Diagnostic performance of perivascular fat attenuation index to predict hemodynamic significance of coronary stenosis: a preliminary coronary computed tomography angiography study
Objective This study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary lesions. Methods Patients with stable angina who underwent coronary computed tomography (CT) angiography and invasive fractional flow reserve (FFR) meas...
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Published in | European radiology Vol. 30; no. 2; pp. 673 - 681 |
---|---|
Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2020
Springer Nature B.V |
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Abstract | Objective
This study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary lesions.
Methods
Patients with stable angina who underwent coronary computed tomography (CT) angiography and invasive fractional flow reserve (FFR) measurement within 2 weeks were retrospectively included. Lesion-based perivascular FAI, high-risk plaque features, total plaque volume (TPV), machine learning–based FFR
CT
, and other parameters were recorded. Lesions with invasive FFR ≤ 0.8 were considered functionally significant.
Results
This study included 167 patients with 219 lesions. Diameter stenosis (DS), lesion length, TPV, and perivascular FAI were significantly larger or longer in the group of hemodynamically significant lesions (FFR ≤ 0.8). In addition, smaller FFR
CT
value was associated with functionally significant lesions (0.720 ± 0.11 vs 0.846 ± 0.10,
p
< 0.001). No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. According to multivariate analysis, DS, TPV, and perivascular FAI were significant predictors of lesion-specific ischemia. When integrating DS, TPV, and perivascular FAI, the area under the curve (AUC) of this combined method was 0.821, which was similar to that of FFR
CT
(AUC, 0.821 vs 0.850;
p
= 0.426). The diagnostic accuracy of FFR
CT
was higher than that of the combined approach, but the difference was statistically insignificant (79.0% vs 74.0%,
p
= 0.093).
Conclusions
Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. The combined use of FAI, TPV, and DS could predict ischemic coronary stenosis with high diagnostic accuracy.
Key Points
• Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions.
• Combined use of FAI, plaque volume, and DS provided diagnostic performance comparable to that of machine learning–based FFR
CT
for predicting ischemic coronary stenosis.
• No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. |
---|---|
AbstractList | ObjectiveThis study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary lesions.MethodsPatients with stable angina who underwent coronary computed tomography (CT) angiography and invasive fractional flow reserve (FFR) measurement within 2 weeks were retrospectively included. Lesion-based perivascular FAI, high-risk plaque features, total plaque volume (TPV), machine learning–based FFRCT, and other parameters were recorded. Lesions with invasive FFR ≤ 0.8 were considered functionally significant.ResultsThis study included 167 patients with 219 lesions. Diameter stenosis (DS), lesion length, TPV, and perivascular FAI were significantly larger or longer in the group of hemodynamically significant lesions (FFR ≤ 0.8). In addition, smaller FFRCT value was associated with functionally significant lesions (0.720 ± 0.11 vs 0.846 ± 0.10, p < 0.001). No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. According to multivariate analysis, DS, TPV, and perivascular FAI were significant predictors of lesion-specific ischemia. When integrating DS, TPV, and perivascular FAI, the area under the curve (AUC) of this combined method was 0.821, which was similar to that of FFRCT (AUC, 0.821 vs 0.850; p = 0.426). The diagnostic accuracy of FFRCT was higher than that of the combined approach, but the difference was statistically insignificant (79.0% vs 74.0%, p = 0.093).ConclusionsPerivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. The combined use of FAI, TPV, and DS could predict ischemic coronary stenosis with high diagnostic accuracy.Key Points• Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions.