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For: Zhang Y, Cui B, Yang H, Ma J, Yang Y, Yang B, Ma Y, Jiao L, Li X, Lu J. Morphological feature and mapping inflammation in classified carotid plaques in symptomatic and asymptomatic patients: A hybrid ¹⁸F-FDG PET/MR study. Front Neurosci 2023;17:1144248. [PMID: 37025371 PMCID: PMC10070967 DOI: 10.3389/fnins.2023.1144248] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 03/06/2023] [Indexed: 04/08/2023] Open

For:	Zhang Y, Cui B, Yang H, Ma J, Yang Y, Yang B, Ma Y, Jiao L, Li X, Lu J. Morphological feature and mapping inflammation in classified carotid plaques in symptomatic and asymptomatic patients: A hybrid ¹⁸F-FDG PET/MR study. Front Neurosci 2023;17:1144248. [PMID: 37025371 PMCID: PMC10070967 DOI: 10.3389/fnins.2023.1144248] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 03/06/2023] [Indexed: 04/08/2023] Open

Number

Cited by Other Article(s)

Kashiwazaki D, Yamamoto S, Akioka N, Hori E, Noguchi K, Kuroda S. Association between Pericarotid Fat Density and Positive Remodeling in Patients with Carotid Artery Stenosis. J Clin Med 2024;13:3892. [PMID: 38999456 DOI: 10.3390/jcm13133892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/29/2024] [Accepted: 06/30/2024] [Indexed: 07/14/2024] Open

Abstract

Background/Objectives: The underlying mechanism of the potential involvement of inflammatory crosstalk between pericarotid fat and vascular layers in atherosclerosis pathogenesis is unclear. We investigated the association between pericarotid fat density and positive remodeling and inflammatory markers in carotid stenosis. We hypothesized that pericarotid fat density might serve as a marker of plaque inflammation in a clinical setting. Methods: We evaluated the stenosis degree and pericarotid fat density in 258 patients with carotid plaques. Plaque composition was examined, and the correlation between pericarotid fat density and expansive remodeling was investigated. Pearson's product-moment correlation coefficient was used to examine the relationship between pericarotid fat density and the expansive remodeling ratio. We also evaluated the relationship of pericarotid fat density with plaque composition, degree of stenosis, and macrophage and microvessel counts by. The subgroup analysis compared these factors between symptomatic mild carotid stenosis. Results: The pericarotid fat density was -63.0 ± 11.1 HU. The carotid fat densities were -56.8 ± 10.4 HU in symptomatic and -69.2 ± 11.4 HU in asymptomatic lesions. The pericarotid fat density values in intraplaque hemorrhage, lipid-rich necrotic core, and fibrous plaque were -51.6 ± 10.4, -59.4 ± 12.8, and -74.2 ± 8.4 HU, respectively. Therefore, the expansive remodeling ratio was 1.64 ± 0.4. Carotid fat density and expansive remodeling ratio were correlated. Immunohistochemistry showed high macrophage and microvessel levels (143.5 ± 61.3/field and 121.2 ± 27.7/field, respectively). In symptomatic mild carotid stenosis, pericarotid fat density was correlated with other inflammatory factors. The pericarotid fat density and expansive remodeling ratio (2.08 ± 0.21) were high in mild stenosis (-50.1 ± 8.4 HU). Conclusions: Pericarotid fat and intraplaque components were well correlated. Carotid fat density may be a marker of plaque inflammation in carotid plaques.

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Yu F, Zhang Y, Sun H, Li X, Shan Y, Zheng C, Cui B, Li J, Yang Y, Yang B, Ma Y, Wang Y, Jiao L, Li X, Lu J. In Vivo Classification and Characterization of Carotid Atherosclerotic Lesions with Integrated ¹⁸F-FDG PET/MRI. Diagnostics (Basel) 2024;14:1006. [PMID: 38786304 PMCID: PMC11120206 DOI: 10.3390/diagnostics14101006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/26/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024] Open

Abstract

BACKGROUND

The aim of this study was to exploit integrated PET/MRI to simultaneously evaluate the morphological, component, and metabolic features of advanced atherosclerotic plaques and explore their incremental value.

