A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography
Abstract Background Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process in...
Uložené v:
| Vydané v: | European heart journal Ročník 40; číslo 43; s. 3529 - 3543 |
|---|---|
| Hlavní autori: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
Oxford University Press
14.11.2019
|
| Predmet: | |
| ISSN: | 0195-668X, 1522-9645, 1522-9645 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Abstract
Background
Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction.
Methods and results
We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62–0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P < 0.001). In Study 3, FRP was significantly higher in 44 patients presenting with acute myocardial infarction compared with 44 matched controls, but unlike FAI, remained unchanged 6 months after the index event, confirming that FRP detects persistent PVAT changes not captured by FAI.
Conclusion
The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art. |
|---|---|
| AbstractList | Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction.BACKGROUNDCoronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction.We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62-0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P < 0.001). In Study 3, FRP was significantly higher in 44 patients presenting with acute myocardial infarction compared with 44 matched controls, but unlike FAI, remained unchanged 6 months after the index event, confirming that FRP detects persistent PVAT changes not captured by FAI.METHODS AND RESULTSWe present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62-0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P < 0.001). In Study 3, FRP was significantly higher in 44 patients presenting with acute myocardial infarction compared with 44 matched controls, but unlike FAI, remained unchanged 6 months after the index event, confirming that FRP detects persistent PVAT changes not captured by FAI.The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art.CONCLUSIONThe CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art. Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction. We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62-0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P < 0.001). In Study 3, FRP was significantly higher in 44 patients presenting with acute myocardial infarction compared with 44 matched controls, but unlike FAI, remained unchanged 6 months after the index event, confirming that FRP detects persistent PVAT changes not captured by FAI. The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art. Abstract Background Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction. Methods and results We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62–0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P < 0.001). In Study 3, FRP was significantly higher in 44 patients presenting with acute myocardial infarction compared with 44 matched controls, but unlike FAI, remained unchanged 6 months after the index event, confirming that FRP detects persistent PVAT changes not captured by FAI. Conclusion The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art. |
| Author | Kesavan, Sujatha Centeno, Erika Hutt Flamm, Scott D Channon, Keith M Shirodaria, Cheerag Neubauer, Stefan Sabharwal, Nikant Thomas, Katharine E Dweck, Marc R Antonopoulos, Alexios S Alashi, Alaa Oikonomou, Evangelos K Kelion, Andrew Antoniades, Charalambos Thomas, Sheena Deanfield, John Griffin, Brian P Desai, Milind Y Van Beek, Edwin J R Newby, David E Fan, Lampson M Williams, Michelle C Marwan, Mohamed Herdman, Laura Akoumianakis, Ioannis Hopewell, Jemma C Kotanidis, Christos P Lyasheva, Maria Achenbach, Stephan |