• Combined use of FAI, plaque volume, and DS provided diagnostic performance comparable to that of machine learning–based FFRCTfor predicting ischemic coronary stenosis.• No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. Objective This study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary lesions. Methods Patients with stable angina who underwent coronary computed tomography (CT) angiography and invasive fractional flow reserve (FFR) measurement within 2 weeks were retrospectively included. Lesion-based perivascular FAI, high-risk plaque features, total plaque volume (TPV), machine learning–based FFR CT , and other parameters were recorded. Lesions with invasive FFR ≤ 0.8 were considered functionally significant. Results This study included 167 patients with 219 lesions. Diameter stenosis (DS), lesion length, TPV, and perivascular FAI were significantly larger or longer in the group of hemodynamically significant lesions (FFR ≤ 0.8). In addition, smaller FFR CT value was associated with functionally significant lesions (0.720 ± 0.11 vs 0.846 ± 0.10, p < 0.001). No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. According to multivariate analysis, DS, TPV, and perivascular FAI were significant predictors of lesion-specific ischemia. When integrating DS, TPV, and perivascular FAI, the area under the curve (AUC) of this combined method was 0.821, which was similar to that of FFR CT (AUC, 0.821 vs 0.850; p = 0.426). The diagnostic accuracy of FFR CT was higher than that of the combined approach, but the difference was statistically insignificant (79.0% vs 74.0%, p = 0.093). Conclusions Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. The combined use of FAI, TPV, and DS could predict ischemic coronary stenosis with high diagnostic accuracy. Key Points • Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. • Combined use of FAI, plaque volume, and DS provided diagnostic performance comparable to that of machine learning–based FFR CT for predicting ischemic coronary stenosis. • No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. OBJECTIVEThis study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary lesions. METHODSPatients with stable angina who underwent coronary computed tomography (CT) angiography and invasive fractional flow reserve (FFR) measurement within 2 weeks were retrospectively included. Lesion-based perivascular FAI, high-risk plaque features, total plaque volume (TPV), machine learning-based FFRCT, and other parameters were recorded. Lesions with invasive FFR ≤ 0.8 were considered functionally significant. RESULTSThis study included 167 patients with 219 lesions. Diameter stenosis (DS), lesion length, TPV, and perivascular FAI were significantly larger or longer in the group of hemodynamically significant lesions (FFR ≤ 0.8). In addition, smaller FFRCT value was associated with functionally significant lesions (0.720 ± 0.11 vs 0.846 ± 0.10, p < 0.001). No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. According to multivariate analysis, DS, TPV, and perivascular FAI were significant predictors of lesion-specific ischemia. When integrating DS, TPV, and perivascular FAI, the area under the curve (AUC) of this combined method was 0.821, which was similar to that of FFRCT (AUC, 0.821 vs 0.850; p = 0.426). The diagnostic accuracy of FFRCT was higher than that of the combined approach, but the difference was statistically insignificant (79.0% vs 74.0%, p = 0.093). CONCLUSIONSPerivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. The combined use of FAI, TPV, and DS could predict ischemic coronary stenosis with high diagnostic accuracy. KEY