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

BackgroundConventional ultrasound diagnosis of thyroid nodules (TNs) had a high false-positive rate, resulting in many unnecessary fine-needle aspirations (FNAs). ObjectiveThis 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. MethodsFrom 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 E and E-shell were recorded, and the E-shell/E 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. ResultsA total of 226 TNs were malignant and 206 were benign. E-shell and E-shell/E ratio were included in the classification algorithm, which showed a depth of two ramifications (E-shell/E <= 0.988 or 0.988-1.043 or >1.043; if E-shell/E <= 0.988, then E-shell <= 64.0 or 64.0-74.0 or >74.0; if E-shell/E = 0.988-1.043, then E-shell <= 66.0 or > 66.0; if E-shell/E >1.043, then E-shell <= 69.0 or >69.0). The unnecessary FNAs could have been avoided in 57.3% of the cases using this algorithm. ConclusionThe prediction model using quantitative parameters had high diagnostic performance; it could quickly distinguish benign lesions and avoid subjective influence to some extent.

Automated summary

This study aimed to establish a simple algorithm to reduce unnecessary FNA on TIRADS 4 thyroid nodules using different quantitative parameters of ultrasonic elasticity and the CHAID method. The prediction model using quantitative parameters had high diagnostic performance, quickly distinguishing benign lesions and minimizing subjective influence.