| Author_xml | – sequence: 1 givenname: Evangelos K orcidid: 0000-0003-4362-0720 surname: Oikonomou fullname: Oikonomou, Evangelos K organization: Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK – sequence: 2 givenname: Michelle C orcidid: 0000-0003-3556-2428 surname: Williams fullname: Williams, Michelle C organization: British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Chancellor's Building, 49 Little France Cres, Edinburgh, UK – sequence: 3 givenname: Christos P orcidid: 0000-0002-1494-8340 surname: Kotanidis fullname: Kotanidis, Christos P organization: Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK – sequence: 4 givenname: Milind Y surname: Desai fullname: Desai, Milind Y organization: Heart and Vascular Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, USA – sequence: 5 givenname: Mohamed surname: Marwan fullname: Marwan, Mohamed organization: Department of Cardiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Ulmenweg 18, Erlangen, Germany – sequence: 6 givenname: Alexios S surname: Antonopoulos fullname: Antonopoulos, Alexios S organization: Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK – sequence: 7 givenname: Katharine E orcidid: 0000-0002-7788-4663 surname: Thomas fullname: Thomas, Katharine E organization: Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK – sequence: 8 givenname: Sheena orcidid: 0000-0002-2097-465X surname: Thomas fullname: Thomas, Sheena organization: Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK – sequence: 9 givenname: Ioannis orcidid: 0000-0002-4674-0210 surname: Akoumianakis fullname: Akoumianakis, Ioannis organization: Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK – sequence: 10 givenname: Lampson M surname: Fan fullname: Fan, Lampson M organization: Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK – sequence: 11 givenname: Sujatha orcidid: 0000-0001-5007-5527 surname: Kesavan fullname: Kesavan, Sujatha organization: Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK – sequence: 12 givenname: Laura surname: Herdman fullname: Herdman, Laura organization: Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK – sequence: 13 givenname: Alaa surname: Alashi fullname: Alashi, Alaa organization: Heart and Vascular Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, USA – sequence: 14 givenname: Erika Hutt surname: Centeno fullname: Centeno, Erika Hutt organization: Heart and Vascular Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, USA – sequence: 15 givenname: Maria surname: Lyasheva fullname: Lyasheva, Maria organization: Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK – sequence: 16 givenname: Brian P surname: Griffin fullname: Griffin, Brian P organization: Heart and Vascular Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, USA – sequence: 17 givenname: Scott D surname: Flamm fullname: Flamm, Scott D organization: Heart and Vascular Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, USA – sequence: 18 givenname: Cheerag orcidid: 0000-0001-6463-1838 surname: Shirodaria fullname: Shirodaria, Cheerag organization: Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK – sequence: 19 givenname: Nikant orcidid: 0000-0002-1989-947X surname: Sabharwal fullname: Sabharwal, Nikant organization: Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK – sequence: 20 givenname: Andrew surname: Kelion fullname: Kelion, Andrew organization: Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK – sequence: 21 givenname: Marc R orcidid: 0000-0001-9847-5917 surname: Dweck fullname: Dweck, Marc R organization: British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Chancellor's Building, 49 Little France Cres, Edinburgh, UK – sequence: 22 givenname: Edwin J R orcidid: 0000-0002-2777-5071 surname: Van Beek fullname: Van Beek, Edwin J R organization: Edinburgh Imaging Facility QMRI, University of Edinburgh, 47 Little France Cres, Edinburgh, UK – sequence: 23 givenname: John orcidid: 0000-0001-8806-6052 surname: Deanfield fullname: