Prediction of thyroid nodule malignancy using thyroid imaging reporting and data system (TIRADS) and nodule size

(2020) Prediction of thyroid nodule malignancy using thyroid imaging reporting and data system (TIRADS) and nodule size. Clin Imaging. pp. 222-227. ISSN 1873-4499 (Electronic) 0899-7071 (Linking)

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Abstract

OBJECTIVES: Thyroid imaging reporting and data system (TIRADS) is a combination of ultrasonographic features developed to help physicians in predicting the malignancy risk of thyroid nodules based on sonographic characteristics. Thyroid nodule size is another factor in determining whether a nodule is malignant. The aim of this study was detecting the predictive value of TIRADS and nodule size based on Bethesda classification in prognostication of malignancy. METHODS: This was a cross-sectional study of 239 patients with thyroid nodules. The patients underwent ultrasonography using TIRADS classification and FNA biopsy based on Bethesda categorization. The results were analyzed using SPSS with the cut off points and predictive values measured. RESULTS: TIRADS >/=4 could detect malignant nodules with a sensitivity of 91.67 and specificity of 52.8. An inverse relationship was observed between nodule size and malignancy risk and cutoff point of 12mm was found for detecting malignant nodules. CONCLUSIONS: Thyroid nodules with TIRADS 4 and 5 and diameter lower than 12mm, are highly suspicious for malignancy and should be considered as indications for fine needle aspiration biopsy. ADVANCES IN KNOWLEDGE: The study suggests TIRADS and thyroid nodule size as sensitive predictors of malignancy.

Item Type: Article
Keywords: Malignancy Pathologic size Thyroid nodules Ultrasound imaging
Subjects: QZ Pathology > QZ 140-180 Pathologic Processes
WN Radiology. Diagnostic Imaging > WN 180-240 Diagnostic Imaging
Divisions: Faculty of Medicine > Departments of Clinical Sciences > Department of Pathology
Faculty of Paramedical > Department of Radiology
Other
Page Range: pp. 222-227
Journal or Publication Title: Clin Imaging
Journal Index: Pubmed, ISI
Volume: 60
Number: 2
Identification Number: https://doi.org/10.1016/j.clinimag.2019.10.004
ISSN: 1873-4499 (Electronic) 0899-7071 (Linking)
Depositing User: Zahra Otroj
URI: http://eprints.mui.ac.ir/id/eprint/11780

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