Browse
Submit Data
Databases
API
Help

Dataset Information

24 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Functional imaging with 11C-metomidate PET for subtype diagnosis in primary aldosteronism.

ABSTRACT:

Objective

Endocrine Society guidelines recommend adrenal venous sampling (AVS) in primary aldosteronism (PA) if adrenalectomy is considered. We tested whether functional imaging of adrenal cortex with 11C-metomidate (11C-MTO) could offer a noninvasive alternative to AVS in the subtype classification of PA.

Design

We prospectively recruited 58 patients with confirmed PA who were eligible for adrenal surgery.

Methods

Subjects underwent AVS and 11C-MTO-PET without dexamethasone pretreatment in random order. The lateralization of 11C-MTO-PET and adrenal CT were compared with AVS in all subjects and in a prespecified adrenalectomy subgroup in which the diagnosis was confirmed with immunohistochemical staining for CYP11B2.

Results

In the whole study population, the concordance of AVS and 11C-MTO-PET was 51% and did not differ from that of AVS and adrenal CT (53%). The concordance of AVS and 11C-MTO-PET was 55% in unilateral and 44% in bilateral PA. In receiver operating characteristics analysis, the maximum standardized uptake value ratio of 1.16 in 11C-MTO-PET had an AUC of 0.507 (P = n.s.) to predict allocation to adrenalectomy or medical therapy with sensitivity of 55% and specificity of 44%. In the prespecified adrenalectomy subgroup, AVS and 11C-MTO-PET were concordant in 10 of 19 subjects with CYP11B2-positive adenoma and in 6 of 10 with CYP11B2-positivity without an adenoma.

Conclusions

The concordance of 11C-MTO-PET with AVS was clinically suboptimal, and did not outperform adrenal CT. In a subgroup with CYP11B2-positive adenoma, 11C-MTO-PET identified 53% of cases. 11C-MTO-PET appeared to be inferior to AVS for subtype classification of PA.

SUBMITTER: Soinio M

PROVIDER: S-EPMC8045447 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Diagnostic accuracy of adrenal imaging for subtype diagnosis in primary aldosteronism: systematic review and meta-analysis.

Project description:ObjectivesAccurate subtype classification in primary aldosteronism (PA) is critical in assessing the optimal treatment options. This study aimed to evaluate the diagnostic accuracy of adrenal imaging for unilateral PA classification.MethodsSystematic searches of PubMed, EMBASE and the Cochrane databases were performed from 1 January 2000 to 1 February 2020, for all studies that used CT or MRI in determining unilateral PA and validated the results against invasive adrenal vein sampling (AVS). Summary diagnostic accuracies were assessed using a bivariate random-effects model. Subgroup analyses, meta-regression and sensitivity analysis were performed to explore the possible sources of heterogeneity.ResultA total of 25 studies, involving a total of 4669 subjects, were identified. The overall analysis revealed a pooled sensitivity of 68% (95% CI: 61% to 74%) and specificity of 57% (95% CI 50% to 65%) for CT/MRI in identifying unilateral PA. Sensitivity was higher in the contrast-enhanced (CT) group versus the traditional CT group (77% (95% CI 66% to 85%) vs 58% (95% CI 50% to 66%). Subgroup analysis stratified by screening test for PA showed that the sensitivity of the aldosterone-to-renin ratio (ARR) group was higher than that of the non-ARR group (78% (95% CI 69% to 84%) vs 66% (95% CI 58% to 72%)). The diagnostic accuracy of PA patients aged ≤40 years was reported in four studies, and the overall sensitivity was 71%, with 79% specificity. Meta-regression revealed a significant impact of sample size on sensitivity and of age and study quality on specificity.ConclusionCT/MRI is not a reliable alternative to invasive AVS without excellent sensitivity or specificity for correctly identifying unilateral PA. Even in young patients (≤40 years), 21% of patients would have undergone unnecessary adrenalectomy based on imaging results alone.

| S-EPMC7780716 | biostudies-literature

Subtype Diagnosis of Primary Aldosteronism: Is Adrenal Vein Sampling Always Necessary?