Deanfield, John organization: National Centre for Cardiovascular Prevention and Outcomes, Institute of Cardiovascular Science, University College London, 1 St Martins Le Grand, London, UK – sequence: 24 givenname: Jemma C surname: Hopewell fullname: Hopewell, Jemma C organization: Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, BHF Centre for Research Excellence, Big Data Institute, Old Road Campus, Roosevelt Drive, Oxford, UK – sequence: 25 givenname: Stefan surname: Neubauer fullname: Neubauer, Stefan organization: Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK – sequence: 26 givenname: Keith M orcidid: 0000-0002-1043-4342 surname: Channon fullname: Channon, Keith M organization: Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK – sequence: 27 givenname: Stephan surname: Achenbach fullname: Achenbach, Stephan organization: Department of Cardiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Ulmenweg 18, Erlangen, Germany – sequence: 28 givenname: David E orcidid: 0000-0001-7971-4628 surname: Newby fullname: Newby, David E organization: British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Chancellor's Building, 49 Little France Cres, Edinburgh, UK – sequence: 29 givenname: Charalambos orcidid: 0000-0002-6983-5423 surname: Antoniades fullname: Antoniades, Charalambos email: antoniad@well.ox.ac.uk organization: Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31504423$$D View this record in MEDLINE/PubMed |
| BookMark | eNo9kU1LxDAQhoMo7ofePUmOglSTNE3Toyx-geBFwVtJk8lu1japSSvov_AfW1n1NId5eOZl3gXa98EDQieUXFBS5Zcwxg2oOGwvYfNZVGwPzWnBWFYJXuyjOaFVkQkhX2ZokdKWECIFFYdoltOCcM7yOfq6wj68Q4s7pTfOA24nn3d-nRmI7h0Mjsq4METlk46uH0LnNE5u7dUwRsDB4v4HVEmPrYrYqgG7ro-TM2GtonFK4-jSK-4jGKcHFzwe03QA6xCDV_EDr56w8msX1lH1m48jdGBVm-D4dy7R88310-oue3i8vV9dPWSaMzlkjS6BE9UQxo2k1AhrZSmANDloaqzgupJcVKIsc645pbapSpBSk8Za0FWRL9HZzjuFfRshDXXnkoa2VR7CmGrGpCxZLpmY0NNfdGw6MHUfXTcFr__eOAHnOyCM_f-WkvqnpPq_pHpXUv4NhvuMZA |
| CitedBy_id | crossref_primary_10_1038_s41598_024_73867_x crossref_primary_10_1186_s41747_024_00493_7 crossref_primary_10_3389_fcvm_2024_1499219 crossref_primary_10_1007_s10554_025_03329_6 crossref_primary_10_1186_s41824_021_00107_0 crossref_primary_10_1093_eurheartj_ehab538 crossref_primary_10_1111_eci_14364 crossref_primary_10_3389_fphys_2022_980996 crossref_primary_10_3390_jcm14030769 crossref_primary_10_1016_j_acra_2022_03_015 crossref_primary_10_1186_s12872_024_03970_4 crossref_primary_10_1007_s11547_025_01991_3 crossref_primary_10_2478_rjc_2024_0029 crossref_primary_10_1016_j_crad_2024_08_018 crossref_primary_10_3389_fcvm_2022_773524 crossref_primary_10_3390_ijms23126504 crossref_primary_10_1089_ars_2020_8128 crossref_primary_10_3390_diagnostics13122074 crossref_primary_10_1016_j_ejrad_2022_110269 crossref_primary_10_1007_s10554_020_01926_1 crossref_primary_10_1186_s12880_025_01876_x crossref_primary_10_1016_j_xcrm_2025_102288 crossref_primary_10_1016_j_jcct_2022_07_005 crossref_primary_10_1136_heartjnl_2021_320265 crossref_primary_10_3389_fcvm_2023_1206156 crossref_primary_10_1093_ehjopen_oeab019 crossref_primary_10_1007_s11886_023_01982_8 crossref_primary_10_1038_s41746_023_00806_x crossref_primary_10_1016_j_atherosclerosis_2025_120414 crossref_primary_10_1093_eurheartjsupp_suad038 crossref_primary_10_1007_s11936_020_00838_6 crossref_primary_10_1016_j_crad_2022_08_149 crossref_primary_10_1186_s12933_025_02913_3 crossref_primary_10_1016_j_ajpc_2021_100176 crossref_primary_10_1016_j_acra_2023_01_023 crossref_primary_10_1016_j_ebiom_2020_102726 crossref_primary_10_1111_bph_15654 crossref_primary_10_3390_jpm11100991 crossref_primary_10_1007_s11547_025_02063_2 crossref_primary_10_1161_CIRCIMAGING_125_018291 crossref_primary_10_1093_bjr_tqae032 crossref_primary_10_1016_j_ijcard_2021_04_060 crossref_primary_10_1109_ACCESS_2024_3369912 crossref_primary_10_1093_eurheartj_ehab678 crossref_primary_10_1186_s13244_024_01804_7 