Objective: Liquid-based (LB)-FNA is widely recognized as a reliable diagnostic method to evaluate thyroid nodules. However, up to 30% of LB-FNA remain indeterminate according to the Bethesda system. Use of molecular biomarkers has been recommended to improve its pathological accuracy but implementation of these tests in clinical practice may be difficult. Here, we evaluated feasibility and performance of molecular profiling in routine practice by testing LB-FNA for BRAF, N/HRAS and TERT mutations.

Methods: We studied a large prospective cohort of 326 cases, including 61 atypia of undetermined significance, 124 follicular neoplasms, 72 suspicious for malignancy and 69 malignant cases. Diagnosis of malignancy was confirmed by histology on paired surgical specimen.

Results: Mutated LB-FNAs were significantly associated with malignancy regardless of the cytological classification. Overall sensitivity was 60% and specificity 89%. Importantly, in atypia of undetermined significance and follicular neoplasm patients undergoing surgery according to the Bethesda guidelines, negative predictive values were 85.4% and 90% respectively. TERT promoter mutation was rare but very specific for malignancy (5.5%) suggesting that it could be of interest in patients with indeterminate cytology.

Conclusions: Mutation profiling can be successfully performed on thyroid LB-FNA without any dedicated sample in a pathology laboratory. It is an easy way to improve diagnostic accuracy of routine LB-FNA and may help to better select patients for surgery and to avoid unnecessary thyroidectomies.

Download full-text PDF

Source
http://dx.doi.org/10.1111/cyt.12493DOI Listing

Publication Analysis

Top Keywords

diagnostic accuracy
8
atypia undetermined
8
undetermined significance
8
lb-fna
5
molecular testing
4
testing braf
4
braf ras
4
ras tert
4
tert thyroid
4
thyroid fnas
4

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 PDF

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 PDF

Bibliometric 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.

View Article and Find Full Text PDF

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 PDF

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 PDF

Want 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!