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Article

Peptide Receptor Radionuclide Therapy Using 90Y- and 177Lu-DOTATATE Modulating Atherosclerotic Plaque Inflammation: Longitudinal Monitoring by 68Ga-DOTATATE Positron Emissions Tomography/Computer Tomography

by
German Rubinstein
1,2,
Harun Ilhan
1,3,
Peter Bartenstein
1,
Sebastian Lehner
1,
Marcus Hacker
4,
Andrei Todica
1,3,
Mathias Johannes Zacherl
1,† and
Maximilian Fischer
5,6,*,†
1
Department of Nuclear Medicine, LMU University Hospital, LMU Munich, 81377 Munich, Germany
2
Department of Medicine IV, LMU University Hospital, LMU Munich, 80336 Munich, Germany
3
DIE RADIOLOGIE, 80331 Munich, Germany
4
Division of Nuclear Medicine, Medical University of Vienna, 1090 Vienna, Austria
5
Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig-Maximilians-Universität, Marchioninistrasse 15, 81377 Munich, Germany
6
DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, 80802 Munich, Germany
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diagnostics 2024, 14(22), 2486; https://doi.org/10.3390/diagnostics14222486
Submission received: 14 October 2024 / Revised: 2 November 2024 / Accepted: 6 November 2024 / Published: 7 November 2024
(This article belongs to the Section Medical Imaging and Theranostics)
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Versions Notes

Abstract

:
Background: Atherosclerosis and its sequels, such as coronary artery disease and cerebrovascular stroke, still represent global health burdens. The pathogenesis of atherosclerosis consists of growing calcified plaques in the arterial wall and is accompanied by inflammatory processes, which are not entirely understood. This study aims to evaluate the effect of peptide receptor radionuclide therapy (PRRT) using 90Y- and 177Lu-DOTATATE on atherosclerotic plaque inflammation. Methods: Atherosclerotic plaques in 57 cancer patients receiving PRRT using 90Y- and 177Lu-DOTATATE were longitudinally monitored by 68Ga-DOTATATE PET/CT. The target-to-background ratio (TBR) and overall vessel uptake (OVU) were measured in eight distinct arterial regions (ascending aorta, aortic arch, descending aorta, abdominal aorta, both iliac arteries, and both carotid arteries) to monitor calcified plaques. Results: PET/CT analysis shows a positive correlation between calcified plaque scores and the 68Ga-DOTATATE overall vessel uptake (OVU) in cancer patients. After PRRT, an initially high OVU was observed to decrease in the therapy group compared to the control group. An excellent correlation could be shown for each target-to-background ratio (TBR) to the OVU, especially the ascending aorta. Conclusions: The ascending aorta could present a future reference for estimating generalized atherosclerotic inflammatory processes. PRRT might represent a therapeutic approach to modulating atherosclerotic plaques.

1. Introduction

Atherosclerosis and its sequels, including aorta disease, coronary artery disease, and peripheral artery disease, are still a significant burden in western society [1].
In atherosclerosis, there is an interplay of inflammation, necrosis, fibrosis, and calcification [2]. Recently, inflammation has been spotlighted as a dominant player in atherosclerosis progression and, therefore, the clinical manifestation of the otherwise silent disease [3]. Risk factors such as hypercholesterinemia hypertension, tobacco use, and diabetes contribute to the progression of atherosclerotic plaques [3].
The search for the best modality to predict which atherosclerotic lesions are prone to rupture is still ongoing. Historically, angiography, computer tomography (CT), and magnetic resonance imaging (MRI) are based on the basic morphological aspects, providing information about the degree of stenosis and, therefore, the hemodynamic relevance of atherosclerotic plaques. However, these imaging technologies cannot estimate the inflammatory activity or the vulnerability of atherosclerotic plaques. The fusion of morphological aspects paired with positron emission tomography could help overcome these limitations and, therefore, offers tremendous potential in diagnosing and treating atherosclerosis [2,4].
Previous studies depict 2-deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG) in imaging atherosclerosis. 18F-FDG is a useful tool in giant cell arteritis [5] and illustrates the activity of immune cells (e.g., macrophages) in atherosclerotic plaques in the carotid arteries of symptomatic patients [6]. Previous correlations illustrate 18F-FDG uptake in carotid plaques and the macrophages in histology and, therefore, noninvasively measure inflammation in patients [7]. Furthermore, 18F-FDG uptake in the left anterior descending artery (LAD) is associated with cardiovascular risk factors and plaque burden in patients [8]. 18F-FDG uptake also predicted future vascular events when quantified in atherosclerotic lesions [9] and detected the anti-atherosclerotic effect of statins [10]. Besides the available literature showing the benefits of imaging atherosclerotic lesions, 18F-FDG bears several limitations. The uptake of 18F-FDG is unselectively into all active metabolic cells. The uptake into vessels is influenced by plaque hypoxia and different microcirculatory conditions (reviewed in [11]). Focusing on the inflammation of cardiac arteries, the proximity to the heart muscle is still an obstacle limiting accurate analyses [12], especially in diabetic patients [13]. In recent years, the knowledge of immune cells, especially macrophages, and their pivotal role in cardiovascular disease and inflammation grew immensely [14].
Macrophages express the somatostatin surface receptor subtype 2 (SSTR-2) [15,16]. Transgenic mice with apolipoprotein E (ApoE) deficiency develop atherosclerotic plaques, validating the uptake of 68Ga-DOTATATE in plaques, as shown by autoradiography [17]. Furthermore, in histochemistry analysis, plaques showed high numbers of macrophages. These results could be reproduced by another atherosclerosis transgenic mouse model using low-density lipoprotein (LDL) receptor deficiency [18].
Moreover, cancer patients receiving 64Cu-DOTATATE and 68Ga-DOTATATE imaging showed relevant tracer uptake in large arteries and significant correlations to cardiovascular risk factors [19]. In the patient undergoing endarterectomy in the carotid artery, 64Cu-DOTATATE was exceptionally high on the symptomatic compared to the contralateral side [20]. However, conflicting data show the opposite observation in other symptomatic patients before the endarterectomy of the carotid artery [21]. Moreover, no SSTR-2 expression was evident in the explanted plaques in immunohistochemistry. Targeting vulnerable atherosclerotic plaques via a PET-tracer may enhance diagnostic imaging modalities [22]. Recently, a subgroup analysis of CT-guided plaque categories showed that more severe plaques correlate with tracer uptake [23]. Significant calcium scores were also associated with a higher uptake of 68Cu-DOTATATE in the left anterior descending artery [8]. Interestingly, no co-localization of 68Cu-DOTATATE and 18F-FDG has been published yet, hypothesizing that the tracers illustrate different processes in atherosclerosis. In summary, 68Cu-DOTATATE imaging illustrates distinct processes in atherosclerotic plaques and, therefore, hypothetically leads to novel diagnostic and therapeutic paths.
The peptide receptor radionuclide therapy (PRRT) is a validated tool in somatostatin receptor-positive cancer, e.g., neuroendocrine cancer. Therapy regimens use 90Yttrium (90Y) and 177Lutetium (177Lu) [24,25,26]. However, it remains unknown what effect the radiotracers have on other cells that express the SSTR-2 in calcified atherosclerotic plaques. In this study, we aimed to evaluate the effect of 90Y- and 177Lu-DOTATATE therapy on atherosclerotic plaque inflammation using serial 68Ga-DOTATATE PET/CT imaging and, therefore, adding data to the existing body of literature.

