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甲状腺超声的观察者间变异性。

Interobserver variability in thyroid ultrasound.

机构信息

Endocrinology Department, Hospital Universitario de Navarra, Pamplona, Navarra, Spain.

Instituto de Investigación Sanitaria de Navarra, Pamplona, Navarra, Spain.

出版信息

Endocrine. 2024 Aug;85(2):730-736. doi: 10.1007/s12020-024-03731-5. Epub 2024 Feb 19.


DOI:10.1007/s12020-024-03731-5
PMID:38372907
Abstract

PURPOSE: Ultrasound evaluation of thyroid nodules is the preferred technique, but it is dependent on operator interpretation, leading to inter-observer variability. The current study aimed to determine the inter-physician consensus on nodular characteristics, risk categorization in the classification systems, and the need for fine needle aspiration puncture. METHODS: Four endocrinologists from the same center blindly evaluated 100 ultrasound images of thyroid nodules from 100 different patients. The following ultrasound features were evaluated: composition, echogenicity, margins, calcifications, and microcalcifications. Nodules were also classified according to ATA, EU-TIRADS, K-TIRADS, and ACR-TIRADS classifications. Krippendorff's alpha test was used to assess interobserver agreement. RESULTS: The interobserver agreement for ultrasound features was: Krippendorff's coefficient 0.80 (0.71-0.89) for composition, 0.59 (0.47-0.72) for echogenicity, 0.73 (0.57-0.88) for margins, 0.55 (0.40-0.69) for calcifications, and 0.50 (0.34-0.67) for microcalcifications. The concordance for the classification systems was 0.7 (0.61-0.80) for ATA, 0.63 (0.54-0.73) for EU-TIRADS, 0.64 (0.55-0.73) for K-TIRADS, and 0.68 (0.60-0.77) for K-TIRADS. The concordance in the indication of fine needle aspiration puncture (FNA) was 0.86 (0.71-1), 0.80 (0.71-0.88), 0.77 0.67-0.87), and 0.73 (0.64-0.83) for systems previously described respectively. CONCLUSIONS: Interobserver agreement was acceptable for the identification of nodules requiring cytologic study using various classification systems. However, limited concordance was observed in risk stratification and many ultrasonographic characteristics of the nodules.

摘要

目的:甲状腺结节的超声评估是首选技术,但它依赖于操作者的解释,导致观察者间的变异性。本研究旨在确定不同医生对结节特征、分类系统中的风险分类以及细针抽吸穿刺的需求的一致性。

方法:来自同一中心的 4 名内分泌学家对来自 100 名不同患者的 100 个甲状腺结节的超声图像进行了盲法评估。评估了以下超声特征:成分、回声、边缘、钙化和微钙化。结节还根据 ATA、EU-TIRADS、K-TIRADS 和 ACR-TIRADS 分类进行分类。Krippendorff 的 alpha 检验用于评估观察者间的一致性。

结果:超声特征的观察者间一致性为:成分的 Krippendorff 系数为 0.80(0.71-0.89),回声为 0.59(0.47-0.72),边缘为 0.73(0.57-0.88),钙化为 0.55(0.40-0.69),微钙化为 0.50(0.34-0.67)。分类系统的一致性为:ATA 为 0.7(0.61-0.80),EU-TIRADS 为 0.63(0.54-0.73),K-TIRADS 为 0.64(0.55-0.73),K-TIRADS 为 0.68(0.60-0.77)。细针抽吸穿刺(FNA)指征的一致性分别为 0.86(0.71-1)、0.80(0.71-0.88)、0.77(0.67-0.87)和 0.73(0.64-0.83)。

结论:使用各种分类系统识别需要细胞学研究的结节时,观察者间的一致性是可以接受的。然而,在风险分层和结节的许多超声特征方面观察到的一致性有限。

相似文献

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[2]
Inter- and Intraobserver Agreement in the Assessment of Thyroid Nodule Ultrasound Features and Classification Systems: A Blinded Multicenter Study.

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[3]
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J Med Life. 2024-6

[4]
Interrater Reliability of Various Thyroid Imaging Reporting and Data System (TIRADS) Classifications for Differentiating Benign from Malignant Thyroid Nodules.

Asian Pac J Cancer Prev. 2019-4-29

[5]
Comparison of Thyroid Risk Categorization Systems and Fine-Needle Aspiration Recommendations in a Multi-Institutional Thyroid Ultrasound Registry.

J Am Coll Radiol. 2021-12

[6]
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[7]
Prevalence of hyperfunctioning thyroid nodules among those in need of fine needle aspiration cytology according to ATA 2015, EU-TIRADS, and ACR-TIRADS.

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[8]
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[9]
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[10]
Diagnostic Performance of Thyroid Nodule Risk Stratification Systems: Comparison of ACR-TIRADS, EU-TIRADS, K-TIRADS, and ATA Guidelines.

Ultrasound Q. 2023-12-1

引用本文的文献

[1]
Thyroid Nodule Characterization: Which Thyroid Imaging Reporting and Data System (TIRADS) Is More Accurate? A Comparison Between Radiologists with Different Experiences and Artificial Intelligence Software.

Diagnostics (Basel). 2025-8-21

[2]
Risk Stratification of Thyroid Nodules Using Ultrasound Cine-Loop Video Sequences.

Cancers (Basel). 2025-8-9

[3]
Exploring the Potential of ChatGPT-4o in Thyroid Nodule Diagnosis Using Multi-Modality Ultrasound Imaging: Dual- vs. Triple-Modality Approaches.

Cancers (Basel). 2025-6-20

[4]
Inconclusive cytology results of fine-needle aspiration for thyroid nodules: the importance of strict guideline implementation.

Ultrasonography. 2025-7

[5]
Molecular Landscape and Therapeutic Strategies in Pediatric Differentiated Thyroid Carcinoma.

Endocr Rev. 2025-5-9

[6]
Assessing the feasibility of ChatGPT-4o and Claude 3-Opus in thyroid nodule classification based on ultrasound images.

Endocrine. 2025-3

[7]
Evaluating diagnostic accuracy and agreement of TI-RADS scoring in thyroid nodules: A comparative analysis between sonographers and radiologists.

PLoS One. 2024

本文引用的文献

[1]
Considerable interobserver variation calls for unambiguous definitions of thyroid nodule ultrasound characteristics.

Eur Thyroid J. 2023-4-1

[2]
Classification of Thyroid Nodules by Using Deep Learning Radiomics Based on Ultrasound Dynamic Video.

J Ultrasound Med. 2022-12

[3]
Reproducibility and Interobserver Agreement of Different Thyroid Imaging and Reporting Data Systems (TIRADS).

Eur Thyroid J. 2021-4

[4]
Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study.

Lancet Digit Health. 2021-4

[5]
Unnecessary thyroid nodule biopsy rates under four ultrasound risk stratification systems: a systematic review and meta-analysis.

Eur Radiol. 2021-5

[6]
Inter- and Intraobserver Agreement in the Assessment of Thyroid Nodule Ultrasound Features and Classification Systems: A Blinded Multicenter Study.

Thyroid. 2020-2

[7]
Predictive Value of Malignancy of Thyroid Nodule Ultrasound Classification Systems: A Prospective Study.

J Clin Endocrinol Metab. 2018-4-1

[8]
Interobserver agreement of various thyroid imaging reporting and data systems.

Endocr Connect. 2018-1

[9]
European Thyroid Association Guidelines for Ultrasound Malignancy Risk Stratification of Thyroid Nodules in Adults: The EU-TIRADS.

Eur Thyroid J. 2017-9

[10]
ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee.

J Am Coll Radiol. 2017-5

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