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Abstract

Introduction

A significant proportion of ischemic strokes are caused by emboli from unstable atherosclerotic carotid artery plaques. Inflammation is a key feature of plaque instability. Positron emission tomography/computed tomography (PET/CT) with 2-deoxy-2-(¹⁸F)-fluoro-D-glucose (¹⁸F-FDG) is a promising technique to quantify plaque inflammation, but a consensus on the methodology has not been established. High inter-reader agreement is essential if ¹⁸F-FDG PET/CT is to be used as a clinical tool for the assessment of unstable plaques and stroke risk.

Methods

We assessed the inter-reader variability of different methods for quantification of ¹⁸F-FDG uptake in 43 patients with carotid artery stenosis ≥70%. Two independent readers delineated the plaque and collected maximum standardized uptake value (SUV_max) from all axial PET slices containing the atherosclerotic plaque.

Results

Uptake values with and without background correction were calculated and intraclass correlation coefficients were highest for uncorrected uptake values (0.97–0.98) followed by those background corrected by subtraction (0.89–0.94) and lowest for those background corrected by division (0.74–0.79).

Conclusion

Quantification methods without background correction have the highest inter-reader agreement for ¹⁸F-FDG PET of carotid artery plaque inflammation. The use of the single highest uptake value (max SUV_max) from the plaque will facilitate the method’s clinical utility in stroke prevention.

Keywords

carotid stenosis stroke cardiology primary and secondary stroke prevention imaging of the brain and arteries quantification method

Introduction

Ischemic strokes caused by thromboembolism from an unstable atherosclerotic plaque in the carotid artery can be prevented by carotid endarterectomy (CEA).^1–3 Patients are selected for CEA based on the degree of carotid artery stenosis and presence or absence of cerebral ischemic symptoms. In recent years it has become increasingly clear that the degree of stenosis alone is not the best predictor of stroke risk. This has led to the concept of the ‘unstable plaque’ describing carotid plaques that carry high risk of stroke irrespective of the degree of artery stenosis and increased focus on factors that destabilize the plaque. Inflammation plays a key role in the development of an unstable plaque.^4–6

Positron emission tomography (PET) imaging of atherosclerosis has been rapidly evolving since the first reports of 2-deoxy-2-(¹⁸F)-fluoro-D-glucose (¹⁸F-FDG) uptake localized to the inflammatory macrophage rich areas in carotid artery plaques.⁷ The goal of the imaging technique is to detect carotid plaques that are at high risk of rupture and therefore carry high risk of stroke. ¹⁸F-FDG PET for the detection of unstable plaques is not in clinical use,⁸ partly due to lack of feasible PET protocols and consensus regarding imaging procedure, method for ¹⁸F-FDG uptake quantification and assessment of stroke risk, although several recommendations exist.^9,10 PET is an imaging modality with limited anatomical information, and it might therefore be challenging to define the vessel-segment-of-interest. Computed tomography angiography (CTA) is often used together with ¹⁸F-FDG PET when assessing patients with carotid artery stenosis, but selection of the plaque area for uptake measurements varies.^11–13 A requirement for introducing a diagnostic method into clinical routine is high inter-reader agreement. Inter-reader agreement has been studied for a few selected uptake parameters with generalized vascular inflammation^14,15 and in patients with symptomatic carotid stenosis,^12,13 but to our knowledge no study has compared inter-reader agreement for different quantification methods.

The aim of this study was to assess inter-reader variability of different methods used for quantification of ¹⁸F-FDG uptake at PET/CT of carotid artery plaques.

Materials and methods

Study population

The study cohort consisted of forty-three patients with ultrasound-confirmed atherosclerosis with internal carotid artery stenosis ≥70% according to consensus criteria of the Society of Radiologists in Ultrasound.¹⁶ Patient characteristics are summarized in Table 1. There were 30 men (66 ± 9 years) and 13 women (67 ± 8 years) with a mean age of 66.2 years. The study protocol conformed with the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Norwegian Regional Committee for Medical and Health Research Ethics South-East A. Written informed consent was obtained from all patients prior to study inclusion.