2. Materials and Methods

2.1. Patient Cohort

This retrospective study analyzed 57 oncologic patients having 68Ga-DOTATATE PET/CT imaging (carcinoid (n = 57), including pancreatic primary (n = 14), small intestine (n = 23), other primary (n = 11), unknown primary (n = 7), thymic carcinoma (n = 1), and thyroid cancer (n = 1). Overall, 37 patients received four PRRT cycles (90Y- or 177-Lu DOTATATE therapy), representing the therapy group. All patients had a baseline 68Ga-DOTATATE PET/CT before PRRT administration and a control PET/CT after 2 PRRT cycles. The other patients (n = 20) had regular staging scans using 68Ga-DOTATATE without PRRT (see Table 1). Patients receiving chemotherapy four weeks before PRRT with systemic rheumatic disease or vasculitis were excluded. In total, 171 PET/CTs were analyzed. This retrospective study was approved by the Ethikkommission der Medizinischen Fakultät der LMU München (protocol code 19-528 and date of approval 29 August 2019) and complied with the declaration of Helsinki.

2.2. 68Ga-DOTATATE Imaging Technique

Patients underwent 68Ga-DOTATATE PET/CT imaging in der Department of Nuclear Medicine, LMU Munich, on a Biograph TruePoint 64 (Siemens Health engineers, Erlangen, Germany). Prior to scanning, patients did not fast. 68Ga-DOTATATE was administered intravenously in a single bolus injection (appr. 200 MBq). Patients received 20 mg of furosemide to enhance diuresis. After 60 min of rest, the patients were transferred to the scanning suite for data acquisition. Transmission data were acquired via a low-dose CT scan (220 mAs, 120 kV, 512 × 512 matrix, 5 mm slice thickness, 5 mm/s increment, 0.5 s rotation time, 0.65 pitch index).
Three-dimensional (3D) PET emission scans were acquired with a 144 × 144 matrix. Following decay and scatter correction, PET data were reconstructed without attenuation correction.

2.3. Image Analysis

The imaging analysis was adapted, as described previously [27]. Eight distinct arterial regions were defined: ascending aorta, aortic arch, descending aorta, abdominal aorta, right iliac artery, left iliac artery, right carotid artery, and left carotid artery. Fused PET/CT images were used for the region of interest (ROI) analyses evaluating each region of the respective artery. A round ROI of 1 cm was used. Maximum standardized uptake values (SUVmax) were measured, as described previously [27]. The mean of three blood-pool SUVs was calculated in the inferior vena cava and superior vena cava, respectively. The mean of the total six values was defined as the SUVbloodpool. The SUVmax was divided by the SUVbloodpool, yielding the target-to-background ratio (TBR) for each artery included in the analysis [9,28]. The overall vessel uptake (OVU) was defined as the sum of all TBR values. CT scans were evaluated for the calcified plaque in the same vessel wall of the same arterial segment investigated by 68Ga-DOTATATE PET. Calcification was semi-quantitatively ranked from 0 to 4, as described previously [29]. A score of 0 was assigned when the calcified plaque was absent; 1, defined as a small, calcified plaque covering less than 10% of the vessel circumference; 2, when the calcified plaque involved 10% to 25% of the vessel circumference; 3, when 25% to 50% of the vessel was calcified; and 4, when more than 50% of the vessel circumference was involved. CP scores are the sum of 8 respective areas, representing the ascending aorta, aortic arch, descending aorta, abdominal aorta, left and right iliac arteries, and left and right carotid arteries. The program Hybrid Viewer (Hermes Medical Solutions, Stockholm, Sweden) was used for image analysis.

2.4. Statistical Analysis

Statistics were performed using IBM SPSS Statistics (Version 21.0.0.0). The Shapiro–Wilk test evaluated normal distribution. The Student’s t-test analyzed data with normal distribution after the Levene test for variance analysis. A non-paired t-test was used to analyze the therapy and control groups. A paired t-test was used for the same group at different follow-up scans. A one-way ANOVA with post hoc Bonferroni was used to analyze more than two groups. Mann–Whitney U tests were used for nonparametric data. The Wilcoxon test was used to measure the differences between the two groups using repeated measurements. The Chi-Square test compared qualitative variables. Correlation analysis used Pearson’s r calculated as a measure of the linear correlation between two metric datasets, and Spearman for non-metric variables. p value < 0.05 was considered statistically significant.

3. Results

3.1. Patient Population

In total, the therapy and control groups consisted of 37 and 20 patients, respectively. The descriptive statistics comparing these groups did not evaluate significant differences, except the dose of the third PET/CT scan (control vs. therapy), and the time between the first and second scan (control vs. therapy) was different. The study cohort did not differ in other parameters, such as age, gender, diabetes mellitus, hypertension, and BMI. All patient parameters are listed in Table 2.

3.2. Correlation of Calcified Plaque Score, Overall Vessel Uptake (OVU), and Age

First, the PET/CT images were evaluated for tracer uptake and CP score. Figure 1A illustrates the CP burden from scores 0 to 4. The SUVbloodpool, SUVmax, and TBR were derived from the fused PET and CT images for the arteries included in the evaluation (e.g., ascending aorta, aortic arch, descending aorta, abdominal aorta, both iliac arteries, and both carotid arteries) (see Figure 1B).
Next, correlation analysis was performed for age, the OVU, and the CP scores at the first PET/CT scan prior to PRRT therapy. The sum of the therapy and control groups was used for analysis (n = 57). Our data show a positive correlation for the CP score to patient age (R = 0.589, p < 0.01) (Figure 2) and for the correlation of the CP score to the OVU (R = 0.276, p < 0.05) in the first PET/CT scan. Of note, there was no correlation between the OVU and age (R = 0.147, p = 0.275).

