N90288fd7366e48f180a15a124f6ea171
49 sg:journal.1289120
50 schema:name Coronary CT angiography–derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia
51 schema:pagination 2378-2387
52 schema:productId N2529216f109e452eaa3921f3376256c5
53 N6bd30d37ab3a4077995729fa47e49e9e
54 N9c5bc9e5a81d4dd7b31b75eb443c24ad
55 Nb4526b4c2feb4f1281777d9fd2393e5e
56 Nd9d1e353e7bc4722b0d4a07ff79ac8bd
57 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110421655
58 https://doi.org/10.1007/s00330-018-5834-z
59 schema:sdDatePublished 2019-04-11T14:20
60 schema:sdLicense https://scigraph.springernature.com/explorer/license/
61 schema:sdPublisher N1dc23f0edd0f40d3bb4663e7d3476618
62 schema:url https://link.springer.com/10.1007%2Fs00330-018-5834-z
63 sgo:license sg:explorer/license/
64 sgo:sdDataset articles
65 rdf:type schema:ScholarlyArticle
66 N0a79f875c4a44e61bea4c6fee39cda55 rdf:first sg:person.07442564431.29
67 rdf:rest Needb765167dc4f20a1e7bc8b272ada8c
68 N1dc23f0edd0f40d3bb4663e7d3476618 schema:name Springer Nature - SN SciGraph project
69 rdf:type schema:Organization
70 N214b5021e0604e9cb2530460f06ad06f rdf:first sg:person.01225030177.19
71 rdf:rest rdf:nil
72 N2529216f109e452eaa3921f3376256c5 schema:name nlm_unique_id
73 schema:value 9114774
74 rdf:type schema:PropertyValue
75 N3717f6bfc3704fc596a58f0a8393093b rdf:first sg:person.0601357112.86
76 rdf:rest N9d8dca1640f145bdaf9b958e630b7ec1
77 N396a63e8baf94c02b9ac3508682fdcb1 rdf:first sg:person.01051044624.85
78 rdf:rest N9114f023f52e410db6590e782e881f9e
79 N4e46645212984b4f98e0aa2063c743cd schema:issueNumber 5
80 rdf:type schema:PublicationIssue
81 N5425a277d5074b39b4b66f889ddbf26d rdf:first sg:person.011300565151.36
82 rdf:rest Na55c69be411a47dc907d68504a8d38d9
83 N6bd30d37ab3a4077995729fa47e49e9e schema:name dimensions_id
84 schema:value pub.1110421655
85 rdf:type schema:PropertyValue
86 N6f03bab1bcfd4105a320558bdae485aa rdf:first sg:person.015727657537.50
87 rdf:rest N0a79f875c4a44e61bea4c6fee39cda55
88 N70938afcc93a4a34876956e4a259d829 rdf:first sg:person.010547760310.49
89 rdf:rest Nb7f470d0e49948b3bfe7b3f84f9e22f7
90 N784be2277d19424da23151cb33aae5a7 rdf:first sg:person.01233115614.53
91 rdf:rest N5425a277d5074b39b4b66f889ddbf26d
92 N81db0311ff9e4bdfb76db308aa14319b rdf:first sg:person.01163476023.48
93 rdf:rest N784be2277d19424da23151cb33aae5a7
94 N90288fd7366e48f180a15a124f6ea171 schema:volumeNumber 29
95 rdf:type schema:PublicationVolume
96 N9114f023f52e410db6590e782e881f9e rdf:first sg:person.013152265557.77
97 rdf:rest N70938afcc93a4a34876956e4a259d829
98 N9c5bc9e5a81d4dd7b31b75eb443c24ad schema:name pubmed_id
99 schema:value 30523456
100 rdf:type schema:PropertyValue
101 N9d8dca1640f145bdaf9b958e630b7ec1 rdf:first sg:person.014332301401.27
102 rdf:rest N396a63e8baf94c02b9ac3508682fdcb1
103 Na55c69be411a47dc907d68504a8d38d9 rdf:first sg:person.01225105741.50
104 rdf:rest N214b5021e0604e9cb2530460f06ad06f
105 Nb4526b4c2feb4f1281777d9fd2393e5e schema:name doi
106 schema:value 10.1007/s00330-018-5834-z
107 rdf:type schema:PropertyValue
108 Nb7f470d0e49948b3bfe7b3f84f9e22f7 rdf:first sg:person.013300603300.85
109 rdf:rest N6f03bab1bcfd4105a320558bdae485aa
110 Nd9d1e353e7bc4722b0d4a07ff79ac8bd schema:name readcube_id
111 schema:value d27b4b3d2aad135308bad6f659c441c86c60551cf583bdd9a821ab0a77368a78
112 rdf:type schema:PropertyValue
113 Ne53fb44ba0114c508c4c5d13ce6751a1 rdf:first sg:person.01205213567.30
114 rdf:rest N3717f6bfc3704fc596a58f0a8393093b
115 Neae32128b8bf4820af197e49c2f43ee3 rdf:first sg:person.012647526207.77
116 rdf:rest Ne53fb44ba0114c508c4c5d13ce6751a1
117 Needb765167dc4f20a1e7bc8b272ada8c rdf:first sg:person.016402221010.51
118 rdf:rest N81db0311ff9e4bdfb76db308aa14319b
119 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
120 schema:name Medical and Health Sciences
121 rdf:type schema:DefinedTerm
122 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
123 schema:name Cardiorespiratory Medicine and Haematology
124 rdf:type schema:DefinedTerm
125 sg:journal.1289120 schema:issn 0938-7994
126 1432-1084
127 schema:name European Radiology
128 rdf:type schema:Periodical
129 sg:person.01051044624.85 schema:affiliation https://www.grid.ac/institutes/grid.259828.c
130 schema:familyName Albrecht
131 schema:givenName Moritz H.