Project description:Aldosterone producing adenoma and bilateral adrenal hyperplasia are the two most common subtypes of primary aldosteronism (PA) that require targeted and distinct therapeutic approaches: unilateral adrenalectomy or lifelong medical therapy with mineralocorticoid receptor antagonists. According to the 2016 Endocrine Society Guideline, adrenal venous sampling (AVS) is the gold standard test to distinguish between unilateral and bilateral aldosterone overproduction and therefore, to safely refer patients with PA to surgery. Despite significant advances in the optimization of the AVS procedure and the interpretation of hormonal data, a standardized protocol across centers is still lacking. Alternative methods are sought to either localize an aldosterone producing adenoma or to predict the presence of unilateral disease and thereby substantially reduce the number of patients with PA who proceed to AVS. In this review, we summarize the recent advances in subtyping PA for the diagnosis of unilateral and bilateral disease. We focus on the developments in the AVS procedure, the interpretation criteria, and comparisons of the performance of AVS with the alternative methods that are currently available.

| S-EPMC5412432 | biostudies-literature

Cost-Effectiveness of Screening for Primary Aldosteronism and Subtype Diagnosis in the Resistant Hypertensive Patients.

Project description:Primary aldosteronism (PA) is a common and underdiagnosed disease with significant morbidity potentially cured by surgery. We aim to assess if the long-term cardiovascular benefits of identifying and treating surgically correctable PA outweigh the upfront increased costs in patients at the time patients are diagnosed with resistant hypertension (RH).A decision-analytic model compares aggregate costs and systolic blood pressure changes of 6 recommended or implemented diagnostic strategies for PA in a simulated population of at-risk RH patients. We also evaluate a 7th "treat all" strategy wherein all patients with RH are treated with a mineralocorticoid-receptor antagonist without further testing at RH diagnosis. Changes in systolic blood pressure are subsequently converted into gains in quality-adjusted life years (QALYs) by applying National Health and Nutrition Examination Survey data on concomitant risk factors to an existing cardiovascular disease simulation model. QALYs and lifetime costs were then used to calculate incremental cost-effectiveness ratios for the competing strategies. The incremental cost-effectiveness ratio for the strategy of computerized tomography (CT) followed by adrenal venous sampling (AVS) was $82,000/QALY compared with treat all. Incremental cost-effectiveness ratios for CT alone and AVS alone were $200,000/QALY and $492,000/QALY; the other strategies were more costly and less effective. Integrating differential patient-reported health-related quality of life adjustments for patients with PA, and incremental cost-effectiveness ratios for screening patients with CT followed by AVS, CT alone, and AVS alone were $52,000/QALY, $114,000/QALY, and $269,000/QALY gained.CT scanning followed by AVS was a cost-effective strategy to screen for PA among patients with RH.

| S-EPMC4651757 | biostudies-literature

Machine learning based models for prediction of subtype diagnosis of primary aldosteronism using blood test.

Project description:Primary aldosteronism (PA) is associated with an increased risk of cardiometabolic diseases, especially in unilateral subtype. Despite its high prevalence, the case detection rate of PA is limited, partly because of no clinical models available in general practice to identify patients highly suspicious of unilateral subtype of PA, who should be referred to specialized centers. The aim of this retrospective cross-sectional study was to develop a predictive model for subtype diagnosis of PA based on machine learning methods using clinical data available in general practice. Overall, 91 patients with unilateral and 138 patients with bilateral PA were randomly assigned to the training and test cohorts. Four supervised machine learning classifiers; logistic regression, support vector machines, random forests (RF), and gradient boosting decision trees, were used to develop predictive models from 21 clinical variables. The accuracy and the area under the receiver operating characteristic curve (AUC) for predicting of subtype diagnosis of PA in the test cohort were compared among the optimized classifiers. Of the four classifiers, the accuracy and AUC were highest in RF, with 95.7% and 0.990, respectively. Serum potassium, plasma aldosterone, and serum sodium levels were highlighted as important variables in this model. For feature-selected RF with the three variables, the accuracy and AUC were 89.1% and 0.950, respectively. With an independent external PA cohort, we confirmed a similar accuracy for feature-selected RF (accuracy: 85.1%). Machine learning models developed using blood test can help predict subtype diagnosis of PA in general practice.

| S-EPMC8096956 | biostudies-literature

Electrochemical [11C]CO2 to [11C]CO conversion for PET imaging.