3.3. Correlation of Each Vessel Segment to OVU

Additionally, correlation studies for each vessel segment to the OVU were performed. This analysis should ensure the representability of the OVU in generalized atherosclerosis. Analysis of the TBR in all vessel segments to the OVU showed excellent R values (p < 0.001). (See Table 3). The best correlation was detected for the ascending aorta (R = 0.891; p < 0.001).

3.4. Longitudinal OVU Evaluation in the Therapy Group

In the next step, the OVU was analyzed in the therapy group to detect alterations in the 68Ga-DOTATAE uptake. The OVU analysis of the therapy group included 37 patients but did not show relevant changes in the follow-up scans (1. PET/CT vs. 2. PET/CT: p = 0.40; 2. PET/CT vs. 3. PET/CT: p = 0.51; 1. PET/CT vs. 3. PET/CT: p = 0.62; see Figure 3).

3.5. Tercile-Based Analysis of Patient Cohorts

In the next step, the therapy and control groups were ranked by their OVU value and separated into tercile-based subgroups, as described previously for the 18F-FDG assessment of atherosclerotic plaques [8]. Four subgroups were created based on the OVU: therapy group low (TG_low), therapy group high (TG_high), control group low (CG_low), and control group high (CG_high). The subgroups were compared by descriptive statistical analysis (see Table 4). The baseline characteristics were equally distributed. No significant differences were found among the tercile-based subgroups. The middle tercile was not used for further analysis.
The tercile-based subgroups intra-group analysis showed no change in the TG_low group from scan one to three but, interestingly, a significant decrease in the TG_high group (1. PET/CT vs. 2. PET/CT; p = 0.04 and 1. PET/CT vs. 3. PET/CT; p = 0.03). Furthermore, in the CG_low group, the OVU changed (1. PET/CT vs. 2. PET/CT; p = 0.04; Table 5 and Figure 4). In inter-group analysis, there was no difference comparing the TG_high and CG_high groups at the 1. PET/CT baseline scan. The OVU was significantly lower in the 2. and 3. PET/CT scan in the TG_high group compared to the CG_high group (each p < 0.001). In addition, there was no statistical difference when comparing the TG_low and CG_low groups.

4. Discussion

Detrimental cardio- and cerebrovascular events, e.g., myocardial infarction or cerebral stroke, mainly result from ruptured atherosclerotic plaques. Without imaging techniques, it is impossible to identify the plaques prone to rupturing, the so-called vulnerable plaques [30,31]. Inflammation in atherosclerotic plaques plays an immense role in this context (reviewed in [32]). Combining PET and CT, or MR for hybrid imaging, enables the evaluation of not only morphological but more critical biological functional data. DOTATATE represents a nuclear tracer binding to the somatostatin receptor, which is also present in immune cells, especially in macrophages [16]. Macrophages play a pivotal role in atherosclerosis [33]. This retrospective study wanted to assess the feasibility of DOTATATE imaging for atherosclerotic plaques and evaluate the impact of PRRT therapy on atherosclerotic plaques. This work shows a decline in tracer uptake in the therapy subgroup with initially high values. At baseline, the OVU moderately correlates with the calcified plaque score, thus enabling a diagnostic approach. However, it should be mentioned that this study did not classify if the atherosclerotic plaques showed, e.g., a high immune cell count in histology. This could partially explain the moderate statistic for correlation analysis by potentially summating low- and high-inflammatory plaques.
Previous retrospective and prospective clinical studies could show the feasibility of PET/CT for atherosclerotic imaging using 18F-FDG [7,34,35,36]. Studies using 18F-FDG managed to show the degree of inflammation in atherosclerotic plaques [13]. However, there is also a specific range in variability derived from patient preparation, blood glucose levels, the injected dose, and the time between the 18F-FDG injection and data acquisition [37]. Besides these difficulties, patients are not allowed to eat six hours before the scans, and some patients suffer from diabetes. However, diabetic patients are prone to develop atherosclerosis. Another limitation is the 18F-FDG uptake in the heart, further limiting the assessment of the coronary arteries (reviewed in [38]).
Several previous studies show the feasibility of 18F-NaF for imaging coronary atherosclerosis (reviewed in [38]). Consequently, other tracers were evaluated for feasibility. In patients with cancer, a retrospective evaluation showed 18F-NaF uptake into the vascular system imaging atherosclerotic plaques [39] and association with cardiovascular risk factors and the Framingham risk score [40,41,42]. Compared to the 18F-FDG uptake, there was a higher uptake of 18F-NaF in the vessels [43]. Also, in prospective studies, 18F-NaF uptake was significantly higher in patients with known coronary artery disease (CAD) compared to 18F-FDG [44]. Interestingly, in patients with recent myocardial infarction, a distinct 18F-NaF uptake in the area of the vulnerable plaque could be shown [45]. Another study could show a higher target-to-background ratio in vessels with vulnerable plaques than in small vessels [46].
The cellular target in macrophages, the somatostatin receptor subtype 2, could be a promising approach [15,16]. Thus, imaging the inflammatory activity in atherosclerotic plaques could be conducted without the limitation of myocardial uptake, compared to 18F-FDG [12,36]. The first prospective study (VISION study: vascular inflammation using somatostatin receptor positron emission tomography) in non-oncologic patients suffering from coronary heart disease and acute coronary syndrome aimed to investigate this question.
68Ga-DOTATATE could more reliably identify vulnerable plaques compared to 18F-FDG [36]. Tarkin et al. could show that 68Ga-DOTATATE represents the activity of macrophages [36]. Major adverse events, such as myocardial infarction and stroke, result from plaque rupture in coronary and carotid arteries, respectively. The detection of vulnerable coronary and carotid plaques, as previously described by Tarkin et al., may tremendously improve risk assessment and clinical follow-up in patients with cardiovascular disease. Therefore, it remains the question if PRRT therapy displayed by 90Y-DOTATATE and 177Lu-DOTATATE can also target macrophages and lead to a modulation of inflammatory processes in atherosclerotic plaques. Schatka et al. recently detected an OVU decrease after PRRT therapy in a small cohort of 11 patients [47]. Different tracer uptake times (30 ± 10 min) and 68Ga-DOTATATE activities (73 ± 13 MBq) make a direct comparison of the studies challenging. The higher OVU in Schatka et al. could be due to patients with high cardiovascular risk. Nevertheless, both studies could show a decrease in the OVU after PRRT, especially in patients with high OVU values. This OVU drop underlines the possibility of new therapeutic approaches, such as immune cell modulation by target-specific tracers.

