Cargando…
Detection of Atherosclerotic Inflammation by (68)Ga-DOTATATE PET Compared to [(18)F]FDG PET Imaging
BACKGROUND: Inflammation drives atherosclerotic plaque rupture. Although inflammation can be measured using fluorine-18-labeled fluorodeoxyglucose positron emission tomography ([(18)F]FDG PET), [(18)F]FDG lacks cell specificity, and coronary imaging is unreliable because of myocardial spillover. OBJ...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Biomedical
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5381358/ https://www.ncbi.nlm.nih.gov/pubmed/28385306 http://dx.doi.org/10.1016/j.jacc.2017.01.060 |
_version_ | 1782519924941389824 |
---|---|
author | Tarkin, Jason M. Joshi, Francis R. Evans, Nicholas R. Chowdhury, Mohammed M. Figg, Nichola L. Shah, Aarti V. Starks, Lakshi T. Martin-Garrido, Abel Manavaki, Roido Yu, Emma Kuc, Rhoda E. Grassi, Luigi Kreuzhuber, Roman Kostadima, Myrto A. Frontini, Mattia Kirkpatrick, Peter J. Coughlin, Patrick A. Gopalan, Deepa Fryer, Tim D. Buscombe, John R. Groves, Ashley M. Ouwehand, Willem H. Bennett, Martin R. Warburton, Elizabeth A. Davenport, Anthony P. Rudd, James H.F. |
author_facet | Tarkin, Jason M. Joshi, Francis R. Evans, Nicholas R. Chowdhury, Mohammed M. Figg, Nichola L. Shah, Aarti V. Starks, Lakshi T. Martin-Garrido, Abel Manavaki, Roido Yu, Emma Kuc, Rhoda E. Grassi, Luigi Kreuzhuber, Roman Kostadima, Myrto A. Frontini, Mattia Kirkpatrick, Peter J. Coughlin, Patrick A. Gopalan, Deepa Fryer, Tim D. Buscombe, John R. Groves, Ashley M. Ouwehand, Willem H. Bennett, Martin R. Warburton, Elizabeth A. Davenport, Anthony P. Rudd, James H.F. |
author_sort | Tarkin, Jason M. |
collection | PubMed |
description | BACKGROUND: Inflammation drives atherosclerotic plaque rupture. Although inflammation can be measured using fluorine-18-labeled fluorodeoxyglucose positron emission tomography ([(18)F]FDG PET), [(18)F]FDG lacks cell specificity, and coronary imaging is unreliable because of myocardial spillover. OBJECTIVES: This study tested the efficacy of gallium-68-labeled DOTATATE ((68)Ga-DOTATATE), a somatostatin receptor subtype-2 (SST(2))-binding PET tracer, for imaging atherosclerotic inflammation. METHODS: We confirmed (68)Ga-DOTATATE binding in macrophages and excised carotid plaques. (68)Ga-DOTATATE PET imaging was compared to [(18)F]FDG PET imaging in 42 patients with atherosclerosis. RESULTS: Target SSTR2 gene expression occurred exclusively in “proinflammatory” M1 macrophages, specific (68)Ga-DOTATATE ligand binding to SST(2) receptors occurred in CD68-positive macrophage-rich carotid plaque regions, and carotid SSTR2 mRNA was highly correlated with in vivo (68)Ga-DOTATATE PET signals (r = 0.89; 95% confidence interval [CI]: 0.28 to 0.99; p = 0.02). (68)Ga-DOTATATE mean of maximum tissue-to-blood ratios (mTBR(max)) correctly identified culprit versus nonculprit arteries in patients with acute coronary syndrome (median difference: 0.69; interquartile range [IQR]: 0.22 to 1.15; p = 0.008) and transient ischemic attack/stroke (median difference: 0.13; IQR: 0.07 to 0.32; p = 0.003). (68)Ga-DOTATATE mTBR(max) predicted high-risk coronary computed tomography features (receiver operating characteristics area under the curve [ROC AUC]: 0.86; 95% CI: 0.80 to 0.92; p < 0.0001), and correlated with Framingham risk score (r = 0.53; 95% CI: 0.32 to 0.69; p <0.0001) and [(18)F]FDG uptake (r = 0.73; 95% CI: 0.64 to 0.81; p < 0.0001). [(18)F]FDG mTBR(max) differentiated culprit from nonculprit carotid lesions (median difference: 0.12; IQR: 0.0 to 0.23; p = 0.008) and high-risk from lower-risk coronary arteries (ROC AUC: 0.76; 95% CI: 0.62 to 0.91; p = 0.002); however, myocardial [(18)F]FDG spillover rendered coronary [(18)F]FDG scans uninterpretable in 27 patients (64%). Coronary (68)Ga-DOTATATE PET scans were readable in all patients. CONCLUSIONS: We validated (68)Ga-DOTATATE PET as a novel marker of atherosclerotic inflammation and confirmed that (68)Ga-DOTATATE offers superior coronary imaging, excellent macrophage specificity, and better power to discriminate high-risk versus low-risk coronary lesions than [(18)F]FDG. (Vascular Inflammation Imaging Using Somatostatin Receptor Positron Emission Tomography [VISION]; NCT02021188) |