METHODS

In this observational prospective cohort study, patients with advanced plaque in the carotid artery underwent 18F-FDG PET/MRI. Plaque morphological features were measured, and plaque component features were determined via MRI according to AHA lesion-types. Maximum standardized uptake values (SUVmax) and tissue to background ratio (TBR) on PET were calculated. Area under the receiver-operating characteristic curve (AUC) and net reclassification improvement (NRI) were used to compare the incremental contribution of FDG uptake when added to AHA lesion-types for symptomatic plaque classification.

RESULTS

A total of 280 patients with advanced plaque in the carotid artery were recruited. A total of 402 plaques were confirmed, and 87 of 402 (21.6%) were symptomatic plaques. 18F-FDG PET/MRI was performed a mean of 38 days (range 1-90) after the symptom. Increased stenosis degree (61.5% vs. 50.0%, p < 0.001) and TBR (2.96 vs. 2.32, p < 0.001) were observed in symptomatic plaques compared with asymptomatic plaques. The performance of the combined model (AHA lesion type VI + stenosis degree + TBR) for predicting symptomatic plaques was the best among all models (AUC = 0.789). The improvement of the combined model (AHA lesion type VII + stenosis degree + TBR) over AHA lesion type VII model for predicting symptomatic plaques was the highest (AUC = 0.757/0.454, combined model/AHA lesion type VII model), and the NRI was 50.7%.

CONCLUSIONS

Integrated PET/MRI could simultaneously evaluate the morphological component and inflammation features of advanced atherosclerotic plaques and provide supplementary optimization information over AHA lesion-types for identifying vulnerable plaques in atherosclerosis subjects to achieve further stratification of stroke risk.

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Affiliation(s)

Fan Yu Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (F.Y.); (Y.Z.); (H.S.); (X.L.); (Y.S.); (C.Z.); (B.C.); (J.L.) Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, China
Yue Zhang Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (F.Y.); (Y.Z.); (H.S.); (X.L.); (Y.S.); (C.Z.); (B.C.); (J.L.) Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, China
Heyu Sun Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (F.Y.); (Y.Z.); (H.S.); (X.L.); (Y.S.); (C.Z.); (B.C.); (J.L.) Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, China
Xiaoran Li Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (F.Y.); (Y.Z.); (H.S.); (X.L.); (Y.S.); (C.Z.); (B.C.); (J.L.) Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, China
Yi Shan Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (F.Y.); (Y.Z.); (H.S.); (X.L.); (Y.S.); (C.Z.); (B.C.); (J.L.) Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, China
Chong Zheng Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (F.Y.); (Y.Z.); (H.S.); (X.L.); (Y.S.); (C.Z.); (B.C.); (J.L.) Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, China
Bixiao Cui Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (F.Y.); (Y.Z.); (H.S.); (X.L.); (Y.S.); (C.Z.); (B.C.); (J.L.) Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, China
Jing Li Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (F.Y.); (Y.Z.); (H.S.); (X.L.); (Y.S.); (C.Z.); (B.C.); (J.L.) Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, China
Yang Yang Beijing United Imaging Research Institute of Intelligent Imaging, Beijing 100094, China;
Bin Yang Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (B.Y.); (Y.M.); (Y.W.); (L.J.) China International Neuroscience Institute (China-INI), Beijing 100053, China Department of Interventional Neuroradiology, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China
Yan Ma Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (B.Y.); (Y.M.); (Y.W.); (L.J.) China International Neuroscience Institute (China-INI), Beijing 100053, China Department of Interventional Neuroradiology, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China
Yabing Wang Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (B.Y.); (Y.M.); (Y.W.); (L.J.) China International Neuroscience Institute (China-INI), Beijing 100053, China Department of Interventional Neuroradiology, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China
Liqun Jiao Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (B.Y.); (Y.M.); (Y.W.); (L.J.) China International Neuroscience Institute (China-INI), Beijing 100053, China Department of Interventional Neuroradiology, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China
Xiang Li Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Vienna General Hospital, Medical University of Vienna, 1090 Vienna, Austria Department of Nuclear Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
Jie Lu Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Changchun Street, No. 45, Beijing 100053, China; (F.Y.); (Y.Z.); (H.S.); (X.L.); (Y.S.); (C.Z.); (B.C.); (J.L.) Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing 100053, China

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Xu T, Wang L, Chang N, Li S, Jiao B, Zhang S, Wang X. CT-Diagnosed Non-Alcoholic Fatty Liver Disease as a Risk Predictor of Symptomatic Carotid Plaque and Cerebrovascular Symptoms. Angiology 2024:33197241227501. [PMID: 38232089 DOI: 10.1177/00033197241227501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]