Limitations

The high-sensitive (hs) C-reactive protein (hs-CRP) as a biomarker for atherosclerosis was not evaluated in this study. Due to the nature of the retrospective study, not all the cardiovascular risk factors and events and atherosclerotic sequels could potentially be derived from the records. In addition, our study population, which consists of oncologic patients, is not suitable for creating a reliable hypothesis regarding hsCRP change. Another limitation is the moderate number of patients included in the study (n = 57). Another limitation is the lack of standard to assess inflammation in vessels and as well the lack of assessment of the plaque morphology beyond that of calcification, since contrast was not given during CT acquisition, which may further enable more accurate plaque assessment.

5. Conclusions

This retrospective study assessed the effect of 90Y-DOTATATE and 177Lu-DOTATATE on atherosclerotic plaque remodeling in oncologic patients by monitoring 68GA-DOTATATE uptake in serial longitudinal PET/CT follow-up scans. We could detect a decline in 68GA-DOTATATE activity in the tercile therapy cohort with an initially high tracer uptake. The OVU shows a solid correlation to the TBR in all arterial segments. The best correlation of the TBR in the ascending aorta could serve as the reference value for atherosclerotic plaque inflammation. Our results support the notion that PRRT could modulate atherosclerotic plaque inflammation and might, therefore, be an interesting therapeutic approach in the future.

Author Contributions

Conceptualization, G.R., M.F. and M.J.Z.; methodology, A.T.; software, H.I.; validation, G.R. and S.L.; formal analysis, M.F.; investigation, G.R.; resources, P.B., M.H. and S.L.; data curation, A.T.; writing—original draft preparation, M.F.; writing—review and editing, M.J.Z. and A.T.; visualization, G.R.; supervision, A.T.; project administration, S.L. and P.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The retrospective study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of LMU Munich (protocol code 19-528 and date of approval 29 August 2019).

Informed Consent Statement

Patient consent was waived due to retrospective and anonymized character of the study according to Ethikkommission der Medizinischen Fakultät der LMU München.

Data Availability Statement

Further information and requests for resources and data should be directed to and will be fulfilled by the lead contact Maximilian Fischer ([email protected]).