crossref_primary_10_1016_j_clinthera_2023_09_019 crossref_primary_10_1016_j_ejrad_2021_109767 crossref_primary_10_1016_j_medp_2023_100001 crossref_primary_10_1093_eurheartj_ehaa227 crossref_primary_10_1038_s41598_024_70218_8 crossref_primary_10_1161_JAHA_120_017759 crossref_primary_10_1093_eurheartj_ehz824 crossref_primary_10_3389_fcvm_2022_949454 crossref_primary_10_3389_fneur_2024_1340202 crossref_primary_10_3389_fcvm_2022_896366 crossref_primary_10_1016_j_pcad_2023_09_001 crossref_primary_10_1097_MD_0000000000037014 crossref_primary_10_1038_s41598_021_02971_z crossref_primary_10_1002_clc_24305 crossref_primary_10_3390_jpm12081208 crossref_primary_10_3390_diagnostics13182902 crossref_primary_10_3389_fphys_2025_1579885 crossref_primary_10_3390_cells10040879 crossref_primary_10_3390_ijms26178320 crossref_primary_10_1007_s00330_022_08587_9 crossref_primary_10_1007_s00330_025_11347_0 crossref_primary_10_1177_02841851231189998 crossref_primary_10_1186_s12880_024_01325_1 crossref_primary_10_1007_s11886_020_01402_1 crossref_primary_10_1016_j_cpcardiol_2023_101841 crossref_primary_10_1016_j_ijcard_2025_133778 crossref_primary_10_1007_s00464_023_10403_4 crossref_primary_10_1093_eurheartj_ehac751 crossref_primary_10_1080_07853890_2024_2431606 crossref_primary_10_1093_eurjpc_zwab067 crossref_primary_10_1088_1361_6560_ac69a7 crossref_primary_10_1161_CIRCIMAGING_121_013025 crossref_primary_10_2478_jim_2021_0008 crossref_primary_10_3390_jcm12237212 crossref_primary_10_1093_eurheartj_ehaa1065 crossref_primary_10_1038_s41598_023_39809_9 crossref_primary_10_1093_cvr_cvz319 crossref_primary_10_1007_s00259_020_05171_5 crossref_primary_10_1109_JBHI_2022_3146590 crossref_primary_10_1161_CIRCIMAGING_120_011747 crossref_primary_10_1259_bjr_20220971 crossref_primary_10_1002_mp_17324 crossref_primary_10_1016_j_crad_2021_07_010 crossref_primary_10_1093_cvr_cvab286 crossref_primary_10_1007_s10554_023_02831_z crossref_primary_10_1093_ehjopen_oeac018 crossref_primary_10_1016_j_ejrad_2021_110102 crossref_primary_10_1016_j_ijcard_2025_133482 crossref_primary_10_3389_fphys_2025_1651249 crossref_primary_10_7759_cureus_58400 crossref_primary_10_1007_s00330_022_08812_5 crossref_primary_10_1007_s00330_021_08109_z crossref_primary_10_1016_j_compbiomed_2024_108910 crossref_primary_10_1161_JAHA_120_016612 crossref_primary_10_3390_bioengineering10030360 crossref_primary_10_1093_eurheartj_ehab045 crossref_primary_10_1097_CP9_0000000000000117 crossref_primary_10_1016_j_acra_2023_04_006 crossref_primary_10_1097_RCT_0000000000001749 crossref_primary_10_1016_j_tcm_2024_06_003 crossref_primary_10_1016_j_jmir_2024_01_005 crossref_primary_10_1016_j_jcmg_2021_09_014 crossref_primary_10_1007_s12553_020_00498_3 crossref_primary_10_1007_s10278_024_01012_1 crossref_primary_10_1016_j_rec_2020_06_036 crossref_primary_10_3390_ijms232113638 crossref_primary_10_1016_j_ijcard_2021_12_006 crossref_primary_10_1016_j_jacasi_2022_12_005 crossref_primary_10_1093_bjr_tqaf046 crossref_primary_10_1371_journal_pone_0291451 crossref_primary_10_1097_RCT_0000000000001770 crossref_primary_10_1186_s12933_023_01748_0 crossref_primary_10_1007_s00330_023_10111_6 crossref_primary_10_1093_eurheartj_ehae465 crossref_primary_10_1155_2022_4824575 crossref_primary_10_4330_wjc_v13_i10_546 crossref_primary_10_1016_j_ijcard_2025_133906 crossref_primary_10_3389_fdgth_2023_1201392 crossref_primary_10_1007_s12559_023_10118_7 crossref_primary_10_1016_j_jcmg_2023_04_004 crossref_primary_10_1177_00033197221098894 crossref_primary_10_1186_s13244_024_01731_7 crossref_primary_10_4250_jcvi_2021_0016 crossref_primary_10_1016_j_ijcard_2024_132749 crossref_primary_10_1038_s41569_024_01110_1 crossref_primary_10_1016_j_compmedimag_2023_102264 crossref_primary_10_1016_j_numecd_2024_06_020 crossref_primary_10_1097_HCO_0000000000000888 crossref_primary_10_3390_medsci11010020 crossref_primary_10_3389_fcvm_2024_1462566 