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...
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| Published in: | European heart journal Vol. 40; no. 43; pp. 3529 - 3543 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
Oxford University Press
14.11.2019
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| Subjects: | |
| ISSN: | 0195-668X, 1522-9645, 1522-9645 |
| Online Access: | Get full text |
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| 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. |
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| 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 |
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| Keywords | Adipose tissue Computed tomography Coronary artery disease Machine learning Risk stratification Radiomics |
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| 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. |
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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... |
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| 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 |
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