Acknowledgments

Part of this study is derived from the doctoral thesis of German Rubinstein.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Mach, F.; Baigent, C.; Catapano, A.L.; Koskinas, K.C.; Casula, M.; Badimon, L.; Chapman, M.J.; de Backer, G.G.; Delgado, V.; Ference, B.A.; et al. 2019 ESC/EAS Guidelines for the Management of Dyslipidaemias: Lipid Modification to Reduce Cardiovascular Risk. Eur. Heart J. 2020, 41, 111–188. [Google Scholar] [CrossRef] [PubMed]
  2. Rudd, J.H.F.; Hyafil, F.; Fayad, Z.A. Inflammation Imaging in Atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 2009, 29, 1009–1016. [Google Scholar] [CrossRef] [PubMed]
  3. Libby, P.; Tabas, I.; Fredman, G.; Fisher, E.A. Inflammation and Its Resolution as Determinants of Acute Coronary Syndromes. Circ. Res. 2014, 114, 1867–1879. [Google Scholar] [CrossRef] [PubMed]
  4. Tomey, M.I.; Narula, J.; Kovacic, J.C. Advances in the Understanding of Plaque Composition and Treatment Options: Year in Review. J. Am. Coll. Cardiol. 2014, 63, 1604–1616. [Google Scholar] [CrossRef] [PubMed]
  5. Zerizer, I.; Tan, K.; Khan, S.; Barwick, T.; Marzola, M.C.; Rubello, D.; Al-Nahhas, A. Role of FDG-PET and PET/CT in the Diagnosis and Management of Vasculitis. Eur. J. Radiol. 2010, 73, 504–509. [Google Scholar] [CrossRef]
  6. Rudd, J.H.F.; Narula, J.; Strauss, H.W.; Virmani, R.; Machac, J.; Klimas, M.; Tahara, N.; Fuster, V.; Warburton, E.A.; Fayad, Z.A.; et al. Imaging Atherosclerotic Plaque Inflammation by Fluorodeoxyglucose With Positron Emission Tomography. J. Am. Coll. Cardiol. 2010, 55, 2527–2535. [Google Scholar] [CrossRef]
  7. Tawakol, A.; Migrino, R.Q.; Bashian, G.G.; Bedri, S.; Vermylen, D.; Cury, R.C.; Yates, D.; LaMuraglia, G.M.; Furie, K.; Houser, S.; et al. In Vivo 18F-Fluorodeoxyglucose Positron Emission Tomography Imaging Provides a Noninvasive Measure of Carotid Plaque Inflammation in Patients. J. Am. Coll. Cardiol. 2006, 48, 1818–1824. [Google Scholar] [CrossRef]
  8. Saam, T.; Rominger, A.; Wolpers, S.; Nikolaou, K.; Rist, C.; Greif, M.; Cumming, P.; Becker, A.; Foerster, S.; Reiser, M.F.; et al. Association of Inflammation of the Left Anterior Descending Coronary Artery with Cardiovascular Risk Factors, Plaque Burden and Pericardial Fat Volume: A PET/CT Study. Eur. J. Nucl. Med. Mol. Imaging 2010, 37, 1203–1212. [Google Scholar] [CrossRef]
  9. Rominger, A.; Saam, T.; Wolpers, S.; Cyran, C.C.; Schmidt, M.; Foerster, S.; Nikolaou, K.; Reiser, M.F.; Bartenstein, P.; Hacker, M. 18F-FDG PET/CT Identifies Patients at Risk for Future Vascular Events in an Otherwise Asymptomatic Cohort with Neoplastic Disease. J. Nucl. Med. 2009, 50, 1611–1620. [Google Scholar] [CrossRef]
  10. Tahara, N.; Kai, H.; Ishibashi, M.; Nakaura, H.; Kaida, H.; Baba, K.; Hayabuchi, N.; Imaizumi, T. Simvastatin Attenuates Plaque Inflammation. Evaluation by Fluorodeoxyglucose Positron Emission Tomography. J. Am. Coll. Cardiol. 2006, 48, 1825–1831. [Google Scholar] [CrossRef]
  11. Tarkin, J.M.; Dweck, M.R.; Evans, N.R.; Takx, R.A.P.; Brown, A.J.; Tawakol, A.; Fayad, Z.A.; Rudd, J.H.F. Imaging Atherosclerosis. Circ. Res. 2016, 118, 750–769. [Google Scholar] [CrossRef] [PubMed]
  12. Alie, N.; Eldib, M.; Fayad, Z.A.; Mani, V. Inflammation, Atherosclerosis, and Coronary Artery Disease: PET/CT for the Evaluation of Atherosclerosis and Inflammation. Clin. Med. Insights Cardiol. 2014, 8, 13–21. [Google Scholar] [CrossRef] [PubMed]
  13. Bucerius, J.; Hyafil, F.; Verberne, H.J.; Slart, R.H.J.A.; Lindner, O.; Sciagra, R.; Agostini, D.; Übleis, C.; Gimelli, A.; Hacker, M. Position Paper of the Cardiovascular Committee of the European Association of Nuclear Medicine (EANM) on PET Imaging of Atherosclerosis. Eur. J. Nucl. Med. Mol. Imaging 2016, 43, 780–792. [Google Scholar] [CrossRef] [PubMed]
  14. Swirski, F.K.; Nahrendorf, M. Cardioimmunology: The Immune System in Cardiac Homeostasis and Disease. Nat. Rev. Immunol. 2018, 18, 733–744. [Google Scholar] [CrossRef]
  15. Dalm, V.A.S.H.; Martin Van Hagen, P.; van Koetsveld, P.M.; Achilefu, S.; Houtsmuller, A.B.; Pols, D.H.J.; van der Lely, A.-J.; Lamberts, S.W.J.; Hofland, L.J. Expression of Somatostatin, Cortistatin, and Somatostatin Receptors in Human Monocytes, Macrophages, and Dendritic Cells. Am. J. Physiol. Endocrinol. Metab. 2003, 285, 344–353. [Google Scholar] [CrossRef]
  16. Armani, C.; Catalani, E.; Balbarini, A.; Bagnoli, P.; Cervia, D. Expression, Pharmacology, and Functional Role of Somatostatin Receptor Subtypes 1 and 2 in Human Macrophages. J. Leukoc. Biol. 2007, 81, 845–855. [Google Scholar] [CrossRef]
  17. Li, X.; Bauer, W.; Kreissl, M.C.; Weirather, J.; Bauer, E.; Israel, I.; Richter, D.; Riehl, G.; Buck, A.; Samnick, S. Specific Somatostatin Receptor II Expression in Arterial Plaque: 68Ga-DOTATATE Autoradiographic, Immunohistochemical and Flow Cytometric Studies in ApoE-Deficient Mice. Atherosclerosis 2013, 230, 33–39. [Google Scholar] [CrossRef]
  18. Rinne, P.; Hellberg, S.; Kiugel, M.; Virta, J.; Li, X.-G.; Käkelä, M.; Helariutta, K.; Luoto, P.; Liljenbäck, H.; Hakovirta, H.; et al. Comparison of Somatostatin Receptor 2-Targeting PET Tracers in the Detection of Mouse Atherosclerotic Plaques. Mol. Imaging Biol. 2016, 18, 99–108. [Google Scholar] [CrossRef]
  19. Malmberg, C.; Ripa, R.S.; Johnbeck, C.B.; Knigge, U.; Langer, S.W.; Mortensen, J.; Oturai, P.; Loft, A.; Hag, A.M.; Kjer, A. 64Cu-DOTATATE for Noninvasive Assessment of Atherosclerosis in Large Arteries and Its Correlation with Risk Factors: Head-to-Head Comparison with 68Ga-DOTATOC in 60 Patients. J. Nucl. Med. 2015, 56, 1895–1900. [Google Scholar] [CrossRef]
  20. Pedersen, S.F.; Sandholt, B.V.; Keller, S.H.; Hansen, A.E.; Clemmensen, A.E.; Sillesen, H.; Højgaard, L.; Ripa, R.S.; Kjær, A. 64Cu-DOTATATE PET/MRI for Detection of Activated Macrophages in Carotid Atherosclerotic Plaques: Studies in Patients Undergoing Endarterectomy. Arterioscler. Thromb. Vasc. Biol. 2015, 35, 1696–1703. [Google Scholar] [CrossRef]
  21. Wan, M.Y.S.; Endozo, R.; Michopoulou, S.; Shortman, R.; Rodriguez-Justo, M.; Menezes, L.; Yusuf, S.; Richards, T.; Wild, D.; Waser, B.; et al. PET/CT Imaging of Unstable Carotid Plaque with 68Ga-Labeled Somatostatin Receptor Ligand. J. Nucl. Med. 2017, 58, 774–780. [Google Scholar] [CrossRef] [PubMed]
  22. Papadakis, G.Z.; Kochiadakis, G.; Lazopoulos, G.; Marias, K.; Klapsinos, N.; Hannah-Shmouni, F.; Igoumenaki, G.G.; Nikolouzakis, T.K.; Kteniadakis, S.; Spandidos, D.A.; et al. Targeting Vulnerable Atherosclerotic Plaque via PET-Tracers Aiming at Cell-Surface Overexpression of Somatostatin Receptors. Biomed. Rep. 2020, 13, 1–3. [Google Scholar] [CrossRef] [PubMed]