Table 1.

Patient characteristics (n = 43).*

Age, years; mean ± SD	66.2 ± 8.4
Sex, male; n (%)	30 (69.8)
Blood glucose, mmol⋅L^–1; mean ± SD (range)	6.8 ± 2.2 (4.9 – 14.9)
Bodyweight, kg; mean ± SD (range)	82.4 ± 15 (55 – 110)
Body mass index, kg/m²; mean ± SD (range)	27.5 ± 4.5 (19.9 – 34.8)

^*The patient material is included in previously published studies.^18,19

¹⁸F-FDG PET/CT examination

After a minimum of six hours fasting the patients were injected with 5 MBq/kg ¹⁸F-FDG and blood glucose, weight, and height were recorded. After approximately 90 minutes a two-bed position PET/CT from the base of the skull to the aortic arch was performed with 15 minutes per bed position using a hybrid PET/CT scanner (Siemens Biograph 64, Siemens Medical Systems, Erlangen, Germany). The PET images were acquired with a 256 × 256 matrix and the images were reconstructed to two millimetre thick slices, with four iterations/eight subsets ordered subset expectation–maximization (OSEM) algorithm and Gaussian post-reconstruction filter with 3.5 mm full width half maximum (FWHM). In addition to a non-contrast CT for attenuation correction a CTA with contrast filling of the arteries (minimum 40 mL Iomeron (iodine 350 mg/mL; Bracco Imaging S.P.A, Milan, Italy) or Visipaque (iodine 320 mg/mL); GE Healthcare, Chicago, USA) was acquired immediately after the PET when still lying in the scanner for 16 of the 43 patients. For 24 patients CTA was performed at other radiologic departments. For three patients no CTA was available when the PET images were analysed.

Image analyses and ¹⁸F-FDG quantification

The images were assessed with Hybrid Viewer 2.0 software (Hermes Medical Solutions AB, Stockholm, Sweden). Two experienced nuclear medicine senior consultants independently evaluated the ¹⁸F-FDG PET/CT examinations. The two readers (R1 and R2) did not undergo any joint training before assessing the images, but they agreed on how to perform the analyses. The instructions were to use the CTA as guide for drawing the region of interests (ROIs) on the fused slices (PET and non-contrast CT). The plaque was defined as vessel wall thickening and a lumen contrast-filling defect on CTA.¹¹ The ROIs were drawn around the entire vessel wall and lumen on all plaque-containing axial PET slices (Figure 1). For patients without CTA available, the plaque was defined as vessel wall with calcification and fat deposits in the level of the carotid bifurcation. Uptake in structures close to the plaque (e.g. lymph nodes, paravertebral muscles or salivary glands) that could falsify the plaque uptake values were excluded from the ROI. The number of plaque-containing slices for each patient was recorded. The pixel values in the PET images were converted into SUV and normalized to lean body mass.¹⁷ SUV_max in all plaque containing ROIs were recorded. Background blood pool activity was obtained from four ROIs placed in the lumen of the jugular vein away from structures with ¹⁸F-FDG uptake but preferably in the same craniocaudal level as the plaque. The background was calculated as the mean of the SUV_mean in these four ROIs. Five different measures of ¹⁸F-FDG uptake were calculated, as previously described in detail:¹⁸

Max SUV_max = the single highest SUV

Mean SUV_max = the mean of all plaque SUV_max

Most Diseased Segment (MDS)3 = the mean SUV_max of the three contiguous slices centred on the slice with the highest SUV_max

MDS5 = the mean SUV_max of the five contiguous slices centred on the slice with the highest SUV_max

Mean SUV_max4 = the mean SUV_max of the four slices with highest SUV_max

Figure 1.