crossref_primary_10_1111_eci_70017 crossref_primary_10_1007_s11547_024_01771_5 crossref_primary_10_1111_echo_70098 crossref_primary_10_3389_fcvm_2023_1192093 crossref_primary_10_1093_eurheartj_ehad484 crossref_primary_10_3389_fcvm_2021_736491 crossref_primary_10_1016_j_jcct_2020_04_004 crossref_primary_10_1093_cvr_cvab059 crossref_primary_10_1016_j_ejro_2025_100638 crossref_primary_10_3389_fcvm_2022_829553 crossref_primary_10_1007_s10278_024_01013_0 crossref_primary_10_3390_life14060652 crossref_primary_10_1007_s40619_024_01493_w crossref_primary_10_1016_j_acra_2020_09_021 crossref_primary_10_1016_j_jcmg_2022_02_004 crossref_primary_10_3390_ijerph18126677 crossref_primary_10_1007_s11883_024_01205_7 crossref_primary_10_1016_j_jcmg_2024_08_006 crossref_primary_10_15829_1560_4071_2021_4425 crossref_primary_10_1093_cvr_cvaa272 crossref_primary_10_1186_s12931_024_02977_x crossref_primary_10_3389_fcvm_2021_736223 crossref_primary_10_1038_s41569_019_0278_y crossref_primary_10_3390_ijms222413471 crossref_primary_10_1007_s10554_020_01896_4 crossref_primary_10_1038_s41598_020_59873_9 crossref_primary_10_1161_ATVBAHA_119_312899 crossref_primary_10_1148_rg_2020190220 crossref_primary_10_3390_cells11060991 crossref_primary_10_1093_eurheartj_ehaa608 crossref_primary_10_1186_s12933_024_02411_y crossref_primary_10_5114_pjr_2023_126084 crossref_primary_10_1109_ACCESS_2023_3327808 crossref_primary_10_1186_s40644_024_00821_5 crossref_primary_10_3389_fcvm_2025_1613900 crossref_primary_10_1007_s11831_024_10099_2 crossref_primary_10_1186_s13052_024_01739_1 crossref_primary_10_2478_jim_2019_0026 crossref_primary_10_1259_bjr_20220885 crossref_primary_10_3390_diagnostics13020307 crossref_primary_10_1016_j_ultrasmedbio_2023_06_008 crossref_primary_10_3390_a18020086 crossref_primary_10_1007_s12928_025_01126_5 crossref_primary_10_1093_eurheartj_ehaa155 crossref_primary_10_1093_cvr_cvaa021 crossref_primary_10_1093_eurjpc_zwae279 crossref_primary_10_1016_j_atherosclerosis_2024_117580 crossref_primary_10_1186_s12933_024_02530_6 crossref_primary_10_1186_s12933_022_01478_9 crossref_primary_10_1111_eci_70004 crossref_primary_10_3389_fonc_2021_646267 crossref_primary_10_1016_j_cjca_2021_09_030 crossref_primary_10_3389_fcvm_2022_1016032 crossref_primary_10_1016_j_jcct_2020_09_008 crossref_primary_10_1007_s11883_024_01245_z crossref_primary_10_1016_j_jcct_2024_09_002 crossref_primary_10_1186_s12880_024_01295_4 crossref_primary_10_3389_fcvm_2021_743538 crossref_primary_10_1093_eurheartj_ehad576 crossref_primary_10_1155_2024_2991243 crossref_primary_10_1186_s12872_023_03162_6 crossref_primary_10_1111_dom_14933 crossref_primary_10_1259_bjr_20190812 crossref_primary_10_1038_s41598_022_19546_1 crossref_primary_10_3390_diagnostics11122383 crossref_primary_10_1007_s00259_021_05341_z crossref_primary_10_1016_j_ijcard_2020_06_008 crossref_primary_10_1016_S0140_6736_24_00596_8 crossref_primary_10_1016_j_jcmg_2025_06_012 crossref_primary_10_1007_s00330_021_08518_0 crossref_primary_10_1016_j_clinimag_2023_02_009 crossref_primary_10_1148_radiol_2021203179 crossref_primary_10_1038_s41598_022_24884_1 crossref_primary_10_1186_s12880_023_01051_0 crossref_primary_10_1007_s12350_022_03179_y crossref_primary_10_1093_eurheartj_ehae415 crossref_primary_10_1080_13506129_2025_2486072 crossref_primary_10_1007_s11886_025_02221_y crossref_primary_10_3390_jimaging9020032 crossref_primary_10_1016_j_atherosclerosis_2023_03_002 crossref_primary_10_1016_j_ejro_2025_100682 crossref_primary_10_1016_j_cjca_2024_06_011 crossref_primary_10_3389_fphys_2024_1522471 crossref_primary_10_1097_RTI_0000000000000790 crossref_primary_10_1016_j_imu_2024_101535 crossref_primary_10_1016_j_acra_2024_08_022 crossref_primary_10_1016_j_clnu_2023_04_003 crossref_primary_10_1093_bjr_tqad017 crossref_primary_10_2459_JCM_0000000000001433 crossref_primary_10_3389_fcvm_2022_822308 