Background: Long-term monitoring of benign thyroid nodules is not addressed in the present American Thyroid Association guidelines. The objective of this study was to determine the appropriate nature and length of follow-up for patients with a benign thyroid nodule.
Study Design: A retrospective review was performed of all patients referred to single endocrine surgeon for evaluation of thyroid nodules between 2006 and 2012. The review included 263 patients who had benign fine needle aspiration (FNA) cytology and either underwent thyroidectomy or had at least a 1-year follow-up ultrasound. Main outcomes measures were repeat FNA and pathology results.
Results: There were 231 women and 32 men. Forty-eight patients underwent immediate thyroidectomy, with pathology showing 2 papillary thyroid cancers (PTC), and 215 patients were followed with annual ultrasounds. During follow-up, 89 (41.3%) nodules underwent repeat FNA after initial biopsy. The repeat FNA cytology showed 91% benign, 7% follicular neoplasm, and 2% PTC. During follow-up, 81 (37.6%) patients underwent thyroidectomy after 3.3±2.8 years. Reasons for surgery included development of symptoms in 58 (71.6%), a non-benign repeat FNA in 8 (9.8%), or patient preference in 15 (18.5%). Surgical pathology identified 70 (86.4%) benign, 7 (8.6%) PTC, 3 (4%) follicular thyroid cancers, and 1 (1.2%) lymphoma. Median time from initial FNA to thyroidectomy in patients who had malignancy was 4.3 years. The maximum initial nodule size and average increase in nodule size did not differ between benign and malignant nodules (p=0.54 and p=0.75, respectively).
Conclusions: Significant numbers of benign thyroid nodules enlarge more than 5 mm over 3 years, triggering repeat FNA or thyroidectomy. Larger diameter nodules and more rapidly growing nodules were not predictive of malignancy. The practice of annually obtaining ultrasound for benign thyroid nodules should be discouraged.
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http://dx.doi.org/10.1016/j.jamcollsurg.2014.12.010 | DOI Listing |
Med Sci Monit
December 2024
Department of Ultrasound Diagnosis, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
BACKGROUND Solitary thyroid nodules present a challenge in differentiating between benign and malignant conditions using ultrasound (US). Arrival time parameter imaging (At-PI) following contrast-enhanced ultrasound (CEUS) can effectively visualize the vascular architectural patterns of the nodules, providing valuable diagnostic information. This study aimed to explore the application value of At-PI in differentiating thyroid nodules, specifically focusing on a sample of 127 cases.
View Article and Find Full Text PDFIr J Med Sci
December 2024
School of Medicine, University College of Cork, Cork, Ireland.
Background: The majority of thyroid nodules are benign; however current guidelines suggest that thyroid incidentalomas should be appropriately evaluated to rule out malignancy.
Aims: This study aims to determine the incidence of thyroid incidentalomas and the likelihood that they harbour sinister pathology in the largest Irish cohort studied to-date.
Methods: A retrospective observational chart review was conducted using data from July 2018 to December 2018 using the Radiology Database in use at Cork University Hospital.
Ann Ital Chir
December 2024
Radiotherapy Department, Affiliated Hospital of Hebei University, 071003 Baoding, Hebei, China.
Diagn Cytopathol
December 2024
Department of Pathology, Dr Ram Manohar Lohia Institute of Medical Sciences, Lucknow, Uttar Pradesh, India.
Introduction: Thyroid lesions are one of the most common diseases observed in clinical practice in the North India. These diseases have distinct cytological morphology and thus FNAC is done frequently. Here we report a case of adenomatoid goitre mimicking adenoid cystic carcinoma (ACC) of salivary gland on cytology.
View Article and Find Full Text PDFUltrasound Med Biol
December 2024
School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing, China. Electronic address:
Objective: Breast ultrasound (BUS) is used to classify benign and malignant breast tumors, and its automatic classification can reduce subjectivity. However, current convolutional neural networks (CNNs) face challenges in capturing global features, while vision transformer (ViT) networks have limitations in effectively extracting local features. Therefore, this study aimed to develop a deep learning method that enables the interaction and updating of intermediate features between CNN and ViT to achieve high-accuracy BUS image classification.
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