Evaluation of Ultrasound Elastography Combined With Chi-Square Automatic Interactive Detector in Reducing Unnecessary Fine-Needle Aspiration on TIRADS 4 Thyroid Nodules

Conventional ultrasound diagnosis of thyroid nodules (TNs) had a high false-positive rate, resulting in many unnecessary fine-needle aspirations (FNAs). This study aimed to establish a simple algorithm to reduce unnecessary FNA on TIRADS 4 TNs using different quantitative parameters of ultrasonic el...

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Published inFrontiers in oncology Vol. 12; p. 823411
Main Authors Liu, Xiao, Xie, Li, Ye, Xianjun, Cui, Yayun, He, Nianan, Hu, Lei
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 16.02.2022
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ISSN2234-943X
2234-943X
DOI10.3389/fonc.2022.823411

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Summary:Conventional ultrasound diagnosis of thyroid nodules (TNs) had a high false-positive rate, resulting in many unnecessary fine-needle aspirations (FNAs). This study aimed to establish a simple algorithm to reduce unnecessary FNA on TIRADS 4 TNs using different quantitative parameters of ultrasonic elasticity and chi-square automatic interactive detector (CHAID) method. From January 2020 to May 2021, 432 TNs were included in the study, which were confirmed by FNA or surgical pathology. Each TN was examined using conventional ultrasound, sound touch elastography, and Shell measurement function. The quantitative parameters and were recorded, and the / values were calculated for each TN. The diagnostic performance of the quantitative parameters was evaluated using the receiver operating characteristic curves. The CHAID was used to classify and analyze the quantitative parameters, and the prediction model was established. A total of 226 TNs were malignant and 206 were benign. and / ratio were included in the classification algorithm, which showed a depth of two ramifications ( / ≤ 0.988 or 0.988-1.043 or >1.043; if / ≤ 0.988, then ≤ 64.0 or 64.0-74.0 or >74.0; if / = 0.988-1.043, then ≤ 66.0 or > 66.0; if / >1.043, then ≤ 69.0 or >69.0). The unnecessary FNAs could have been avoided in 57.3% of the cases using this algorithm. The prediction model using quantitative parameters had high diagnostic performance; it could quickly distinguish benign lesions and avoid subjective influence to some extent.
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This article was submitted to Cancer Imaging and Image-directed Interventions, a section of the journal Frontiers in Oncology
Edited by: Po-Hsiang Tsui, Chang Gung University, Taiwan
Reviewed by: Lei Zhu, Fifth Affiliated Hospital of Wenzhou Medical University, China; Pietro Locantore, Catholic University of the Sacred Heart, Italy; Zhimin Ding, Southern University of Science and Technology, China; Yijie Dong, Shanghai Jiaotong University School of Medicine, China
ISSN:2234-943X
2234-943X
DOI:10.3389/fonc.2022.823411