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Tarkin J, Corovic A, Wall C, Nus M, Gopalan D, Huang Y, Imaz M, Zulcinski M, Reynolds G, Morgan AW, Jorgensen HF, Mallat Z, Peters JE, Rudd JHF, Mason JC. Somatostatin receptor PET/MR imaging of large vessel inflammation in active compared with inactive vasculitis and atherosclerosis. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Use of 18F-FDG PET in large vessel vasculitis (LVV) is limited by non-specific uptake due to arterial remodelling and/or atherosclerosis leading to diagnostic uncertainty.
Purpose
To investigate somatostatin receptor 2 (SST2) as a novel inflammation-specific PET imaging target in LVV.
Methods
In a prospective observational cohort study, we tested the ability of PET/MRI using two somatostatin receptor tracers (68Ga-DOTATATE and 18F-FET-βAG-TOCA) to differentiate active from inactive LVV, and aortic atherosclerosis in patients with recent myocardial infarction. Ex vivo mapping of the imaging target was performed using immunofluorescence microscopy, imaging mass cytometry, and bulk, single-cell and single-nuclei RNA sequencing of temporal artery biopsies from LVV patients.
Results
Sixty-one participants were included (LVV, n=27; myocardial infarction ≤2 weeks, n=25; control subjects with an oncological indication for imaging, n=9). LVV patients (mean age 58 [SD 16] years; 78% female; 63% active or grumbling disease) had giant cell arteritis (n=13), Takayasu arteritis (n=13), or unspecified LVV (n=1). Baseline index vessel SST2 PET maximum tissue-to-blood ratio (TBRmax) was 61.8% (95% CI 31.5–99.0%, p<0.0001) higher in patients with active/grumbling LVV than inactive LVV, and 34.6% (95% CI 15.1–57.6%, p=0.0002) higher than recent myocardial infarction (Fig. 1a–c; arrow: PET signal; arrowhead: aortic thickening; asterisk: aortic atherosclerosis), with good diagnostic accuracy (AUC ≥0.86, p<0.001 for both). None of the control subjects without LVV or MI had increased arterial SST2 PET signal (Fig. 1d).
Mean aortic TBRmax was strongly correlated with Indian Takayasu Clinical Activity Score (r=0.82 [95% CI 0.46–0.95], p=0.001) and maximum wall thickness on MRI (r=0.68 [95% CI 0.31–0.87], p=0.002). SST2 PET/MRI was generally consistent with 18F-FDG PET/CT in LVV patients with contemporaneous scans (Fig. 1a, b), but with very low background signal in the brain and heart allowing for unimpeded assessment of nearby coronary, myocardial, and intracranial artery involvement. On follow-up imaging after a mean 9.3 (SD 3.2) months, clinically effective treatment for LVV was associated with a 0.49 ±SEM 0.24 (p=0.04; 22.3%) reduction in SST2 PET TBRmax, with good scan-scan repeatability in inactive LVV patients with no change in treatment (ICC 0.86, 95% CI 0.04–0.99).
SST2 localised to macrophages, pericytes, and perivascular adipocytes in inflamed arterial specimens (Fig. 2; a: H&E; b: imaging mass cytometry; arrow: SST2/CD68 co-staining). SSTR2-expressing macrophages co-expressed pro-inflammatory markers (S100A8, S100A9). Specific SST2 radioligand binding was confirmed by autoradiography in LVV specimens.
Conclusion
This is the first study to examine SST2 PET/MRI in LVV and to provide histological and gene expression data for validation. Here we show this novel approach holds major promise for diagnosis and therapeutic monitoring in LVV.