132 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01051044624.85
133 rdf:type schema:Person
134 sg:person.010547760310.49 schema:affiliation https://www.grid.ac/institutes/grid.259828.c
135 schema:familyName Bauer
136 schema:givenName Maximilian J.
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010547760310.49
138 rdf:type schema:Person
139 sg:person.011300565151.36 schema:affiliation https://www.grid.ac/institutes/grid.411778.c
140 schema:familyName Schönberg
141 schema:givenName Stefan O.
142 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011300565151.36
143 rdf:type schema:Person
144 sg:person.01163476023.48 schema:affiliation https://www.grid.ac/institutes/grid.259828.c
145 schema:familyName Varga-Szemes
146 schema:givenName Akos
147 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01163476023.48
148 rdf:type schema:Person
149 sg:person.01205213567.30 schema:affiliation https://www.grid.ac/institutes/grid.259828.c
150 schema:familyName De Cecco
151 schema:givenName Carlo N.
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01205213567.30
153 rdf:type schema:Person
154 sg:person.01225030177.19 schema:affiliation https://www.grid.ac/institutes/grid.259828.c
155 schema:familyName Tesche
156 schema:givenName Christian
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01225030177.19
158 rdf:type schema:Person
159 sg:person.01225105741.50 schema:affiliation https://www.grid.ac/institutes/grid.259828.c
160 schema:familyName Nance
161 schema:givenName John W.
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01225105741.50
163 rdf:type schema:Person
164 sg:person.01233115614.53 schema:affiliation https://www.grid.ac/institutes/grid.259828.c
165 schema:familyName Bayer
166 schema:givenName Richard R.
167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01233115614.53
168 rdf:type schema:Person
169 sg:person.012647526207.77 schema:affiliation https://www.grid.ac/institutes/grid.411778.c
170 schema:familyName von Knebel Doeberitz
171 schema:givenName Philipp L.
172 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012647526207.77
173 rdf:type schema:Person
174 sg:person.013152265557.77 schema:affiliation https://www.grid.ac/institutes/grid.411088.4
175 schema:familyName van Assen
176 schema:givenName Marly
177 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013152265557.77
178 rdf:type schema:Person
179 sg:person.013300603300.85 schema:affiliation https://www.grid.ac/institutes/grid.259828.c
180 schema:familyName Savage
181 schema:givenName Rock H.
182 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013300603300.85
183 rdf:type schema:Person
184 sg:person.014332301401.27 schema:affiliation https://www.grid.ac/institutes/grid.259828.c
185 schema:familyName Duguay
186 schema:givenName Taylor M.
187 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014332301401.27
188 rdf:type schema:Person
189 sg:person.015727657537.50 schema:affiliation https://www.grid.ac/institutes/grid.259828.c
190 schema:familyName Pannell
191 schema:givenName J. Trent
192 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015727657537.50
193 rdf:type schema:Person
194 sg:person.016402221010.51 schema:affiliation https://www.grid.ac/institutes/grid.259828.c
195 schema:familyName Johnson
196 schema:givenName Addison A.