POINTS• Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. • Combined use of FAI, plaque volume, and DS provided diagnostic performance comparable to that of machine learning-based FFR CTfor predicting ischemic coronary stenosis. • No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. This study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary lesions. Patients with stable angina who underwent coronary computed tomography (CT) angiography and invasive fractional flow reserve (FFR) measurement within 2 weeks were retrospectively included. Lesion-based perivascular FAI, high-risk plaque features, total plaque volume (TPV), machine learning-based FFR , and other parameters were recorded. Lesions with invasive FFR ≤ 0.8 were considered functionally significant. This study included 167 patients with 219 lesions. Diameter stenosis (DS), lesion length, TPV, and perivascular FAI were significantly larger or longer in the group of hemodynamically significant lesions (FFR ≤ 0.8). In addition, smaller FFR value was associated with functionally significant lesions (0.720 ± 0.11 vs 0.846 ± 0.10, p < 0.001). No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. According to multivariate analysis, DS, TPV, and perivascular FAI were significant predictors of lesion-specific ischemia. When integrating DS, TPV, and perivascular FAI, the area under the curve (AUC) of this combined method was 0.821, which was similar to that of FFR (AUC, 0.821 vs 0.850; p = 0.426). The diagnostic accuracy of FFR was higher than that of the combined approach, but the difference was statistically insignificant (79.0% vs 74.0%, p = 0.093). Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. The combined use of FAI, TPV, and DS could predict ischemic coronary stenosis with high diagnostic accuracy. • Perivascular FAI was significantly higher for flow-limiting lesions than for non-flow-limiting lesions. • Combined use of FAI, plaque volume, and DS provided diagnostic performance comparable to that of machine learning-based FFR for predicting ischemic coronary stenosis. • No significant difference was found between the hemodynamically significant and insignificant subgroups with respect to CT-derived high-risk plaque features. |
Author | Deng, Jianhong Yu, Mengmeng Shen, Chengxing Dai, Xu Zhang, Jiayin Lu, Zhigang |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31444596$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1016/j.jacc.2009.02.068 10.1007/s00330-018-5822-3 10.1161/CIRCIMAGING.117.007217 10.1016/j.ijcard.2018.01.075 10.1007/s00330-017-5223-z 10.1016/j.jcmg.2008.11.015 10.1001/jamacardio.2016.0263 10.1016/S0140-6736(18)31114-0 10.1148/radiol.2018171291 10.1016/j.jacc.2012.12.012 10.1056/NEJM199606273342604 10.1126/scitranslmed.aal2658 10.1007/s00330-018-5834-z 10.2307/2531595 10.1016/j.jacc.2008.07.031 10.1056/NEJMoa0806576 10.1152/japplphysiol.00752.2015 10.1016/j.jacc.2008.08.058 10.1016/j.jacc.2007.03.067 10.1007/s10554-011-9816-3 10.1001/2012.jama.11274 10.1016/j.jcct.2018.02.006 10.1161/CIRCULATIONAHA.106.671420 10.1016/j.jcmg.2012.09.016 10.1152/ajpendo.00053.2015 10.1148/radiol.13122550 10.1093/eurheartj/ehv690 10.1007/s00330-018-5781-8 10.1016/j.jacc.2006.01.041 10.1007/s00330-019-06139-2 10.1001/jamacardio.2018.1997 |
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References | Oikonomou, Marwan, Desai (CR12) 2018; 392 von Knebel Doeberitz, De Cecco, Schoepf, Duguay, Albrecht, van Assen, Bauer, Savage, Pannell, De Santis, Johnson, Varga-Szemes, Bayer, Schönberg, Nance, Tesche (CR29) 2018; 29 DeLong, DeLong, Clarke-Pearson (CR16) 1988; 44 Hoffmann, Moselewski, Nieman (CR6) 2006; 47 Goeller, Achenbach, Cadet (CR13) 2018; 3 Motoyama, Sarai, Harigaya (CR4) 2009; 54 Otsuka, Fukuda, Tanaka (CR5) 2013; 6 Ahmadi, Stone, Leipsic (CR26) 2016; 1 Itu, Rapaka, Passerini (CR14) 2016; 121 Hadamitzky, Freismith, Meyer (CR7) 2009; 2 Min, Leipsic, Pencina (CR9) 2012; 308 Grant, Stephens (CR17) 2015; 309 