Project description:The development of a novel electrochemical methodology to generate carbon-11 carbon monoxide ([11C]CO) from cyclotron-produced carbon-11 carbon dioxide ([11C]CO2) using Ni(cyclam) and Zn(cyclen) complexes is described. This methodology allows up to 10% yields of [11C]CO from [11C]CO2. Produced [11C]CO was subsequently converted to [11C]N-benzylbenzamide under mild conditions with a radiochemical purity (RCP) of >98%.

| S-EPMC5633808 | biostudies-other

Metabolic Volumetric Parameters in 11C-Choline PET/MR Are Superior PET Imaging Biomarkers for Primary High-Risk Prostate Cancer.

Project description:Purpose:Positron emission tomography/magnetic resonance imaging (PET/MRI) can facilitate the use of noninvasive imaging biomarkers in clinical prostate cancer staging. Although multiparametric MRI is a widely used technique, the clinical value of simultaneous PET imaging remains unclear. This study aimed at investigating this issue. Methods:Between January 2015 and December 2016, 31 high-risk prostate cancer patients underwent 11C-choline PET/MRI for staging purposes. Clinical characteristics and imaging parameters, including the standardized uptake value (SUV) and metabolic volumetric parameters from PET imaging; apparent diffusion coefficient (ADC) values from diffusion-weighted imaging; and volume transfer rate constant (Ktrans), reflux rate constant (Kep), and initial area under curve (iAUC) in 60 seconds from dynamic contrast-enhanced (DCE) MRI were analyzed. Results:11C-Choline PET imaging parameters were significantly correlated with prostate-specific antigen (PSA) levels, and metabolic volumetric parameters, including metabolic tumor volume (MTV) and uptake volume product (UVP), showed significant correlations with other MRI parameters. In our cohort analysis, the PET/MRI parameters UVP/minimal ADC value (ADCmin) and kurtosis of Kep (Kepkur)/ADCmin were significant predictors for progression-free survival (PFS) (HR = 1.01, 95% CI: 1.00-1.02, p=0.031 and HR = 1.09, 95% CI: 1.02-1.16, p=0.009, respectively) in multivariate Cox regression analysis. High UVP/ADCmin and Kepkur/ADCmin values were significantly associated with shorter PFS. Conclusions:Metabolic volumetric parameters such as MTV and UVP can be routinely used as PET imaging biomarkers to add prognostic value and show better correlations in combination with MR imaging parameters in high-risk prostate cancer patients undergoing 11C-choline PET/MRI.

| S-EPMC6247714 | biostudies-literature

11C-acetate PET imaging in patients with multiple sclerosis.

Project description:BACKGROUND: Activation of glial cells is a cardinal feature in multiple sclerosis (MS) pathology, and acetate has been reported to be selectively uptaken by astrocytes in the CNS. The aim of this study was to investigate the efficacy of PET with (11)C-acetate for MS diagnosis. MATERIALS AND METHODS: Six patients with relapsing-remitting MS and 6 healthy volunteers (HV) were enrolled. The (11)C-acetate brain uptake on PET was measured in patients with MS and HV. Volume-of-interest analysis of cerebral gray and white matter based on the segmentation technique for co-registered MRI and voxel-based statistical parametric analysis were performed. Correlation between 11C-acetate uptake and the lesion number in T1- and T2- weighted MR images were also assessed. RESULTS: The standardized uptake value (SUV) of 11C-acetate was increased in both white and gray matter in MS patients compared to HV. Voxel-based statistical analysis revealed a significantly increased SUV relative to that in the bilateral thalami (SUVt) in a broad area of white matter, particularly in the subcortical white matter of MS patients. The numbers of T2 lesions and T1 black holes were significantly correlated with SUV of (11)C-acetate in white and gray matter. CONCLUSIONS: The 11C-acetate uptake significantly increased in MS patients and correlated to the number of MRI lesions. These preliminary data suggest that (11)C-acetate PET can be a useful clinical examination for MS patients.

| S-EPMC4219725 | biostudies-literature

11C-Labeling of Aryl Ketones as Candidate Histamine Subtype-3 Receptor PET Radioligands through Pd(0)-Mediated 11C-Carbonylative Coupling.