  23. Mojtahedi, A.; Alavi, A.; Thamake, S.; Amerinia, R.; Ranganathan, D.; Tworowska, I.; Delpassand, E.S. Assessment of Vulnerable Atherosclerotic and Fibrotic Plaques in Coronary Arteries Using (68)Ga-DOTATATE PET/CT. Am. J. Nucl. Med. Mol. Imaging 2015, 5, 65–71. [Google Scholar] [PubMed]
  24. Kam, B.L.R.; Teunissen, J.J.M.; Krenning, E.P.; de Herder, W.W.; Khan, S.; van Vliet, E.I.; Kwekkeboom, D.J. Lutetium-Labelled Peptides for Therapy of Neuroendocrine Tumours. Eur. J. Nucl. Med. Mol. Imaging 2012, 39, 103–112. [Google Scholar] [CrossRef]
  25. Khan, S.; Krenning, E.P.; van Essen, M.; Kam, B.L.; Teunissen, J.J.; Kwekkeboom, D.J. Quality of Life in 265 Patients with Gastroenteropancreatic or Bronchial Neuroendocrine Tumors Treated with [ 177Lu-DOTA 0,Tyr 3]Octreotate. J. Nucl. Med. 2011, 52, 1361–1368. [Google Scholar] [CrossRef]
  26. Kwekkeboom, D.J.; de Herder, W.W.; Kam, B.L.; van Eijck, C.H.; van Essen, M.; Kooij, P.P.; Feelders, R.A.; van Aken, M.O.; Krenning, E.P. Treatment with the Radiolabeled Somatostatin Analog [177Lu- DOTA0,Tyr3]Octreotate: Toxicity, Efficacy, and Survival. J. Clin. Oncol. 2008, 26, 2124–2130. [Google Scholar] [CrossRef]
  27. Rominger, A.; Saam, T.; Vogl, E.; Übleis, C.; la Fougère, C.; Förster, S.; Haug, A.; Cumming, P.; Reiser, M.F.; Nikolaou, K.; et al. In Vivo Imaging of Macrophage Activity in the Coronary Arteries Using 68Ga-DOTATATE PET/CT: Correlation with Coronary Calcium Burden and Risk Factors. J. Nucl. Med. 2010, 51, 193–197. [Google Scholar] [CrossRef]
  28. Rudd, J.H.F.; Myers, K.S.; Bansilal, S.; Machac, J.; Rafique, A.; Farkouh, M.; Fuster, V.; Fayad, Z.A. 18Fluorodeoxyglucose Positron Emission Tomography Imaging of Atherosclerotic Plaque Inflammation Is Highly Reproducible. Implications for Atherosclerosis Therapy Trials. J. Am. Coll. Cardiol. 2007, 50, 892–896. [Google Scholar] [CrossRef]
  29. Brown, E.R.; Kronmal, R.A.; Bluemke, D.A.; Guerci, A.D.; Carr, J.J.; Goldin, J.; Detrano, R. Coronary Calcium Coverage Score: Determination, Correlates, and Predictive Accuracy in the Multi-Ethnic Study of Atherosclerosis. Radiology 2008, 247, 669–678. [Google Scholar] [CrossRef]
  30. Virmani, R.; Burke, A.P.; Farb, A.; Kolodgie, F.D. Pathology of the Vulnerable Plaque. J. Am. Coll. Cardiol. 2006, 47, C13–C18. [Google Scholar] [CrossRef]
  31. Sadeghi, M.M.; Glover, D.K.; Lanza, G.M.; Fayad, Z.A.; Johnson, L.L. Imaging Atherosclerosis and Vulnerable Plaque. J. of Nucl. Med. 2010, 51, 51S–65S. [Google Scholar] [CrossRef] [PubMed]
  32. Wolf, D.; Ley, K. Immunity and Inflammation in Atherosclerosis. Circ. Res. 2019, 124, 315–327. [Google Scholar] [CrossRef] [PubMed]
  33. Libby, P. The Changing Landscape of Atherosclerosis. Nature 2021, 592, 524–533. [Google Scholar] [CrossRef] [PubMed]
  34. Rudd, J.H.F.; Myers, K.S.; Bansilal, S.; Machac, J.; Woodward, M.; Fuster, V.; Farkouh, M.E.; Fayad, Z.A. Relationships among Regional Arterial Inflammation, Calcification, Risk Factors, and Biomarkers: A Prospective Fluorodeoxyglucose Positron-Emission Tomography/Computed Tomography Imaging Study. Circ. Cardiovasc. Imaging 2009, 2, 107–115. [Google Scholar] [CrossRef] [PubMed]
  35. Rudd, J.H.F.; Myers, K.S.; Bansilal, S.; Machac, J.; Pinto, C.A.; Tong, C.; Rafique, A.; Hargeaves, R.; Farkouh, M.; Fuster, V.; et al. Atherosclerosis Inflammation Imaging with 18F-FDG PET: Carotid, Iliac, and Femoral Uptake Reproducibility, Quantification Methods, and Recommendations. J. Nucl. Med. 2008, 49, 871–878. [Google Scholar] [CrossRef]
  36. Tarkin, J.M.; Joshi, F.R.; Evans, N.R.; Chowdhury, M.M.; Figg, N.L.; Shah, A.v.; Starks, L.T.; Martin-Garrido, A.; Manavaki, R.; Yu, E.; et al. Detection of Atherosclerotic Inflammation by 68Ga-DOTATATE PET Compared to [18F]FDG PET Imaging. J. Am. Coll. Cardiol. 2017, 69, 1774–1791. [Google Scholar] [CrossRef]
  37. Gholami, S.; Salavati, A.; Houshmand, S.; Werner, T.J.; Alavi, A. Assessment of Atherosclerosis in Large Vessel Walls: A Comprehensive Review of FDG-PET/CT Image Acquisition Protocols and Methods for Uptake Quantification. J. Nucl. Cardiol. 2015, 22, 468–479. [Google Scholar] [CrossRef]
  38. Scherer, D.J.; Psaltis, P.J. Future Imaging of Atherosclerosis: Molecular Imaging of Coronary Atherosclerosis with 18F Positron Emission Tomography. Cardiovasc. Diagn. Ther. 2016, 6, 354–367. [Google Scholar] [CrossRef]
  39. Derlin, T.; Richter, U.; Bannas, P.; Begemann, P.; Buchert, R.; Mester, J.; Klutmann, S. Feasibility of 18F-Sodium Fluoride PET/CT for Imaging of Atherosclerotic Plaque. J. Nucl. Med. 2010, 51, 862–865. [Google Scholar] [CrossRef]
  40. Derlin, T.; Wisotzki, C.; Richter, U.; Apostolova, I.; Bannas, P.; Weber, C.; Mester, J.; Klutmann, S. In Vivo Imaging of Mineral Deposition in Carotid Plaque Using 18F-Sodium Fluoride PET/CT: Correlation with Atherogenic Risk Factors. J. Nucl. Med. 2011, 52, 362–368. [Google Scholar] [CrossRef]
  41. Janssen, T.; Bannas, P.; Herrmann, J.; Veldhoen, S.; Busch, J.D.; Treszl, A.; Münster, S.; Mester, J.; Derlin, T. Association of Linear 18F-Sodium Fluoride Accumulation in Femoral Arteries as a Measure of Diffuse Calcification with Cardiovascular Risk Factors: A PET/CT Study. J. Nucl. Cardiol. 2013, 20, 569–577. [Google Scholar] [CrossRef] [PubMed]
  42. Morbelli, S.; Fiz, F.; Piccardo, A.; Picori, L.; Massollo, M.; Pestarino, E.; Marini, C.; Cabria, M.; Democrito, A.; Cittadini, G.; et al. Divergent Determinants of 18F-NaF Uptake and Visible Calcium Deposition in Large Arteries: Relationship with Framingham Risk Score. Int. J. Cardiovasc. Imaging 2014, 30, 439–447. [Google Scholar] [CrossRef] [PubMed]
  43. Derlin, T.; Tóth, Z.; Papp, L.; Wisotzki, C.; Apostolova, I.; Habermann, C.R.; Mester, J.; Klutmann, S. Correlation of Inflammation Assessed By18F-FDG PET, Active Mineral Deposition Assessed By18F-Fluoride PET, and Vascular Calcification in Atherosclerotic Plaque: A Dual-Tracer PET/CT Study. J. Nucl. Med. 2011, 52, 1020–1027. [Google Scholar] [CrossRef] [PubMed]
  44. Dweck, M.R.; Jenkins, W.S.A.; Vesey, A.T.; Pringle, M.A.H.; Chin, C.W.L.; Malley, T.S.; Cowie, W.J.A.; Tsampasian, V.; Richardson, H.; Fletcher, A.; et al. 18F-Sodium Fluoride Uptake Is a Marker of Active Calcification and Disease Progression in Patients With Aortic Stenosis. Circ. Cardiovasc. Imaging 2014, 7, 371–378. [Google Scholar] [CrossRef]
  45. Dweck, M.R.; Chow, M.W.L.; Joshi, N.V.; Williams, M.C.; Jones, C.; Fletcher, A.M.; Richardson, H.; White, A.; McKillop, G.; van Beek, E.J.R.; et al. Coronary Arterial 18F-Sodium Fluoride Uptake: A Novel Marker of Plaque Biology. J. Am. Coll. Cardiol. 2012, 59, 1539–1548. [Google Scholar] [CrossRef]
  46. Joshi, N.v.; Vesey, A.T.; Williams, M.C.; Shah, A.S.V.; Calvert, P.A.; Craighead, F.H.M.; Yeoh, S.E.; Wallace, W.; Salter, D.; Fletcher, A.M.; et al. 18F-Fluoride Positron Emission Tomography for Identification of Ruptured and High-Risk Coronary Atherosclerotic Plaques: A Prospective Clinical Trial. Lancet 2014, 383, 705–713. [Google Scholar] [CrossRef]