Lavi, McConnell, Rihal, Prasad, Mathew, Lerman, Lerman (CR18) 2007; 115 Yu, Zhao, Li (CR22) 2018; 12 Budoff, Dowe, Jollis (CR1) 2008; 52 Yu, Lu, Shen (CR10) 2019; 29 Pijls, De Bruyne, Peels (CR15) 1996; 334 Antonopoulos, Sanna, Sabharwal (CR11) 2017; 9 Siogkas, Anagnostopoulos, Liga, Exarchos, Sakellarios, Rigas, Scholte, Papafaklis, Loggitsi, Pelosi, Parodi, Maaniitty, Michalis, Knuuti, Neglia, Fotiadis (CR30) 2018; 29 Tesche, De Cecco, Baumann (CR19) 2018; 288 Min, Shaw, Devereux (CR8) 2007; 50 Waksman, Legutko, Singh (CR24) 2013; 61 Meijboom, Meijs, Schuijf (CR2) 2008; 52 Brugaletta, Garcia-Garcia, Shen (CR25) 2012; 28 van Hamersvelt, Zreik, Voskuil, Viergever, Išgum, Leiner (CR31) 2018; 29 Gaur, Øvrehus, Dey (CR27) 2016; 13 Miller, Rochitte, Dewey (CR3) 2008; 359 Coenen, Kim, Kruk (CR20) 2018; 11 Li, Zhang, Pan (CR21) 2013; 269 Yu, Lu, Li (CR23) 2018; 265 Dey, Gaur, Ovrehus (CR28) 2018; 28 M Yu (6400_CR23) 2018; 265 AS Antonopoulos (6400_CR11) 2017; 9 EK Oikonomou (6400_CR12) 2018; 392 R Waksman (6400_CR24) 2013; 61 M Yu (6400_CR10) 2019; 29 WB Meijboom (6400_CR2) 2008; 52 M Yu (6400_CR22) 2018; 12 ER DeLong (6400_CR16) 1988; 44 Shahar Lavi (6400_CR18) 2007; 115 RW Grant (6400_CR17) 2015; 309 A Ahmadi (6400_CR26) 2016; 1 Panagiotis K. Siogkas (6400_CR30) 2018; 29 NH Pijls (6400_CR15) 1996; 334 C Tesche (6400_CR19) 2018; 288 M Hadamitzky (6400_CR7) 2009; 2 M Li (6400_CR21) 2013; 269 L Itu (6400_CR14) 2016; 121 Philipp L. von Knebel Doeberitz (6400_CR29) 2018; 29 S Gaur (6400_CR27) 2016; 13 D Dey (6400_CR28) 2018; 28 JM Miller (6400_CR3) 2008; 359 M Goeller (6400_CR13) 2018; 3 Robbert W. van Hamersvelt (6400_CR31) 2018; 29 JK Min (6400_CR8) 2007; 50 JK Min (6400_CR9) 2012; 308 S Brugaletta (6400_CR25) 2012; 28 S Motoyama (6400_CR4) 2009; 54 MJ Budoff (6400_CR1) 2008; 52 K Otsuka (6400_CR5) 2013; 6 U Hoffmann (6400_CR6) 2006; 47 A Coenen (6400_CR20) 2018; 11 |
References_xml | – volume: 54 start-page: 49 issue: 1 year: 2009 end-page: 57 ident: CR4 article-title: Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2009.02.068 contributor: fullname: Harigaya – volume: 29 start-page: 2350 issue: 5 year: 2018 end-page: 2359 ident: CR31 article-title: Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis publication-title: European Radiology doi: 10.1007/s00330-018-5822-3 contributor: fullname: Leiner – volume: 11 start-page: e007217 issue: 6 year: 2018 ident: CR20 article-title: Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium publication-title: Circ Cardiovasc Imaging doi: 10.1161/CIRCIMAGING.117.007217 contributor: fullname: Kruk – volume: 265 start-page: 256 year: 2018 end-page: 261 ident: CR23 article-title: CT morphological index provides incremental value to machine learning based CT-FFR for predicting hemodynamically significant coronary stenosis publication-title: Int J Cardiol doi: 10.1016/j.ijcard.2018.01.075 contributor: fullname: Li – volume: 28 start-page: 2655 issue: 6 year: 2018 end-page: 2664 ident: CR28 article-title: Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study publication-title: Eur Radiol doi: 10.1007/s00330-017-5223-z contributor: fullname: Ovrehus – volume: 2 start-page: 404 year: 2009 end-page: 411 ident: CR7 article-title: Prognostic value of coronary computed tomographic angiography for prediction of cardiac events in patients with suspected coronary artery disease publication-title: JACC Cardiovasc Imaging doi: 10.1016/j.jcmg.2008.11.015 contributor: fullname: Meyer – volume: 1 start-page: 350 issue: 3 year: 2016 end-page: 357 ident: CR26 article-title: Association of coronary stenosis and plaque morphology with fractional flow reserve and outcomes publication-title: JAMA Cardiol doi: 10.1001/jamacardio.2016.0263 contributor: fullname: Leipsic – volume: 392 start-page: 929 issue: 10151 year: 2018 end-page: 939 ident: CR12 article-title: Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data publication-title: Lancet. doi: 