Interobserver agreement and efficacy of consensus reading in Kwak-, EU-, and ACR-thyroid imaging recording and data systems and ATA guidelines for the ultrasound risk stratification of thyroid nodules

Endocrine ◽  
2019 ◽  
Vol 67 (1) ◽  
pp. 143-154 ◽  
Author(s):  
Philipp Seifert ◽  
Rainer Görges ◽  
Michael Zimny ◽  
Michael C. Kreissl ◽  
Simone Schenke
Keyword(s):  
Risk Stratification ◽  
Interobserver Agreement ◽  
Thyroid Nodules ◽  
Data Systems ◽  
Thyroid Imaging ◽  
Consensus Reading

scholarly journals Diagnostic Performance of Different Thyroid Imaging Reporting and Data Systems (Kwak-TIRADS, EU-TIRADS and ACR TI-RADS) for Risk Stratification of Small Thyroid Nodules (≤10 mm)

2020 ◽  
Vol 9 (1) ◽  
pp. 236 ◽  
Author(s):  
Simone Schenke ◽  
Rigobert Klett ◽  
Philipp Seifert ◽  
Michael C. Kreissl ◽  
Rainer Görges ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Cohort Analysis ◽  
Medullary Carcinoma ◽  
Data Systems ◽  
Thyroid Imaging ◽  
Promising Tool ◽  
Risk Of Malignancy

Due to the widespread use of ultrasound, small thyroid nodules (TNs) ≤ 10 mm are common findings. Standardized approaches for the risk stratification of TNs with Thyroid Imaging Reporting and Data Systems (TIRADS) were evaluated for the clinical routine. With TIRADS, the risk of malignancy in TNs is calculated by scoring the number or combination of suspicious ultrasound features, leading to recommendations for further diagnostic steps. However, there are only scarce data on the performance of TIRADS for small TNs. The aim was to compare three different TIRADS for risk stratification of small TNs in routine clinical practice. We conducted a retrospective cohort analysis of TNs ≤ 10 mm and their available histology. Nodules were classified according to three different TIRADS. In the study, 140 patients (n = 113 female) with 145 thyroid nodules (n = 76 malignant) were included. Most of the malignant nodules were papillary carcinoma (97%), and the remaining 3% were medullary carcinoma. For all tested TIRADS, the prevalence of malignancy rose with increasing category levels. The highest negative predictive value was found for ACR TI-RADS and the highest positive predictive value for Kwak-TIRADS. All tested variants of TIRADS showed comparable diagnostic performance for the risk stratification of small TNs. TIRADS seems to be a promising tool to reliably assess the risk of malignancy of small TNs.

scholarly journals Reproducibility and Interobserver Agreement of Different Thyroid Imaging and Reporting Data Systems (TIRADS)

2020 ◽  
pp. 1-7
Author(s):  
Yulia P. Sych ◽  
Valentin V. Fadeev ◽  
Elena P. Fisenko ◽  
Marina Kalashnikova
Keyword(s):  
Interobserver Agreement ◽  
Data Systems ◽  
Thyroid Imaging ◽  
Reporting Data

Thyroid Imaging Reporting and Data System Risk Stratification of Thyroid Nodules: Categorization Based on Solidity and Echogenicity

Thyroid ◽  
2016 ◽  
Vol 26 (4) ◽  
pp. 562-572 ◽  
Author(s):  
Dong Gyu Na ◽  
Jung Hwan Baek ◽  
Jin Yong Sung ◽  
Ji-Hoon Kim ◽  
Jae Kyun Kim ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Thyroid Nodules ◽  
Data System ◽  
Thyroid Imaging ◽  
System Risk

A Comparison of Different Thyroid Imaging Reporting and Data Systems to Reduce Unnecessary FNAs and Missed Malignancies

2021 ◽  
pp. 875647932110550
Author(s):  
Ehsan Seif ◽  
Mostafa Qorbani ◽  
Shaghayegh Mousavi ◽  
Ali Salahshoor
Keyword(s):  
Artificial Intelligence ◽  
Thyroid Nodules ◽  
Scoring Systems ◽  
Data System ◽  
Data Systems ◽  
Fine Needle ◽  
Conservative Method ◽  
Thyroid Imaging ◽  
The Eu

Objective: The Thyroid Imaging Reporting and Data System (TIRADS) has been proposed to reduce the number of unnecessary fine needle aspirations (FNA) from thyroid nodules. Materials and Methods: An individual radiologist provided sonographic examinations and FNA on a collection of 188 thyroid nodules. The recommendations based on the TIRADS system, for each nodule, was determined and evaluated against the cytology results. Results: The American College of Radiology (ACR), artificial intelligence (AI), European (EU), and Korean (K) scoring systems reduced FNAs by 53%, 56%, 48%, and 28%, respectively. Among those lesions without a recommendation for immediate FNA, The ACR would have missed four malignant nodules, the AI would have missed four malignant nodules, and K TIRADS would have missed three malignant nodules but with a recommended follow-up imaging. The ACR would have missed three malignant nodules, the AI would have missed four malignant nodules, and EU TIRADS would have missed four malignant nodules, without a recommended follow-up examination. The highest and lowest kappa interrelated agreements were between ACR and AI (0.902) and AI and K (0.448). Conclusion: The ACR and AI TIRADS could substantially decrease the number of FNAs but rely on follow-up imaging. The EU TIRADS reduced the number of FNAs, the least however this system had less dependence on follow-up imaging. The K TIRADS was the most conservative method and the least dependent on follow-up diagnostics.

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