Region of interest. On each plaque-containing axial slice a region of interest (ROI) was drawn manually around the entire vessel wall including the plaque and the lumen. (a) (fused PET/non-contrast CT) and (b) (PET) show increased uptake (arrow) in the plaque in the right internal carotid artery. (c) shows how the plaque location on contrast enhanced CT (low attenuation plaque with thin contrast filled lumen in the centre) guides the actual drawing of the ROI (green dotted line) on the fused PET/non-contrast CT (d).

Blood background corrected values were calculated as the ¹⁸F-FDG uptake values divided by the mean blood pool activity (TBR) and subtraction of the blood pool activity from the ¹⁸F-FDG uptake values (corrected SUV (cSUV)).

Statistical analysis

The IBM SPSS Statistics software for Windows (version 25.0; IBM Corp., Armonk, USA) was used for data analyses. Groups of paired data were compared using the Wilcoxon signed rank test for non-normally distributed variables. Inter-reader agreement was calculated using intraclass correlation coefficients (ICC’s; model two-way random, type absolute agreement). All statistical results were considered significant when p < 0.05.

Results

The different ¹⁸F-FDG uptake values for the two readers are summarized in Table 2. Reader 2 identified significantly more slices as plaque containing (median; 10, range; 4–23) than reader 1 (median; 9, range; 3–18) (p = 0.001).

Table 2.

¹⁸F-FDG uptake values and intraclass correlation coefficients between the two readers (n = 43 patients).

Quantification method	¹⁸F-FDG uptake values
Quantification method	Reader 1	Reader 2	p	ICC
Max SUV_max	1.74 (1.18 – 2.66)	1.74 (1.20 – 2.66)	0.304	.979
Mean SUV_max	1.51 (1.11 – 2.28)	1.51 (1.06 – 2.15)	0.687	.973
MDS3	1.68 (1.17 – 2.51)	1.68 (1.19 – 2.51)	0.400	.978
MDS5	1.64 (1.15 – 2.32)	1.63 (1.17 – 2.45)	0.438	.972
Mean SUV_max4	1.68 (1.15 – 2.45)	1.68 (1.13 – 2.45)	0.060	.972
Background	0.87 (0.55 – 1.26)	0.89 (0.55 – 1.30)	0.245	.767
TBR max SUV_max	1.95 (1.34 – 3.07)	2.02 (1.34 – 2.68)	0.314	.792
TBR mean SUV_max	1.72 (1.16 – 2.59)	1.76 (1.25 – 2.37)	0.232	.741
TBR MDS3	1.87 (1.26 – 2,89)	1.97 (1.30 – 2.55)	0.296	.775
TBR MDS5	1.80 (1.22 – 2.79)	1.94 (1.24 – 2.53)	0.241	.769
TBR mean SUV_max4	1.81 (1.26 – 2.82)	1.93 (1.31 – 2.61)	0.358	.758
cSUV max SUV_max	0.83 (0.42 – 1.79)	0.87 (0.38 – 1.67)	0.837	.944
cSUV mean SUV_max	0.68 (0.20 – 1.28)	0.68 (0.28 – 1.19)	0.435	.893
cSUV MDS3	0.80 (0.33 – 1.64)	0.79 (0.34 – 1.51)	0.769	.931
cSUV MDS5	0.75 (0.28 – 1.45)	0.76 (0.27 – 1.45)	0.595	.916
cSUV mean SUV_max4	0.74 (0.32 – 1.58)	0.77 (0.35 – 1.45)	0.975	.919

Data are given as median (range). P-value from Wilcoxon signed ranks test. SUV, standardized uptake value; MDS, most diseased segment; TBR, target-to-background ratio; cSUV, background subtracted SUV; ICC, intraclass correlation coefficient.

There were no differences in ¹⁸F-FDG uptake between the two readers (Table 2). The ICC for the different ¹⁸F-FDG quantification methods was highest for uncorrected SUVs (0.97–0.98) followed by cSUVs (0.89–0.94) and TBRs (0.74–0.79), and 0.77 for the background blood pool (Table 2). The differences in the median for the uptake values between the readers ranged from 0.00 and 0.01 for the uncorrected SUVs to 0.04–0.14 for TBRs (0.14 for TBR MDS5). The difference for the background value was 0.02 (Table 2).