crossref_primary_10_1161_CIRCULATIONAHA_119_044720 crossref_primary_10_1007_s42979_023_02529_y crossref_primary_10_3389_fmed_2025_1626390 crossref_primary_10_1016_j_jcmg_2020_06_033 crossref_primary_10_1007_s00330_021_08429_0 crossref_primary_10_1097_RTI_0000000000000583 crossref_primary_10_3389_fonc_2021_663370 crossref_primary_10_1007_s12170_023_00731_4 crossref_primary_10_1093_eurheartj_ehz929 crossref_primary_10_1186_s12933_024_02474_x crossref_primary_10_1007_s10554_025_03425_7 crossref_primary_10_1136_heartjnl_2023_322482 crossref_primary_10_1016_j_ijcha_2025_101765 crossref_primary_10_1111_obr_13820 crossref_primary_10_1016_j_crad_2025_107018 crossref_primary_10_1016_j_acra_2024_11_063 crossref_primary_10_1136_heartjnl_2022_321158 crossref_primary_10_3389_fcvm_2023_1120361 crossref_primary_10_1007_s10278_024_01028_7 crossref_primary_10_1080_14779072_2025_2564137 crossref_primary_10_1002_lipd_12290 crossref_primary_10_1093_eurheartj_ehz930 crossref_primary_10_1007_s11883_023_01174_3 crossref_primary_10_1007_s00330_024_10607_9 crossref_primary_10_15829_1560_4071_2025_6540 crossref_primary_10_3389_fcvm_2021_755295 crossref_primary_10_1038_s41440_023_01469_7 crossref_primary_10_1002_jmri_29440 crossref_primary_10_1007_s12350_023_03247_x crossref_primary_10_3390_bioengineering11080843 crossref_primary_10_1016_j_heliyon_2023_e15738 crossref_primary_10_1002_mco2_413 crossref_primary_10_1016_j_crad_2021_10_019 crossref_primary_10_3390_diagnostics13122021 crossref_primary_10_1080_17434440_2025_2500631 crossref_primary_10_1007_s00330_022_09175_7 crossref_primary_10_1007_s12181_021_00454_z crossref_primary_10_1016_j_jcmg_2024_04_019 crossref_primary_10_1016_j_jcmg_2025_05_017 crossref_primary_10_1093_eurheartj_ehae508 crossref_primary_10_1111_echo_70148 crossref_primary_10_3390_healthcare10020232 crossref_primary_10_3389_fcvm_2022_839400 crossref_primary_10_3390_jcm14020462 crossref_primary_10_1093_eurheartj_ehab472 crossref_primary_10_1093_eurheartj_ehae069 crossref_primary_10_1016_j_hfc_2021_11_003 crossref_primary_10_1007_s00059_023_05207_0 crossref_primary_10_3389_fmed_2022_1025382 crossref_primary_10_1186_s40537_023_00817_1 crossref_primary_10_1093_ehjimp_qyaf031 crossref_primary_10_1007_s11547_023_01658_x crossref_primary_10_1097_QAD_0000000000002449 crossref_primary_10_1097_QAD_0000000000002448 crossref_primary_10_1016_j_hfc_2024_12_005 crossref_primary_10_1016_j_cjca_2024_07_014 crossref_primary_10_3389_fcvm_2023_1223035 crossref_primary_10_1259_bjr_20211274 crossref_primary_10_2459_JCM_0000000000001103 crossref_primary_10_3389_fcvm_2021_782971 crossref_primary_10_1016_j_ejrad_2020_108969 crossref_primary_10_1016_j_ijcard_2020_09_070 crossref_primary_10_1055_a_2499_3122 crossref_primary_10_3390_diagnostics14161830 crossref_primary_10_1007_s00330_023_10527_0 crossref_primary_10_1016_j_kint_2021_05_031 crossref_primary_10_1093_eurjpc_zwab139 crossref_primary_10_1097_MD_0000000000040592 crossref_primary_10_1016_j_ejrad_2021_109551 crossref_primary_10_1007_s00330_023_10311_0 crossref_primary_10_3389_fcvm_2020_618849 crossref_primary_10_1007_s12652_021_03612_z crossref_primary_10_1111_bph_15634 crossref_primary_10_1007_s40119_022_00271_9 crossref_primary_10_1111_bph_15632 crossref_primary_10_1093_ehjci_jeac092 crossref_primary_10_1148_ryct_240242 crossref_primary_10_1161_CIRCULATIONAHA_122_060308 crossref_primary_10_1186_s12938_024_01273_5 crossref_primary_10_1136_openhrt_2020_001297 crossref_primary_10_1093_bjr_tqad046 crossref_primary_10_1016_j_jcct_2022_05_004 crossref_primary_10_1093_bjr_tqae135 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2019. Published by Oxford University Press on behalf of the European Society of Cardiology. 2019 The Author(s) 2019. Published by Oxford University Press on behalf of the European Society of Cardiology. |