  47. Schatka, I.; Wollenweber, T.; Haense, C.; Brunz, F.; Gratz, K.F.; Bengel, F.M. Peptide Receptor-Targeted Radionuclide Therapy Alters Inflammation in Atherosclerotic Plaques. J. Am. Coll. Cardiol. 2013, 62, 2344–2345. [Google Scholar] [CrossRef]
Diagnostics 14 02486 g001
Figure 1. PET/CT image analysis. (A) Example of modified calcified plaque (CP) scoring system. Representative images show scores of 0 (no calcified plaque), 1 (calcified plaque involving 10% of vessel circumference), 2 (calcified plaque involving 10% to 25%), 3 (calcified plaque involving 25% to 50%), and 4 (calcified plaque involving >50%). (B) Representative image for target-to-background (TBR) calculation method. ROIs were drawn in the center of the vena cava inferior (blue) and arterial wall (red) for SUV evaluation.
Figure 1. PET/CT image analysis. (A) Example of modified calcified plaque (CP) scoring system. Representative images show scores of 0 (no calcified plaque), 1 (calcified plaque involving 10% of vessel circumference), 2 (calcified plaque involving 10% to 25%), 3 (calcified plaque involving 25% to 50%), and 4 (calcified plaque involving >50%). (B) Representative image for target-to-background (TBR) calculation method. ROIs were drawn in the center of the vena cava inferior (blue) and arterial wall (red) for SUV evaluation.
Diagnostics 14 02486 g001
Diagnostics 14 02486 g002
Figure 2. Correlation analysis of CP score, age, and OVU in the first PET/CT scan prior to PRRT therapy. N = 57.
Figure 2. Correlation analysis of CP score, age, and OVU in the first PET/CT scan prior to PRRT therapy. N = 57.
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Diagnostics 14 02486 g003
Figure 3. Boxplots illustrating the therapy group’s longitudinal assessment of the OVU, including the first to third 68Ga-DOTATATE scan.
Figure 3. Boxplots illustrating the therapy group’s longitudinal assessment of the OVU, including the first to third 68Ga-DOTATATE scan.
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Figure 4. Boxplot illustration of the OVU course in therapy and control group in all three PET/CTs. * p < 0.05.
Figure 4. Boxplot illustration of the OVU course in therapy and control group in all three PET/CTs. * p < 0.05.
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Table 1. Study design of the retrospective evaluation.
Table 1. Study design of the retrospective evaluation.
Study Design
Therapy GroupControl Group
1. 68Ga-DOTATATE PET/CT (Baseline)1. 68Ga-DOTATATE PET/CT (Baseline)
      1.
Cycle PRRT
      2.
Cycle PRRT
2. 68Ga-DOTATATE PET/CT2. 68Ga-DOTATATE PET/CT
      3.
Cycle PRRT
      4.
Cycle PRRT
3. 68Ga-DOTATATE PET/CT3. 68Ga-DOTATATE PET/CT
Table 2. Descriptive statistics illustrating baseline characteristics of therapy and control group. Nominal variables are displayed as numbers/percentages, and metric variables are shown as mean values ± standard deviation. Years (y), days (d), megabecquerel (MBq).
Table 2. Descriptive statistics illustrating baseline characteristics of therapy and control group. Nominal variables are displayed as numbers/percentages, and metric variables are shown as mean values ± standard deviation. Years (y), days (d), megabecquerel (MBq).
Descriptive Statistics for Therapy and Control Group
Therapy Group
(n = 37)		Control Group
(n = 20)		p-Value
Age [y]59 ± 1063 ± 120.17
Male23 (62.2%)9 (45%)0.268
Diabetes mellitus6 (16.2%)4 (20%)0.728
Hypertension26 (70.3%)12 (60%)0.558
BMI [kg/m2]25.9 ± 4.827.5 ± 4.90.198
OVU18.5 ± 6.719.6 ± 5.70.707
CP score6 ± 610 ± 90.156
Dose 1. PET/CT [MBq]206 ± 27208 ± 310.841
Dose 2. PET/CT [MBq]218 ± 20221 ± 290.735
Dose 3. PET/CT [MBq]211 ± 23229 ± 400.036
Cycle count 177Lutetium136
Dose 177Lutetium [MBq]7254 ± 640
Cycle count 90Yttrium12
Dose 90Yttrium [MBq]3647 ± 189
Time between 1st and 2nd PET/CT [d]214 ± 68174 ± 760.019
Time between 2nd and 3rd PET/CT [d]407 ± 356238 ± 1000.408
Time between 1st and 3rd PET/CT [d]621 ± 401413 ± 1140.126
Table 3. Correlation analysis of TBR and OVU for each vessel segment.
Table 3. Correlation analysis of TBR and OVU for each vessel segment.
Correlation Analysis of Each TBR to OVU
Vessel segment
TBR ascending aortaR = 0.891; p < 0.001
TBR aortic archR = 0.825; p < 0.001
TBR descending aortaR = 0.887; p < 0.001
TBR abdominal aortaR = 0.877; p < 0.001
TBR right iliac arteriesR = 0.853; p < 0.001
TBR left iliac arteriesR = 0.804; p < 0.001
TBR right carotid arteryR = 0.784; p < 0.001
TBR left carotid arteryR = 0.848; p < 0.001
Table 4. Descriptive statistics of several subgroups based on tercile split. Metric variables are displayed as mean ± standard deviation. Nominal variables are illustrated as the number/percentage of the whole group. Years (y), days (d), megabecquerel (MBq).
Table 4. Descriptive statistics of several subgroups based on tercile split. Metric variables are displayed as mean ± standard deviation. Nominal variables are illustrated as the number/percentage of the whole group. Years (y), days (d), megabecquerel (MBq).
Descriptive Statistics for Subgroups
Therapy Group (TG)Control Group (CG)p Value
TercileLowHighLowHigh
Count12121010
Age [y]57 ± 1262 ± 1063 ± 1264 ± 120.577
Men6 (50%)9 (75%)3 (30%)6 (60%)0.197
Diabetes mellitus1 (8.3%)2 (16.7%)3 (30%)1 (10%)0.519
Hypertension5 (41.7%)11 (91.7%)6 (60%)6 (60%)0.082
BMI [kg/m2]24.2 ± 4.725.9 ± 3.826.7 ± 5.928.4 ± 3.80.221
OVU14.8 ± 1.423.7 ± 7.714.4 ± 0.624.9 ± 2.6<0.001
CP score4.6 ± 5.27.2 ± 7.27.2 ± 7.512.1 ± 9.70.146
Dose 1. PET/CT [MBq]205 ± 30200 ± 31205 ± 36210 ± 260.897
Dose 2. PET/CT [MBq]219 ± 19225 ± 20217 ± 31225 ± 280.821
Dose 3. PET/CT [MBq]207 ± 20214 ± 17232 ± 30227 ± 500.226
Time from 1. to 3. PET/CT [d]438 ± 94370 ± 70412 ± 86413 ± 1420.425
Table 5. OVU course from first to third PET/CT in the different subgroups (therapy and control group) after tercile splits. Data represent mean ± standard deviation.
Table 5. OVU course from first to third PET/CT in the different subgroups (therapy and control group) after tercile splits. Data represent mean ± standard deviation.
OVU Course
1. PET/CT2. PET/CT3. PET/CT
TG_low14.76 ± 1.4415.4 ± 1.6615.98 ± 3.11
TG_high23.69 ± 7.7218.88 ± 4.6418.7 ± 4
CG_low14.42 ± 0.5615.45 ± 1.4716.22 ± 2.03
CG_high24.89 ± 2.5624.12 ± 1.9324.09 ± 1.93
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Rubinstein, G.; Ilhan, H.; Bartenstein, P.; Lehner, S.; Hacker, M.; Todica, A.; Zacherl, M.J.; Fischer, M. Peptide Receptor Radionuclide Therapy Using 90Y- and 177Lu-DOTATATE Modulating Atherosclerotic Plaque Inflammation: Longitudinal Monitoring by 68Ga-DOTATATE Positron Emissions Tomography/Computer Tomography. Diagnostics 2024, 14, 2486. https://doi.org/10.3390/diagnostics14222486