format | Online Article Text |
id | pubmed-5381358 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Elsevier Biomedical |
record_format | MEDLINE/PubMed |
spelling | pubmed-53813582017-04-12 Detection of Atherosclerotic Inflammation by (68)Ga-DOTATATE PET Compared to [(18)F]FDG PET Imaging Tarkin, Jason M. Joshi, Francis R. Evans, Nicholas R. Chowdhury, Mohammed M. Figg, Nichola L. Shah, Aarti V. Starks, Lakshi T. Martin-Garrido, Abel Manavaki, Roido Yu, Emma Kuc, Rhoda E. Grassi, Luigi Kreuzhuber, Roman Kostadima, Myrto A. Frontini, Mattia Kirkpatrick, Peter J. Coughlin, Patrick A. Gopalan, Deepa Fryer, Tim D. Buscombe, John R. Groves, Ashley M. Ouwehand, Willem H. Bennett, Martin R. Warburton, Elizabeth A. Davenport, Anthony P. Rudd, James H.F. J Am Coll Cardiol Original Investigation BACKGROUND: Inflammation drives atherosclerotic plaque rupture. Although inflammation can be measured using fluorine-18-labeled fluorodeoxyglucose positron emission tomography ([(18)F]FDG PET), [(18)F]FDG lacks cell specificity, and coronary imaging is unreliable because of myocardial spillover. OBJECTIVES: This study tested the efficacy of gallium-68-labeled DOTATATE ((68)Ga-DOTATATE), a somatostatin receptor subtype-2 (SST(2))-binding PET tracer, for imaging atherosclerotic inflammation. METHODS: We confirmed (68)Ga-DOTATATE binding in macrophages and excised carotid plaques. (68)Ga-DOTATATE PET imaging was compared to [(18)F]FDG PET imaging in 42 patients with atherosclerosis. RESULTS: Target SSTR2 gene expression occurred exclusively in “proinflammatory” M1 macrophages, specific (68)Ga-DOTATATE ligand binding to SST(2) receptors occurred in CD68-positive macrophage-rich carotid plaque regions, and carotid SSTR2 mRNA was highly correlated with in vivo (68)Ga-DOTATATE PET signals (r = 0.89; 95% confidence interval [CI]: 0.28 to 0.99; p = 0.02). (68)Ga-DOTATATE mean of maximum tissue-to-blood ratios (mTBR(max)) correctly identified culprit versus nonculprit arteries in patients with acute coronary syndrome (median difference: 0.69; interquartile range [IQR]: 0.22 to 1.15; p = 0.008) and transient ischemic attack/stroke (median difference: 0.13; IQR: 0.07 to 0.32; p = 0.003). (68)Ga-DOTATATE mTBR(max) predicted high-risk coronary computed tomography features (receiver operating characteristics area under the curve [ROC AUC]: 0.86; 95% CI: 0.80 to 0.92; p < 0.0001), and correlated with Framingham risk score (r = 0.53; 95% CI: 0.32 to 0.69; p <0.0001) and [(18)F]FDG uptake (r = 0.73; 95% CI: 0.64 to 0.81; p < 0.0001). [(18)F]FDG mTBR(max) differentiated culprit from nonculprit carotid lesions (median difference: 0.12; IQR: 0.0 to 0.23; p = 0.008) and high-risk from lower-risk coronary arteries (ROC AUC: 0.76; 95% CI: 0.62 to 0.91; p = 0.002); however, myocardial [(18)F]FDG spillover rendered coronary [(18)F]FDG scans uninterpretable in 27 patients (64%). Coronary (68)Ga-DOTATATE PET scans were readable in all patients. CONCLUSIONS: We validated (68)Ga-DOTATATE PET as a novel marker of atherosclerotic inflammation and confirmed that (68)Ga-DOTATATE offers superior coronary imaging, excellent macrophage specificity, and better power to discriminate high-risk versus low-risk