A comparative analysis of strain and shear wave elastography in differentiating thyroid nodules

Background: Ultrasonography alone often demonstrates limited accuracy in diagnosing thyroid malignancy. While Strain Elastography (SE) is sensitive and specific, it remains highly operator-dependent. In contrast, shear wave elastography (SWE) provides quantitative tissue stiffness measurements, potentially improving reproducibility. This study aims to directly compare the diagnostic accuracy of SE and SWE in differentiating benign from malignant thyroid nodules, using cytological or histological results as reference standards. Methods: We prospectively evaluated 281 patients presenting with thyroid nodules. All nodules were classified based on the ACR-TIRADS ultrasound criteria. Of these, 88 nodules meeting criteria for further assessment underwent fine-needle aspiration (FNA). Each nodule was evaluated with both SE and SWE, and stiffness was quantified by calculating the strain ratio (E2/E1), comparing nodule stiffness to the adjacent sternocleidomastoid muscle. Logistic regression and ROC curve analyses were used to determine the predictive accuracy of each elastography method. Results: A strong correlation was observed between SE and SWE (Spearman’s r = 0.363, p < 0.001). ROC analysis revealed comparable performance between SE (sensitivity 80%, specificity 93%) and SWE (sensitivity 80%, specificity 96%), with no significant difference in diagnostic accuracy (ΔAUC = 0.017, p = 0.552). Combining both elastographic techniques yielded an area under the ROC curve (AUC) of 0.906 (95% CI 0.825–0.958), without statistically significant superiority over either method alone. Conclusions: SE and SWE demonstrate comparable diagnostic accuracy in distinguishing benign from malignant thyroid nodules, both significantly outperforming conventional ultrasonography. Given their equivalent effectiveness, the choice between SE and SWE may be guided by available resources and expertise, particularly in contexts already utilizing ACR-TIRADS criteria.