10.1016/S0140-6736(18)31114-0 contributor: fullname: Desai – volume: 288 start-page: 64 issue: 1 year: 2018 end-page: 72 ident: CR19 article-title: Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling publication-title: Radiology doi: 10.1148/radiol.2018171291 contributor: fullname: Baumann – volume: 61 start-page: 917 year: 2013 end-page: 923 ident: CR24 article-title: FIRST: fractional flow reserve and intravascular ultrasound relationship study publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2012.12.012 contributor: fullname: Singh – volume: 334 start-page: 1703 issue: 26 year: 1996 end-page: 1708 ident: CR15 article-title: Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses publication-title: N Engl J Med doi: 10.1056/NEJM199606273342604 contributor: fullname: Peels – volume: 9 start-page: eaal2658 year: 2017 ident: CR11 article-title: Detecting human coronary inflammation by imaging perivascular fat publication-title: Sci Transl Med doi: 10.1126/scitranslmed.aal2658 contributor: fullname: Sabharwal – volume: 29 start-page: 2378 issue: 5 year: 2018 end-page: 2387 ident: CR29 article-title: Coronary CT angiography–derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia publication-title: European Radiology doi: 10.1007/s00330-018-5834-z contributor: fullname: Tesche – volume: 44 start-page: 837 issue: 3 year: 1988 end-page: 845 ident: CR16 article-title: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach publication-title: Biometrics doi: 10.2307/2531595 contributor: fullname: Clarke-Pearson – volume: 52 start-page: 1724 year: 2008 end-page: 1732 ident: CR1 article-title: Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2008.07.031 contributor: fullname: Jollis – volume: 359 start-page: 2324 year: 2008 end-page: 2336 ident: CR3 article-title: Diagnostic performance of coronary angiography by 64-row CT publication-title: N Engl J Med doi: 10.1056/NEJMoa0806576 contributor: fullname: Dewey – volume: 121 start-page: 42 issue: 1 year: 2016 end-page: 52 ident: CR14 article-title: A machine-learning approach for computation of fractional flow reserve from coronary computed tomography publication-title: J Appl Physiol (1985) doi: 10.1152/japplphysiol.00752.2015 contributor: fullname: Passerini – volume: 52 start-page: 2135 year: 2008 end-page: 2144 ident: CR2 article-title: Diagnostic accuracy of 64-slice computed tomography coronary angiography: a prospective, multicenter, multivendor study publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2008.08.058 contributor: fullname: Schuijf – volume: 50 start-page: 1161 year: 2007 end-page: 1170 ident: CR8 article-title: Prognostic value of multidetector coronary computed tomographic angiography for prediction of all-cause mortality publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2007.03.067 contributor: fullname: Devereux – volume: 28 start-page: 221 year: 2012 end-page: 228 ident: CR25 article-title: Morphology of coronary artery lesions assessed by virtual histology intravascular ultrasound tissue characterization and fractional flow reserve publication-title: Int J Cardiovasc Imaging doi: 10.1007/s10554-011-9816-3 contributor: fullname: Shen – volume: 308 start-page: 1237 year: 2012 end-page: 1245 ident: CR9 article-title: Diagnostic accuracy of fractional flow reserve from anatomic CT angiography publication-title: JAMA doi: 10.1001/2012.jama.11274 contributor: fullname: Pencina – volume: 12 start-page: 247 issue: 3 year: 2018 end-page: 254 ident: CR22 article-title: Relationship of the Duke jeopardy score combined with minimal lumen diameter as assessed by computed tomography angiography to the hemodynamic relevance of coronary artery stenosis publication-title: J Cardiovasc Comput Tomogr doi: 10.1016/j.jcct.2018.02.006 contributor: fullname: Li – volume: 115 start-page: 2715 issue: 21 year: 2007 end-page: 2721 ident: CR18 article-title: Local Production of Lipoprotein-Associated