| Copyright_xml | – notice: The Author(s) 2019. Published by Oxford University Press on behalf of the European Society of Cardiology. 2019 – notice: The Author(s) 2019. Published by Oxford University Press on behalf of the European Society of Cardiology. |
| DBID | TOX CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1093/eurheartj/ehz592 |
| DatabaseName | Oxford Journals Open Access Collection Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: TOX name: Oxford Journals Open Access Collection url: https://academic.oup.com/journals/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| DocumentTitleAlternate | Focus Issue on Preventive Cardiology |
| EISSN | 1522-9645 |
| EndPage | 3543 |
| ExternalDocumentID | 31504423 10.1093/eurheartj/ehz592 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: National Institute for Health Research Oxford Biomedical Research Centre – fundername: SCOT-HEART grantid: CZH/4/588 – fundername: British Heart Foundation grantid: FS/16/15/32047; TG/16/3/32687; CH/16/1/32013; FS/14/55/30806 funderid: 10.13039/501100000274 – fundername: Edinburgh and Lothians Health Foundation Trust – fundername: Heart Diseases Research Fund – fundername: Chief Scientist Office of the Scottish Government, the British Heart Foundation grantid: CH/09/002; RE/13/3/30183 – fundername: British Heart Foundation grantid: TG/16/3/32687 – fundername: British Heart Foundation grantid: RE/18/5/34216 – fundername: Department of Health – fundername: Chief Scientist Office grantid: CZH/4/588 – fundername: British Heart Foundation grantid: FS/14/78/31020 – fundername: Wellcome Trust – fundername: British Heart Foundation grantid: RG/17/10/32859 – fundername: British Heart Foundation grantid: CH/16/1/32013 – fundername: British Heart Foundation grantid: PG/13/56/30383 – fundername: British Heart Foundation grantid: RE/13/3/30183 |
| GroupedDBID | --- -E4 .2P .I3 .XZ .ZR 08P 0R~ 18M 1TH 29G 2WC 4.4 482 48X 53G 5GY 5RE 5VS 5WA 5WD 70D AABZA AACZT AAJKP AAMVS AAOGV AAPNW AAPQZ AAPXW AARHZ AASNB AAUAY AAUQX AAVAP ABEUO ABIXL ABKDP ABNHQ ABNKS ABOCM ABPTD ABQLI ABQNK ABWST ABXVV ABZBJ ACGFO ACGFS ACPRK ACUFI ACUTJ ACUTO ACYHN ADBBV ADEYI ADEZT ADGZP ADHKW ADHZD ADIPN ADJQC ADOCK ADQBN ADRIX ADRTK ADVEK ADYVW ADZXQ AEGPL AEGXH AEJOX AEKSI AEMDU AENEX AENZO AEPUE AETBJ AEWNT AFFZL AFIYH AFOFC AFXAL AFXEN AGINJ AGKEF AGQXC AGSYK AGUTN AHMBA AHXPO AIAGR AIJHB AJEEA ALMA_UNASSIGNED_HOLDINGS ALUQC APIBT APWMN ATGXG AXUDD BAWUL BAYMD BCGUY BCRHZ BEYMZ BHONS BTRTY BVRKM C45 CDBKE CS3 CZ4 DAKXR DIK DILTD D~K E3Z EBS EE~ EMOBN ENERS F5P F9B FECEO FLUFQ FOEOM FOTVD FQBLK GAUVT GJXCC GX1 H13 H5~ HAR HW0 HZ~ IOX J21 KAQDR KBUDW KOP KQ8 KSI KSN L7B M-Z M41 M49 MHKGH ML0 N9A NGC NOMLY NOYVH NU- O9- OAUYM OAWHX OB3 OCZFY ODMLO OGROG OJQWA OJZSN OK1 OPAEJ OVD OWPYF P2P PAFKI PEELM PQQKQ Q1. Q5Y R44 RD5 ROL ROX ROZ RUSNO RW1 RXO SEL TCURE TEORI TJX TOX W8F WOQ X7H YAYTL YKOAZ YXANX ZKX ~91 AAFWJ ABDFA ABEJV ABGNP ABJNI ABPQP ABVGC ADGHP ADNBA AEMQT AFYAG AGORE AHGBF AHMMS AJBYB AJNCP ALXQX CGR CUY CVF ECM EIF JXSIZ NPM 7X8 |
| ID | FETCH-LOGICAL-c428t-bc7e40ab024d811d6ff876e0b3ec1df64c9846967734c411fb97e88c0bffec953 |
| IEDL.DBID | TOX |
| ISICitedReferencesCount | 359 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000501743300009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0195-668X 1522-9645 |
| IngestDate | Sun Nov 09 14:19:32 EST 2025 Mon Jul 21 05:42:29 EDT 2025 Wed Sep 11 04:40:39 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 43 |
| Keywords | Adipose tissue Computed tomography Coronary artery disease Machine learning Risk stratification Radiomics |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. The Author(s) 2019. Published by Oxford University Press on behalf of the European Society of Cardiology. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c428t-bc7e40ab024d811d6ff876e0b3ec1df64c9846967734c411fb97e88c0bffec953 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-1494-8340 0000-0001-9847-5917 0000-0003-4362-0720 0000-0002-1043-4342 0000-0002-7788-4663 0000-0002-6983-5423 0000-0001-5007-5527 0000-0001-8806-6052 0000-0002-2097-465X 0000-0001-7971-4628 0000-0002-1989-947X 0000-0002-4674-0210 0000-0001-6463-1838 0000-0002-2777-5071 0000-0003-3556-2428 |