Project description:Pd(0)-mediated coupling between iodoarenes, [11C]carbon monoxide and aryltributylstannanes has been used to prepare simple model [11C]aryl ketones. Here, we aimed to label four 2-aminoethylbenzofuran chemotype based molecules ([11C]1-4) in the carbonyl position, as prospective positron emission tomography (PET) radioligands for the histamine subtype 3 receptor (H3R) by adapting this methodology with use of aryltrimethylstannanes. Radiosynthesis was successfully performed on a platform equipped with a mini-autoclave and a liquid handling robotic arm, within a lead-shielded hot-cell. Candidate radioligands were readily formulated in saline containing ethanol (10%, v/v) and ascorbic acid (0.5 mg/10 mL). Yields for preclinical use were in the range of 5-9%, decay-corrected from cyclotron-produced [11C]CO₂ and molar activities were >115 GBq/µmol at end of synthesis. Radiochemical purities exceeded >97%.

| S-EPMC5530730 | biostudies-literature

11C-hydroxy-ephedrine-PET/CT in the Diagnosis of Pheochromocytoma and Paraganglioma.

Project description:Pheochromocytomas (PCC) and paragangliomas (PGL) may be difficult to diagnose because of vague and uncharacteristic symptoms and equivocal biochemical and radiological findings. This was a retrospective cohort study in 102 patients undergoing 11C-hydroxy-ephedrine (11C-HED)-PET/CT because of symptoms and/or biochemistry suspicious for PCC/PGL and/or with radiologically equivocal adrenal incidentalomas. Correlations utilized CT/MRI, clinical, biochemical, surgical, histopathological and follow-up data. 11C-HED-PET/CT correctly identified 19 patients with PCC and six with PGL, missed one PCC, attained one false positive result (nodular hyperplasia) and correctly excluded PCC/PGL in 75 patients. Sensitivity, specificity, positive and negative predictive values of 11C-HED-PET/CT for PCC/PGL diagnosis was 96%, 99%, 96% and 99%, respectively. In 41 patients who underwent surgical resection and for whom correlation to histopathology was available, the corresponding figures were 96%, 93%, 96% and 93%, respectively. Tumor 11C-HED-uptake measurements (standardized uptake value, tumor-to-normal-adrenal ratio) were unrelated to symptoms of catecholamine excess (p > 0.05) and to systolic blood pressure (p > 0.05). In PCC/PGL patients, norepinephrine and systolic blood pressure increased in parallel (R2 = 0.22, p = 0.016). 11C-HED-PET/CT was found to be an accurate tool to diagnose and rule out PCC/PGL in complex clinical scenarios and for the characterization of equivocal adrenal incidentalomas. PET measurements of tumor 11C-HED uptake were not helpful for tumor characterization.

| S-EPMC6627429 | biostudies-literature

[11C]acetate PET as a tool for diagnosis of liver steatosis.

Project description:PURPOSE:To investigate [11C]acetate PET-surrogate parameter of fatty acid synthase activity-as suitable tool for diagnosis and monitoring of liver steatosis. METHODS:In this retrospective study, data were obtained from 83 prostatic carcinoma patients from 1/2008 to 1/2014. Mean HU was calculated from unenhanced CT of all patients from liver with liver HU less than 40 as threshold for liver steatosis. SUVmax of the liver and of the blood pool in thoracic aorta (as background for calculation of a liver/background ratio [SUVl/b]) was measured. t test was used with a P?<?0.05 considered as statistically significant difference and ROC analysis was used for calculating specificity and sensitivity. RESULTS:19/83 patients (20%) had diagnosis of hepatic steatosis according to CT. Uptake of [11C]acetate was significantly higher in patients with hepatic steatosis as compared to control group (SUVmax 7.96?±?2.0 vs. 5.48?±?2.3 [P?<?0.001]). There was also a significant correlation between both SUVmax (r?=?- 0.52, P?<?0.001) and SUVl/b (r?=?- 0.59, P?<?0.001) with the density (HU) of the liver. In ROC analysis for detection of liver steatosis SUVmax (threshold: 5.86) had a sensitivity of 94% and specificity of 69% with an AUC of 0.81. Increasing body mass index is correlated with the severity of steatosis. CONCLUSION:We showed for the first time that hepatic steatosis associates with increased [11C]acetate uptake. Also, severity of steatosis correlates with [11C]acetate uptake. [11C]acetate uptake PET seems promising for the assessment of liver steatosis.

| S-EPMC6208816 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data