197 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016402221010.51
198 rdf:type schema:Person
199 sg:person.0601357112.86 schema:affiliation https://www.grid.ac/institutes/grid.259828.c
200 schema:familyName Schoepf
201 schema:givenName U. Joseph
202 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601357112.86
203 rdf:type schema:Person
204 sg:person.07442564431.29 schema:affiliation https://www.grid.ac/institutes/grid.7841.a
205 schema:familyName De Santis
206 schema:givenName Domenico
207 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07442564431.29
208 rdf:type schema:Person
209 sg:pub.10.1007/s00330-017-5223-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1100480206
210 https://doi.org/10.1007/s00330-017-5223-z
211 rdf:type schema:CreativeWork
212 sg:pub.10.1007/s10554-010-9613-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033586565
213 https://doi.org/10.1007/s10554-010-9613-4
214 rdf:type schema:CreativeWork
215 sg:pub.10.1186/s13550-017-0342-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092988070
216 https://doi.org/10.1186/s13550-017-0342-8
217 rdf:type schema:CreativeWork
218 https://app.dimensions.ai/details/publication/pub.1077191575 schema:CreativeWork
219 https://doi.org/10.1002/clc.22076 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008780439
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1016/j.amjcard.2016.11.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038498954
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1016/j.atherosclerosis.2010.02.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052153593
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1016/j.ejrad.2015.04.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044795744
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1016/j.jacc.2003.09.053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021555807
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1016/j.jacc.2007.03.044 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042918285
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1016/j.jacc.2011.06.066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032139249
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1016/j.jacc.2013.11.043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049479733
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1016/j.jcct.2014.07.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052752907
236 rdf:type schema:CreativeWork
237 https://doi.org/10.1016/j.jcct.2016.01.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006978803
238 rdf:type schema:CreativeWork
239 https://doi.org/10.1016/j.jcct.2016.03.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018537125
240 rdf:type schema:CreativeWork
241 https://doi.org/10.1016/j.jcct.2016.04.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025328519
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1016/j.jcct.2016.07.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052348244
244 rdf:type schema:CreativeWork
245 https://doi.org/10.1016/j.jcmg.2011.03.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027708901
246 rdf:type schema:CreativeWork
247 https://doi.org/10.1016/j.jcmg.2012.03.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006283869
248 rdf:type schema:CreativeWork
249 https://doi.org/10.1016/j.jcmg.2014.11.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011001441
250 rdf:type schema:CreativeWork
251 https://doi.org/10.1056/nejmoa0807611 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044990023
252 rdf:type schema:CreativeWork
253 https://doi.org/10.1093/eurheartj/eht296 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003024186
254 rdf:type schema:CreativeWork
255 https://doi.org/10.1093/eurheartj/ehv690 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034822555
256 rdf:type schema:CreativeWork
257 https://doi.org/10.1097/rti.0000000000000236 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018811596
258 rdf:type schema:CreativeWork
259 https://doi.org/10.1097/rti.0000000000000289 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091235515
260 rdf:type schema:CreativeWork
261 https://doi.org/10.1148/radiol.14140992 schema:sameAs https://app.dimensions.ai/details/publication/pub.1078989089
262 rdf:type schema:CreativeWork
263 https://doi.org/10.1148/radiol.2015141648 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029195366
264 rdf:type schema:CreativeWork
265 https://doi.org/10.1148/radiol.2017162641 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091858533
266 rdf:type schema:CreativeWork
267 https://doi.org/10.1152/japplphysiol.00752.2015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063198930
268 rdf:type schema:CreativeWork
269 https://doi.org/10.1161/01.cir.99.17.2345 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037897669
270 rdf:type schema:CreativeWork
271 https://doi.org/10.1161/circimaging.113.000297 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051102364
272 rdf:type schema:CreativeWork
273 https://doi.org/10.1161/circinterventions.113.000978 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044501711
274 rdf:type schema:CreativeWork
275 https://doi.org/10.1161/circulationaha.107.699579 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043867491
276 rdf:type schema:CreativeWork
277 https://doi.org/10.1177/001316447503500301 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004954099
278 rdf:type schema:CreativeWork
279 https://doi.org/10.21037/cdt.2016.11.06 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092306275
280 rdf:type schema:CreativeWork
281 https://doi.org/10.2214/ajr.08.1277 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069299621
282 rdf:type schema:CreativeWork
283 https://doi.org/10.2307/2531595 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069977037
284 rdf:type schema:CreativeWork
285 https://www.grid.ac/institutes/grid.259828.c schema:alternateName Medical University of South Carolina
286 schema:name Center for Medical Imaging North East Netherlands, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
287 Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany
288 Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA
289 Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
290 Heart & Vascular Center, Ashley River Tower, Medical University of South Carolina, 25 Courtenay Drive, 29425-2260, Charleston, SC, USA
291 rdf:type schema:Organization
292 https://www.grid.ac/institutes/grid.411088.4 schema:alternateName University Hospital Frankfurt
293 schema:name Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
294 Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
295 rdf:type schema:Organization
296 https://www.grid.ac/institutes/grid.411778.c schema:alternateName University Medical Centre Mannheim
297 schema:name Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
298 Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany
299 rdf:type schema:Organization
300 https://www.grid.ac/institutes/grid.7841.a schema:alternateName Sapienza University of Rome
301 schema:name Department of Radiological Sciences, Oncology and Pathology, University of Rome “Sapienza”, Rome, Italy
302 Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
303 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...