| OpenAccessLink | https://dx.doi.org/10.1093/eurheartj/ehz592 |
| PMID | 31504423 |
| PQID | 2288723826 |
| PQPubID | 23479 |
| PageCount | 15 |
| ParticipantIDs | proquest_miscellaneous_2288723826 pubmed_primary_31504423 oup_primary_10_1093_eurheartj_ehz592 |
| PublicationCentury | 2000 |
| PublicationDate | 2019-11-14 |
| PublicationDateYYYYMMDD | 2019-11-14 |
| PublicationDate_xml | – month: 11 year: 2019 text: 2019-11-14 day: 14 |
| PublicationDecade | 2010 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | European heart journal |
| PublicationTitleAlternate | Eur Heart J |
| PublicationYear | 2019 |
| Publisher | Oxford University Press |
| Publisher_xml | – name: Oxford University Press |
| References | 31534173 - Nat Rev Cardiol. 2019 Nov;16(11):646-647. doi: 10.1038/s41569-019-0278-y. 31605139 - Eur Heart J. 2019 Nov 14;40(43):3544-3546. doi: 10.1093/eurheartj/ehz717. |
| References_xml | – reference: 31534173 - Nat Rev Cardiol. 2019 Nov;16(11):646-647. doi: 10.1038/s41569-019-0278-y. – reference: 31605139 - Eur Heart J. 2019 Nov 14;40(43):3544-3546. doi: 10.1093/eurheartj/ehz717. |
| SSID | ssj0008616 |
| Score | 2.6912854 |
| Snippet | Abstract
Background
Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as... Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular... |
| SourceID | proquest pubmed oup |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 3529 |
| SubjectTerms | Adipose Tissue - diagnostic imaging Adipose Tissue - pathology Aged Algorithms Case-Control Studies Computed Tomography Angiography Coronary Artery Disease - diagnostic imaging Coronary Artery Disease - genetics Coronary Artery Disease - pathology Female Follow-Up Studies Gene Expression Profiling - methods Genetic Markers Humans Machine Learning Male Middle Aged Phenotype Plaque, Atherosclerotic - diagnostic imaging Plaque, Atherosclerotic - genetics Plaque, Atherosclerotic - pathology Risk Assessment Transcriptome |
| Title | A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/31504423 https://www.proquest.com/docview/2288723826 |
| Volume | 40 |
| WOSCitedRecordID | wos000501743300009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwELWgQhWXUqDAAq0Gqdeo-XDs-FhVVFy69LBIuUX-GG8XQVJlsyuVf8E_Zuykeyg9wCVH2_KMZp4zM-8xdmoLL13ueOJMahLKt2mihA-TXJqyvTEllj6KTcj5vKprdT3xbK8fKeGr4gw3fRB3Hr6f4c2vUoV4m5VV8OnF13oXdSsRZU6j9qAQVT2VJB9b4MEs21-QMqaWyxf_dahDdjAhSDgfTf6SPcH2Fdu_mmrkr9nvc2i7Lf6An7FPEmEShlgmjrxtiw567VbdEJJUDBlhLhlCH0fk-ITOQ2A_vm9RBa8HWMVfD7gGGx3KQuhIh9s-bBosC6F9fgk20CHo_g4uFqDb5Wqiwz5i3y4_Ly6-JJPwQmLpNTIkxkrkqTaUv12VZU54T0ETU1OgzZwX3CqCLUpIWXDLs8wbJbGqbGpCD4oqizdsr-1afMcAlc8x17rknLAC51qgJATpnZf0OBb5jJ2SLZrbkVqjGUviRbO73ma83hn7dG-shvw_FDV0i91m3eQ5hUnCHbmYsbejFXerFYR2ad_i_b9t8oE9JzikwqRhxj-yvaHf4DF7ZrfDat2fsKeyrug7v746ia73B4uB3Mk |
| linkProvider | Oxford University Press |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+novel+machine+learning-derived+radiotranscriptomic+signature+of+perivascular+fat+improves+cardiac+risk+prediction+using+coronary+CT+angiography&rft.jtitle=European+heart+journal&rft.au=Oikonomou%2C+Evangelos+K&rft.au=Williams%2C+Michelle+C&rft.au=Kotanidis%2C+Christos+P&rft.au=Desai%2C+Milind+Y&rft.date=2019-11-14&rft.pub=Oxford+University+Press&rft.issn=0195-668X&rft.eissn=1522-9645&rft.volume=40&rft.issue=43&rft.spage=3529&rft.epage=3543&rft_id=info:doi/10.1093%2Feurheartj%2Fehz592&rft.externalDocID=10.1093%2Feurheartj%2Fehz592 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0195-668X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0195-668X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0195-668X&client=summon |