Figure 2 shows the differences in max SUV_max and mean SUV_max for individual patients for the two readers without background correction (a and b), and the corresponding values when the ¹⁸F-FDG uptake is corrected for background blood pool by division (TBR; 2(c) and (d)) and by subtraction (cSUV; 2(e) and (f)). The difference in venous background is shown in Figure 2(g). The difference between the readers is highest for the uptake values corrected for background blood pool by division (2(c) and (d)), and lowest for the uptake values without background correction (2(a) and (b)).

Figure 2.

Inter-reader difference for the ¹⁸F-FDG quantification methods. Difference between the readers (R2 minus R1, (y-axis)) for the included patients (x-axis). Max SUVmax (a), mean SUVmax (b), TBR max SUVmax (c), TBR mean SUVmax (d), cSUV max SUVmax (e), cSUV mean SUVmax (f), and venous background (g).

Discussion

In this study we found high inter-reader agreement between different methods for ¹⁸F-FDG uptake quantification of inflammation in high grade carotid artery stenosis. The inter-reader agreement was highest for the methods without background correction. Two studies in patients with carotid stenosis supports our finding that methods without correction for background blood activity have higher inter-reader agreement than background corrected values: Kwee et al.¹² reported an ICC of 0.61 for TBR mean SUV_max and 0.65 for TBR max SUV_max, and Marnane et al.¹³ found an ICC of 0.99 for mean SUV_max.

In our study the highest ICC was found for max SUV_max (0.98). For the methods without background blood pool correction only 12% of the max SUV_max and 14% of the mean SUV_max measurements differed with more than ±0.10 (Figure 2(a), (b)). Patient number 42 is an outlier with an inter-reader difference of 0.38. This is probably due to different delineations of the plaque ROIs as this patient had high uptake in neighbouring muscle (Figure 3). Reader 1 can have excluded more of the plaque ROIs to be sure to avoid spill-in activity than reader 2. The problem with spill-in from neighbouring structures is due to the relatively low spatial resolution of PET combined with unspecific uptake of ¹⁸F-FDG.

Figure 3.

Spill-in activity. Fused image of non-contrast CT and PET (a) and contrast enhanced CT (b) show a plaque in the level of the right carotid bifurcation with low uptake but with high uptake in nearby muscles. PET with normal intensity on the SUV scale (c) and PET with high intensity on the SUV scale (d) show that ¹⁸F-FDG uptake from nearby muscle activity influences the ROI around the plaque (inserted picture at 4 to 5 o'clock position).

For the background corrected values, the difference was larger with 40% of TBR max SUV_max and 30% of TBR mean SUV_max having a difference of ±0.25 or more (Figure 2(c), (d)). In our previous study exploring ¹⁸F-FDG-uptake in symptomatic versus asymptomatic patients¹⁹ the difference in median mean SUV_max between the groups was 0.32 (1.75 versus 1.43). In two studies using TBR max SUV_max as uptake parameter the difference was found to be 0.19 and 0.29.^20,21 Thus, methods with reader difference of 0.25 prohibit differentiation between symptomatic and asymptomatic patients.

We found an ICC for background blood pool activity of 0.77. This discordant assessment of background blood pool activity introduces variation in TBR and cSUVs due to methodology rather than biology. The background blood pool activity in our study was obtained from four ROIs within the lumen of the jugular vein preferably in the same craniocaudal level as the plaque. The vena jugularis has a small diameter and it was often challenging to draw reproducible ROIs within the vein that excluded contribution from neighbouring structures. In a ¹⁸F-FDG PET study of generalized vascular inflammation in which the background blood pool activity was obtained from eight ROIs in the jugular vein the ICC for TBR mean SUV_max of the carotid arteries was 0.94–0.96.¹⁴ This suggests that including data from more slices or from a larger vessel segment such as the vena cava superior or atria of the heart could have reduced the inter-reader variability of measuring the blood pool activity. In this study the two readers also had trained together by co-reading several pilot studies before they established an analysis protocol.¹⁴ This is optimal for research studies, but hard to accomplish in larger trials where the readers often are located in different departments.