AMA Style

Rubinstein G, Ilhan H, Bartenstein P, Lehner S, Hacker M, Todica A, Zacherl MJ, Fischer M. Peptide Receptor Radionuclide Therapy Using 90Y- and 177Lu-DOTATATE Modulating Atherosclerotic Plaque Inflammation: Longitudinal Monitoring by 68Ga-DOTATATE Positron Emissions Tomography/Computer Tomography. Diagnostics. 2024; 14(22):2486. https://doi.org/10.3390/diagnostics14222486

Chicago/Turabian Style

Rubinstein, German, Harun Ilhan, Peter Bartenstein, Sebastian Lehner, Marcus Hacker, Andrei Todica, Mathias Johannes Zacherl, and Maximilian Fischer. 2024. "Peptide Receptor Radionuclide Therapy Using 90Y- and 177Lu-DOTATATE Modulating Atherosclerotic Plaque Inflammation: Longitudinal Monitoring by 68Ga-DOTATATE Positron Emissions Tomography/Computer Tomography" Diagnostics 14, no. 22: 2486. https://doi.org/10.3390/diagnostics14222486

APA Style

Rubinstein, G., Ilhan, H., Bartenstein, P., Lehner, S., Hacker, M., Todica, A., Zacherl, M. J., & Fischer, M. (2024). Peptide Receptor Radionuclide Therapy Using 90Y- and 177Lu-DOTATATE Modulating Atherosclerotic Plaque Inflammation: Longitudinal Monitoring by 68Ga-DOTATATE Positron Emissions Tomography/Computer Tomography. Diagnostics, 14(22), 2486. https://doi.org/10.3390/diagnostics14222486

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