Evidence map›Paper›PMID 42582462›Full record

ArticleFrontiers in endocrinology2026

Efficiency of 2017 ACR-TIRADS combined with contrast-enhanced ultrasound in diagnosing thyroid malignant nodules.

Zhiqun Bai, Xi Chen, Yi Fang, Xuemei Wang, Zhen Zhang, Zhiguang Chen

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Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Zhiqun BaiDepartment of Ultrasonic Diagnosis, the First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Xi ChenDepartment of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Yi FangDepartment of Ultrasonic Diagnosis, the First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Xuemei WangDepartment of Ultrasonic Diagnosis, the First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Zhen ZhangDepartment of Ultrasonic Diagnosis, the First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Zhiguang ChenDepartment of Ultrasonic Diagnosis, the First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The aim of this study was to compare the efficiency of 2017 ACR-TIRADS and contrast-enhanced ultrasound (CEUS) in diagnosing malignant thyroid nodules, and propose a new classification system based on CEUS and 2017 ACR-TIRADS. Methods: A total of 788 thyroid nodules, which were examined by conventional ultrasound and CEUS, were analyzed. The efficacy of contrast parameters in the diagnosis of benign and malignant thyroid nodules was analyzed by univariate analysis and logistic regression. The contrast parameters were assigned based on the results of logistic regression analysis, and subsequently, a new classification system was proposed. Results: 1) The result of univariate analysis showed that non-uniform enhancement, hyper-enhancement, fast entry, and washout were significant differences between benign and malignant nodules. 2) When taking ≥ 7 points as the cutoff value for diagnosing malignant thyroid nodules, the sensitivity and specificity of the 2017 ACR-TIRADS were 87.27% and 55.06%, respectively; whereas the sensitivity and specificity of the combined CEUS and ACR-TIRADS were 86.41% and 57.30%, respectively; 3) By incorporating the 2 points derived from CEUS into the 2017 ACR-TIRADS assignment process, a new grading system, namely CEUS-TIRADS, was established. The sensitivity and specificity of CEUS-TIRADS were 87.27% and 57.30% respectively. Conclusion: The combined application of CEUS and 2017 ACR-TIRADS demonstrated better diagnostic efficiency than their individual application. Incremental improvement in diagnostic specificity and Youden index compared with their individual application, although the difference did not reach statistical significance. The CEUS-TIRADS offers a preliminary scoring approach and a new research direction for future prospective studies to analyze quantitative and qualitative CEUS data and establish a validated CEUS classification system for thyroid nodules. Advances in knowledge: The combined application of CEUS and 2017 ACR-TIRADS provides a preliminary scoring framework and a promising research direction for the future development of a validated CEUS-based classification system for thyroid nodules, pending external validation in larger cohorts.

Indexed as

Contrast MediaThyroid NeoplasmsThyroid NoduleAdultAgedFemaleHumansMaleMiddle AgedSensitivity and SpecificityUltrasonographyContrast Media2017 ACR-TIRADScontrast-enhanced ultrasounddiagnostic accuracythyroid cancerthyroid nodulesultrasound

Identifiers

PMID42582462
PMCPMC13457465

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.