There is a large amount of studies that quantifies the ¹⁸F-FDG uptake in the vessel wall of patients with suspected generalized vascular inflammation (atherosclerosis not necessarily confirmed by other imaging methods). Although our findings cannot automatically be generalized, one might question the need for background correction for these patients.

Reader 2 included significantly more plaque-containing slices than reader 1. This did not reduce the ICC of the ¹⁸F-FDG measurements, supporting that the plaque slices with the highest uptake values all were included in both readers plaque area and that the number of slices included in the plaque area has minimal influence on mean SUV_max. Our interpretation of this finding is that the plaque inflammation we can detect with ¹⁸F-FDG PET is homogeneously spread out, and also present in the extreme tails of the plaque. This was also one of our main findings when we explored associations between different ¹⁸F-FDG uptake parameters and plaque inflammation at histopathology.¹⁸ Furthermore, this is in accordance with the study results from Kwee et al.¹² who found a strong correlation between TBRs of ipsilateral symptomatic plaques and contralateral asymptomatic plaques and supports the hypothesis that plaque inflammation is systemic to some extent.

A strength of our study is a relatively large patient population with a wide range of uptake values (max SUV_max from 1.18 to 2.66) representing low to high plaque inflammatory activity confirmed by histology.¹⁸ A weakness of the study is that only patients with high-grade stenosis ≥70% were included. In a clinical setting, ¹⁸F-FDG PET will be used for decision making also in lower grade stenosis. However, a study by Marnane et al.¹³ did not indicate that also including a lower stenosis degree would worsen the inter-reader agreement. They found an ICC of 0.99 for mean SUV_max in symptomatic patients where the stenosis was 50 to 69% in 30 of 60 patients, and ≥70% in the rest. Another weakness is that no CTA was available for three patients. The lack of CTA could have affected the results but their uptake measures were in line with the values for the other 40 patients and there was also a high degree of concordance between the two readers.

In conclusion, our study confirms the reproducibility of quantification of ¹⁸F-FDG uptake in carotid artery plaques and supports the superiority of quantification methods that do not include blood pool background. The ICC was highest for max SUV_max (the single highest uptake value within the plaque) and thus, our suggestion is to further explore this parameter for atherosclerosis imaging.

Footnotes

Acknowledgements

The authors would like to thank the bioengineers and radiographers of The Department of Nuclear Medicine for their help in performing the study.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article:This work received funding from the South-Eastern Norway Regional Health Authority, grant number: 2009006.

Ethical approval

The study protocol was approved by the Norwegian Regional Committee for Medical and Health Research Ethics South-East A (approval number S-09233a).

Guarantor

Kjersti Johnsrud.

Contributorship

Kjersti Johnsrud took part in planning of the study, performed PET imaging, PET image reading, data analysis, statistical analysis, interpretation of data and wrote the manuscript. Therese Seierstad took part in planning of the study data analysis, statistical analysis, interpretation of data and revised and proof read the manuscript. David Russell formulated the research goal and aims and revised and proof read the manuscript. Mona-Elisabeth Revheim took part in planning of the study, performed PET image reading, data analysis, statistical analysis, interpretation of data and revised and proof read the manuscript.

ORCID iD

Kjersti Johnsrud

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Inter-reader agreement of 18 F-FDG PET/CT for the quantification of carotid artery plaque inflammation

Abstract

Introduction

Methods

Results

Conclusion

Keywords

Introduction

Materials and methods

Study population

18F-FDG PET/CT examination

Image analyses and 18F-FDG quantification

Statistical analysis

Results

Discussion

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

Ethical approval

Guarantor

Contributorship

ORCID iD

References

¹⁸F-FDG PET/CT examination

Image analyses and ¹⁸F-FDG quantification