coronary lesions than [(18)F]FDG. (Vascular Inflammation Imaging Using Somatostatin Receptor Positron Emission Tomography [VISION]; NCT02021188) Elsevier Biomedical 2017-04-11 /pmc/articles/PMC5381358/ /pubmed/28385306 http://dx.doi.org/10.1016/j.jacc.2017.01.060 Text en © 2017 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Original Investigation Tarkin, Jason M. Joshi, Francis R. Evans, Nicholas R. Chowdhury, Mohammed M. Figg, Nichola L. Shah, Aarti V. Starks, Lakshi T. Martin-Garrido, Abel Manavaki, Roido Yu, Emma Kuc, Rhoda E. Grassi, Luigi Kreuzhuber, Roman Kostadima, Myrto A. Frontini, Mattia Kirkpatrick, Peter J. Coughlin, Patrick A. Gopalan, Deepa Fryer, Tim D. Buscombe, John R. Groves, Ashley M. Ouwehand, Willem H. Bennett, Martin R. Warburton, Elizabeth A. Davenport, Anthony P. Rudd, James H.F. Detection of Atherosclerotic Inflammation by (68)Ga-DOTATATE PET Compared to [(18)F]FDG PET Imaging |
title | Detection of Atherosclerotic Inflammation by (68)Ga-DOTATATE PET Compared to [(18)F]FDG PET Imaging |
title_full | Detection of Atherosclerotic Inflammation by (68)Ga-DOTATATE PET Compared to [(18)F]FDG PET Imaging |
title_fullStr | Detection of Atherosclerotic Inflammation by (68)Ga-DOTATATE PET Compared to [(18)F]FDG PET Imaging |
title_full_unstemmed | Detection of Atherosclerotic Inflammation by (68)Ga-DOTATATE PET Compared to [(18)F]FDG PET Imaging |
title_short | Detection of Atherosclerotic Inflammation by (68)Ga-DOTATATE PET Compared to [(18)F]FDG PET Imaging |
title_sort | detection of atherosclerotic inflammation by (68)ga-dotatate pet compared to [(18)f]fdg pet imaging |
topic | Original Investigation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5381358/ https://www.ncbi.nlm.nih.gov/pubmed/28385306 http://dx.doi.org/10.1016/j.jacc.2017.01.060 |
work_keys_str_mv | AT tarkinjasonm detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT joshifrancisr detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT evansnicholasr detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT chowdhurymohammedm detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT figgnicholal detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT shahaartiv detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT starkslakshit detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT martingarridoabel detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT manavakiroido detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT yuemma detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT kucrhodae detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT grassiluigi detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT kreuzhuberroman detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT kostadimamyrtoa detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT frontinimattia detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT kirkpatrickpeterj detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT coughlinpatricka detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT gopalandeepa detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT fryertimd detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT buscombejohnr detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT grovesashleym detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT ouwehandwillemh detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT bennettmartinr detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT warburtonelizabetha detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT davenportanthonyp detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging AT ruddjameshf detectionofatheroscleroticinflammationby68gadotatatepetcomparedto18ffdgpetimaging |