Diagnostic reliability of the thyroid imaging reporting and data system (TI-RADS) in routine practice

Allen San Shell Jabar, Prakashini Koteshwara, Jasbon Andrade

Research output: Contribution to journalArticle

Abstract

Purpose: To evaluate the diagnostic reliability of Thyroid Imaging Reporting and Data System (TI-RADS) classifica-tions described by American College of Radiology (ACR) and Kwak et al. by calculating the risk of malignancy, to assess the role of TI-RADS in reducing fine-needle aspiration cytology (FNAC) of benign lesions. Material and methods: This was a prospective study during the period from December 2017 to August 2018. Thyroid nodules were classified using ACR TI-RADS and TI-RADS proposed by Kwak et al. The TI-RADS categorisations were compared to the final diagnosis obtained by cytopathological/histopathological analysis. The risk of malignancy for each category was calculated. Sensitivity, specificity, and positive and negative predictive values for individual suspicious ultrasound features were also assessed. Results: We evaluated a total of 127 thyroid nodules. The risk of malignancy was 0% in ACR TR1, 0% in ACR TR2, 6.9% in ACR TR3, 29.2% in ACR TR4, and 80% in ACR TR5 categories. The risk of malignancy for TI-RADS according to Kwak et al. were 0%, 0%, 21.5%, 32.4%, 100% for TI-RADS 2, 3, 4A, 4B, and 4C categories, respectively. Kwak TI-RADS 2 and 3 had higher sensitivity in predicting benignity compared to ACR TR1 and 2 (35.4% vs. 25.9%). Conclusions: We found TI-RADS classification to be a reliable, non-invasive, and practical method for assessing thyroid nodules in routine practice. TI-RADS can safely avert avoidable FNACs in a significant proportion of benign thyroid lesions.

Original languageEnglish
Pages (from-to)274-280
Number of pages7
JournalPolish Journal of Radiology
Volume84
DOIs
Publication statusPublished - 01-01-2019

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Information Systems
Thyroid Gland
Radiology
Thyroid Nodule
Neoplasms
Fine Needle Biopsy
Cell Biology
Prospective Studies
Sensitivity and Specificity

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

Cite this

@article{d73ee590ad2e45d18b2a4c2ddf4ab1b8,
title = "Diagnostic reliability of the thyroid imaging reporting and data system (TI-RADS) in routine practice",
abstract = "Purpose: To evaluate the diagnostic reliability of Thyroid Imaging Reporting and Data System (TI-RADS) classifica-tions described by American College of Radiology (ACR) and Kwak et al. by calculating the risk of malignancy, to assess the role of TI-RADS in reducing fine-needle aspiration cytology (FNAC) of benign lesions. Material and methods: This was a prospective study during the period from December 2017 to August 2018. Thyroid nodules were classified using ACR TI-RADS and TI-RADS proposed by Kwak et al. The TI-RADS categorisations were compared to the final diagnosis obtained by cytopathological/histopathological analysis. The risk of malignancy for each category was calculated. Sensitivity, specificity, and positive and negative predictive values for individual suspicious ultrasound features were also assessed. Results: We evaluated a total of 127 thyroid nodules. The risk of malignancy was 0{\%} in ACR TR1, 0{\%} in ACR TR2, 6.9{\%} in ACR TR3, 29.2{\%} in ACR TR4, and 80{\%} in ACR TR5 categories. The risk of malignancy for TI-RADS according to Kwak et al. were 0{\%}, 0{\%}, 21.5{\%}, 32.4{\%}, 100{\%} for TI-RADS 2, 3, 4A, 4B, and 4C categories, respectively. Kwak TI-RADS 2 and 3 had higher sensitivity in predicting benignity compared to ACR TR1 and 2 (35.4{\%} vs. 25.9{\%}). Conclusions: We found TI-RADS classification to be a reliable, non-invasive, and practical method for assessing thyroid nodules in routine practice. TI-RADS can safely avert avoidable FNACs in a significant proportion of benign thyroid lesions.",
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Diagnostic reliability of the thyroid imaging reporting and data system (TI-RADS) in routine practice. / Jabar, Allen San Shell; Koteshwara, Prakashini; Andrade, Jasbon.

In: Polish Journal of Radiology, Vol. 84, 01.01.2019, p. 274-280.

Research output: Contribution to journalArticle

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