Phospholipase A 2 and Lysophosphatidylcholine in the Coronary Circulation publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.106.671420 contributor: fullname: Lerman – volume: 6 start-page: 448 issue: 4 year: 2013 end-page: 457 ident: CR5 article-title: Napkin-ring sign on coronary CT angiography for the prediction of acute coronary syndrome publication-title: JACC Cardiovasc Imaging doi: 10.1016/j.jcmg.2012.09.016 contributor: fullname: Tanaka – volume: 309 start-page: E205 issue: 3 year: 2015 end-page: E213 ident: CR17 article-title: Fat in flames: influence of cytokines and pattern recognition receptors on adipocyte lipolysis publication-title: Am J Physiol Endocrinol Metab doi: 10.1152/ajpendo.00053.2015 contributor: fullname: Stephens – volume: 269 start-page: 713 issue: 3 year: 2013 end-page: 721 ident: CR21 article-title: Coronary stenosis: morphologic index characterized by using CT angiography correlates with fractional flow reserve and is associated with hemodynamic status publication-title: Radiology doi: 10.1148/radiol.13122550 contributor: fullname: Pan – volume: 13 start-page: 1220 year: 2016 end-page: 1227 ident: CR27 article-title: Coronary plaque quantification and fractional flow reserve by coronary computed tomography angiography identify ischaemia-causing lesions publication-title: Eur Heart J doi: 10.1093/eurheartj/ehv690 contributor: fullname: Dey – volume: 29 start-page: 2117 issue: 4 year: 2018 end-page: 2126 ident: CR30 article-title: Noninvasive CT-based hemodynamic assessment of coronary lesions derived from fast computational analysis: a comparison against fractional flow reserve publication-title: European Radiology doi: 10.1007/s00330-018-5781-8 contributor: fullname: Fotiadis – volume: 47 start-page: 1655 year: 2006 end-page: 1662 ident: CR6 article-title: Noninvasive assessment of plaque morphology and composition in culprit and stable lesions in acute coronary syndrome and stable lesions in stable angina by multidetector computed tomography publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2006.01.041 contributor: fullname: Nieman – volume: 29 start-page: 3647 issue: 7 year: 2019 end-page: 3657 ident: CR10 article-title: The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFR , or high-risk plaque features? publication-title: Eur Radiol doi: 10.1007/s00330-019-06139-2 contributor: fullname: Shen – volume: 3 start-page: 858 issue: 9 year: 2018 end-page: 863 ident: CR13 article-title: Pericoronary adipose tissue computed tomography attenuation and high-risk plaque characteristics in acute coronary syndrome compared with stable coronary artery disease publication-title: JAMA Cardiol doi: 10.1001/jamacardio.2018.1997 contributor: fullname: Cadet – volume: 309 start-page: E205 issue: 3 year: 2015 ident: 6400_CR17 publication-title: Am J Physiol Endocrinol Metab doi: 10.1152/ajpendo.00053.2015 contributor: fullname: RW Grant – volume: 11 start-page: e007217 issue: 6 year: 2018 ident: 6400_CR20 publication-title: Circ Cardiovasc Imaging doi: 10.1161/CIRCIMAGING.117.007217 contributor: fullname: A Coenen – volume: 1 start-page: 350 issue: 3 year: 2016 ident: 6400_CR26 publication-title: JAMA Cardiol doi: 10.1001/jamacardio.2016.0263 contributor: fullname: A Ahmadi – volume: 3 start-page: 858 issue: 9 year: 2018 ident: 6400_CR13 publication-title: JAMA Cardiol doi: 10.1001/jamacardio.2018.1997 contributor: fullname: M Goeller – volume: 13 start-page: 1220 year: 2016 ident: 6400_CR27 publication-title: Eur Heart J doi: 10.1093/eurheartj/ehv690 contributor: fullname: S Gaur – volume: 359 start-page: 2324 year: 2008 ident: 6400_CR3 publication-title: N Engl J Med doi: 10.1056/NEJMoa0806576 contributor: fullname: JM Miller – volume: 29 start-page: 3647 issue: 7 year: 2019 ident: 6400_CR10 publication-title: Eur Radiol doi: 10.1007/s00330-019-06139-2 contributor: fullname: M Yu – volume: 61 start-page: 917 year: 2013 ident: 6400_CR24 publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2012.12.012 contributor: fullname: R Waksman – volume: 52 start-page: 2135 year: 2008 ident: 6400_CR2 publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2008.08.058 contributor: fullname: WB Meijboom – volume: 52 start-page: 1724 year: 2008 ident: 6400_CR1 publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2008.07.031 contributor: fullname: MJ Budoff – volume: 12 start-page: 247 issue: 3 year: 2018 ident: 6400_CR22 publication-title: J Cardiovasc Comput Tomogr doi: 10.1016/j.jcct.2018.02.006 contributor: fullname: M Yu – volume: 2 start-page: 404 year: 2009 ident: 6400_CR7 publication-title: JACC Cardiovasc Imaging doi: 10.1016/j.jcmg.2008.11.015 contributor: fullname: M Hadamitzky – volume: 265 start-page: 256 year: 2018 ident: 6400_CR23 publication-title: Int J Cardiol doi: 10.1016/j.ijcard.2018.01.075 contributor: fullname: M Yu – volume: 6 start-page: 448 issue: 4 year: 2013 ident: 6400_CR5 publication-title: JACC Cardiovasc Imaging doi: 10.1016/j.jcmg.2012.09.016 contributor: fullname: K Otsuka – volume: 288 start-page: 64 issue: 1 year: 2018 ident: 6400_CR19 publication-title: Radiology doi: 10.1148/radiol.2018171291 contributor: fullname: C Tesche – volume: 29 start-page: 2378 issue: 5 year: 2018 ident: 6400_CR29 publication-title: European Radiology doi: 10.1007/s00330-018-5834-z contributor: fullname: Philipp L. von Knebel Doeberitz – volume: 9 start-page: eaal2658 year: 2017 ident: 6400_CR11 publication-title: Sci Transl Med doi: 10.1126/scitranslmed.aal2658 contributor: fullname: AS Antonopoulos – volume: 334 start-page: 1703 issue: 26 year: 1996 ident: 6400_CR15 publication-title: N Engl J Med doi: 10.1056/NEJM199606273342604 contributor: fullname: NH Pijls – volume: 28 start-page: 221 year: 2012 ident: 6400_CR25 publication-title: Int J Cardiovasc Imaging doi: 10.1007/s10554-011-9816-3 contributor: fullname: S Brugaletta – volume: 115 start-page: 2715 issue: 21 year: 2007 ident: 6400_CR18 publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.106.671420 contributor: fullname: Shahar Lavi – volume: 308 start-page: 1237 year: 2012 ident: 6400_CR9 publication-title: JAMA doi: 10.1001/2012.jama.11274 contributor: fullname: JK Min – volume: 47 start-page: 1655 year: 2006 ident: 6400_CR6 publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2006.01.041 contributor: fullname: U Hoffmann – volume: 29 start-page: 2350 issue: 5 year: 2018 ident: 6400_CR31 publication-title: European Radiology doi: 10.1007/s00330-018-5822-3 contributor: fullname: Robbert W. van Hamersvelt – volume: 121 start-page: 42 issue: 1 year: 2016 ident: 6400_CR14 publication-title: J Appl Physiol (1985) doi: 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This study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary lesions.... This study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary lesions. Patients... ObjectiveThis study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary... OBJECTIVEThis study aimed to investigate the association between perivascular fat attenuation index (FAI) and hemodynamic significance of coronary lesions.... |
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SubjectTerms | Angina Angiography Artificial intelligence Attenuation Cardiac Computation Computed tomography Constraining Diagnostic Radiology Diagnostic systems Hemodynamics Imaging Internal Medicine Interventional Radiology Ischemia Learning algorithms Lesions Machine learning Medical diagnosis Medical imaging Medicine Medicine & Public Health Multivariate analysis Neuroradiology Radiology Risk Stenosis Subgroups Ultrasound |
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Title | Diagnostic performance of perivascular fat attenuation index to predict hemodynamic significance of coronary stenosis: a preliminary coronary computed tomography angiography study |
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