Funding Acknowledgement
Type of funding sources: Foundation. Main funding source(s): Wellcome Trust; Imperial NIHR Biomedical Research Centre
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Affiliation(s)
- J Tarkin
- University of Cambridge , Cambridge , United Kingdom
| | - A Corovic
- University of Cambridge , Cambridge , United Kingdom
| | - C Wall
- University of Cambridge , Cambridge , United Kingdom
| | - M Nus
- University of Cambridge , Cambridge , United Kingdom
| | - D Gopalan
- Imperial College Healthcare NHS Trust , London , United Kingdom
| | - Y Huang
- University of Cambridge , Cambridge , United Kingdom
| | - M Imaz
- University of Cambridge , Cambridge , United Kingdom
| | - M Zulcinski
- University of Leeds , Leeds , United Kingdom
| | - G Reynolds
- Newcastle University , Newcastle-Upon-Tyne , United Kingdom
| | - A W Morgan
- University of Leeds , Leeds , United Kingdom
| | - H F Jorgensen
- University of Cambridge , Cambridge , United Kingdom
| | - Z Mallat
- University of Cambridge , Cambridge , United Kingdom
| | - J E Peters
- Imperial College London , London , United Kingdom
| | - J H F Rudd
- University of Cambridge , Cambridge , United Kingdom
| | - J C Mason
- Imperial College London , London , United Kingdom
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Le E, Tarkin J, Evans N, Chowdhury M, Rudd J. 875 Using Stress Testing to Identify Vulnerabilities in Artificial Intelligence Models for the Identification of Culprit Carotid Lesions in Cerebrovascular Events. Br J Surg 2021. [DOI: 10.1093/bjs/znab259.1123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Abstract
Introduction
Carotid atherosclerosis is a major risk factor for ischaemic stroke, a leading cause of death. Carotid CT angiography (CTA) is commonly performed following a stroke or transient ischaemic attack (TIA) to help guide patient management in secondary prevention of stroke. Deep learning algorithms can help extract greater information from scans.
Method
The dataset comprised CTA scans from 40 culprit and 40 non-culprit carotid arteries of patients with recent stroke/TIA, and 40 carotid arteries of asymptomatic patients without previous stroke/TIA. A 3D convolutional neural network was trained to classify carotid artery type. Each input comprised 14 axial CTA carotid patches (centred around the carotid artery) concatenated together to form a 3D volume (capturing ∼3cm of artery). 75% of the dataset was used for training and 25% for internal validation. Following training, computer vision operations were applied to input images to assess their impact on the model’s classification decisions.
Results
The model achieved 100% accuracy on the training set and 67% on the internal validation set. However, after subjecting input images to image operations, vulnerabilities in the deep learning model were revealed, even when using input images from the training set. For example, using a Gaussian blur filter with sigma 1.0 was sufficient to change classification decisions, as was horizontally flipping the image.
Conclusions
Deep learning has exceptional capabilities for learning, however the risk with such high-capacity models is failure to learn relevant features from the data. Stress testing provides a viable method to further evaluate deep learning models before clinical deployment.
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Affiliation(s)
- E Le
- Department of Medicine, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - J Tarkin
- Department of Medicine, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - N Evans
- Department of Medicine, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - M Chowdhury
- Division of Vascular and Endovascular Surgery, Addenbrooke's Hospital, Cambridge, United Kingdom
- Department of Medicine, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - J Rudd
- Department of Medicine, Addenbrooke's Hospital, Cambridge, United Kingdom
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Wall C, Huang Y, Uy C, Le E, Tombetti E, Gopalan D, Manavaki R, Dweck M, Ariff B, Bennett M, Slomka P, Dey D, Mason J, Rudd J, Tarkin J. Pericoronary adipose tissue density is associated with clinical disease activity in Takayasu arteritis and coronary arterial inflammation measured by 68Ga-DOTATATE PET in atherosclerosis. Eur Heart J 2020. [DOI: 10.1093/ehjci/ehaa946.0182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Coronary artery disease (CAD) is an under-recognized complication of intense arterial inflammation in Takayasu arteritis (TAK). While pericoronary adipose tissue (PCAT) density is associated with arterial inflammation in CAD patients, this relationship has not previously been studied in TAK patients, nor directly compared with coronary arterial inflammation measured by 68Ga-DOTATATE positron emission tomography (PET).
Purpose
To compare PCAT density with clinical, biochemical and molecular imaging markers of inflammation in TAK and CAD patients.
Methods
PCAT density was quantified from computed tomography coronary angiography (CTCA) around each of the 17 coronary segments in patients with: (1) TAK and CAD, (2) atherosclerotic CAD, and (3) age and gender-matched healthy controls, using semi-automated software (Autoplaque). In TAK patients, PCAT density was compared to the Indian Takayasu Clinical Activity Score (ITAS) and high-sensitivity C-reactive protein (CRP). In CAD patients, PCAT density was compared to local arterial inflammation measured by coronary motion-frozen 68Ga-DOTATATE PET using image registration software (FusionQuant), and systemic (aortic) inflammation using 18F-fluorodeoxyglucose (FDG) PET. Data was acquired either during routine clinical care or prior research that established 68Ga-DOTATATE as an experimental marker of arterial inflammation that binds macrophage somatostatin receptor-2 in atherosclerotic plaques (NCT02021188).
