Background: Primary hyperparathyroidism is characterized by an autonomous hypersecretion of parathyroid hormone by one or more parathyroid glands. Preoperative localization of the affected gland(s) is of key importance in order to allow minimally invasive surgery. At the moment, 11C-Methionine and 18F-Fluorocholine PET studies appear to be among the most promising second-line localization techniques; their comparative diagnostic performance, however, is still unknown.
Methods: PubMed/Medline and Embase databases were searched up to October 2020 for studies estimating the diagnostic accuracy of 11C-Methionine PET or 18F-Fluorocholine PET for parathyroid localization in patients with primary hyperparathyroidism. Pooled sensitivity and positive predictive value were calculated for each tracer on a 'per-lesion' basis and compared using a random-effect model subgroup analysis.
Results: In total, 22Twenty-two studies were finally considered in the meta-analysis. Of these, 8 evaluated the diagnostic accuracy of 11C-Methionine and 14 that of 18F-Fluorocholine. No study directly comparing the two tracers was found. The pooled sensitivity of 18F-Fluorocholine was higher than that of 11C-Methionine (92% vs 80%, P < 0.01), while the positive predictive value was similar (94% vs 95%, P = 0.99). These findings were confirmed in multivariable meta-regression models, demonstrating their apparent independence from other possible predictors or confounders at a study level.
Conclusion: This was the first meta-analysis that specifically compared the diagnostic accuracy of 11C-Methionine and 18F-Fluorocholine PET for parathyroid localization in patients with primary hyperparathyroidism. Our results suggested a superior performance of 18F-Fluorocholine in terms of sensitivity, while the two tracers had comparable accuracy in terms of positive predictive value.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1530/EJE-21-0038 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics.
BMC Pulm Med
January 2025
Universal Scientific Education and Research Network (USERN), Tehran, Iran.
Objective: Lung cancer (LC), the primary cause for cancer-related death globally is a diverse illness with various characteristics. Saliva is a readily available biofluid and a rich source of miRNA. It can be collected non-invasively as well as transported and stored easily.
View Article and Find Full Text PDFAge group classification based on optical measurement of brain pulsation using machine learning.
Sci Rep
January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
View Article and Find Full Text PDFBibliometric analysis of global research trends in vestibular neuritis (1980-2024).
Eur Arch Otorhinolaryngol
January 2025
Faculty of Applied Sciences, Department of Accounting and Financial Management, Necmettin Erbakan University, Konya, Turkey.
Purpose: Vestibular neuritis (VN) is a common cause of vertigo with significant impact on patients' quality of life. This study aimed to analyze global research trends in VN using bibliometric methods to identify key themes, influential authors, institutions, and countries contributing to the field.
Methods: We conducted a comprehensive search of the Web of Science Core Collection database for publications related to VN from 1980 to 2024.
A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification.
NPJ Digit Med
January 2025
Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.
View Article and Find Full Text PDFMachine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.
Sci Rep
January 2025
Department of Anesthesiology and Surgical Intensive Care Unit, Kunming Children's Hospital, Kunming, Yunnan, China.
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!