Results
60 patients were included (TAK, n=20; CAD, n=20; healthy, n=20). Non-calcified plaque burden (TAK: 95.2%; CAD: 90.4%, p<0.0001) and CRP (TAK: 25.2 ±SD 16.1 mg/L; CAD: 2.5 ±SD 1.7 mg/L, p=0.04) were greater in TAK than CAD patients.
PCAT density varied significantly among the three groups (median [IQR] TAK: −72.9 [−81.2 to -66.1] Hounsfield unit [HU]; CAD: −79.9 [−88.0 to −72.2]; healthy: −83.8 [−90.1 to −75.8] HU, p<0.0001). Figure: box-plot showing the distribution of PCAT values by group, with corresponding representative multiplanar reconstructed and cross-sectional CTCA images with surrounding PCAT density displayed by color table in left anterior descending arteries.
PCAT density was significantly associated with ITAS (r=0.61, p=0.004) and CRP (r=0.43, p=0.03) in TAK patients, and coronary 68Ga-DOTATATE maximum tissue-to-blood ratio (r=0.31, p<0.001) in CAD patients. PCAT density was not associated with aortic 18F-FDG uptake in CAD patients, nor subcutaneous (pre-sternal) adipose tissue density in either disease group. No significant patient-level confounders were identified using linear mixed-effects regression modelling.
Conclusion
PCAT density measured by CTCA is greater in TAK than CAD patients, and is associated with clinical and biochemical markers of disease activity in TAK, and coronary arterial inflammation measured by 68Ga-DOTATATE PET in CAD. PCAT could be a useful, easy to measure marker of coronary inflammation and disease activity in both TAK and CAD.
PCAT density is greater in TAK than CAD
Funding Acknowledgement
Type of funding source: Foundation. Main funding source(s): Wellcome Trust
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Affiliation(s)
- C Wall
- University of Cambridge, Cambridge, United Kingdom
| | - Y Huang
- University of Cambridge, Cambridge, United Kingdom
| | - C Uy
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - E Le
- University of Cambridge, Cambridge, United Kingdom
| | - E Tombetti
- University Vita-Salute San Raffaele, Milan, Italy
| | - D Gopalan
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - R Manavaki
- University of Cambridge, Cambridge, United Kingdom
| | - M Dweck
- University of Edinburgh, Edinburgh, United Kingdom
| | - B Ariff
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - M Bennett
- University of Cambridge, Cambridge, United Kingdom
| | - P Slomka
- Cedars-Sinai Medical Center, Los Angeles, United States of America
| | - D Dey
- Cedars-Sinai Medical Center, Los Angeles, United States of America
| | - J Mason
- Imperial College London, London, United Kingdom
| | - J Rudd
- University of Cambridge, Cambridge, United Kingdom
| | - J Tarkin
- University of Cambridge, Cambridge, United Kingdom
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Tarkin J, Nijjer S, Sen S, Petraco R, Mayet J, Echavarria Pinto M, Redwood S, Francis D, Escaned J, Davies J. The haemodynamic response to intravenous adenosine and its impact on fractional flow reserve: results of the AFFECTS (Adenosine For the Functional assEssment of Coronary sTenosis Severity) study. Eur Heart J 2013. [DOI: 10.1093/eurheartj/eht309.2868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Kaneria S, Tarkin J, Williams G, Bain G, Quigley M. The CT halo sign: A rare manifestation of squamous cell carcinoma of the lung. Clin Radiol 2012; 67:613-5. [DOI: 10.1016/j.crad.2011.11.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2011] [Revised: 11/13/2011] [Accepted: 11/17/2011] [Indexed: 11/16/2022]
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Sen S, Escaned J, Malik I, Mikhail G, Foale R, Mila R, Tarkin J, Petraco R, Broyd C, Jabbour R, Sethi A, Baker C, Bellamy M, Al-Bustami M, Hackett D, Khan M, Lefroy D, Parker K, Hughes A, Francis D, Di Mario C, Mayet J, Davies J. 019 Development and validation of a novel pressure-only intra-coronary index of coronary stenosis severity. Heart 2012. [DOI: 10.1136/heartjnl-2012-301877b.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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