医学
接收机工作特性
甲状腺结节
结核(地质)
切断
恶性肿瘤
放射科
甲状腺
前瞻性队列研究
超声波
队列
活检
曲线下面积
核医学
内科学
生物
物理
古生物学
量子力学
作者
Lin Wang,Zhidong Xuan,Xiran Qian,Cai Chang,Mengting Xu,Yue Qin
标识
DOI:10.1016/j.ultrasmedbio.2025.07.019
摘要
OBJECTIVE: To investigate and validate the clinical utility of the Thyroid Imaging Reporting and Data System (TI-RADS) combined with ultrasound viscosity imaging for differentiating benign and malignant thyroid nodules. METHODS: This prospective diagnostic study enrolled 437 consecutive patients with 437 thyroid nodules referred to our institution between February 2022 and August 2024. Participants were stratified into a development cohort (DC, n = 306) and a validation cohort (VC, n = 131) based on enrollment chronology. Nodules were further categorized into large (>1 cm), small (≤1 cm) and overall cohorts for subgroup analysis. Using histopathologic results from surgical excision or core needle biopsy as the reference standard, we identified the optimal viscosity-related diagnostic parameter by comparing receiver operating characteristic (ROC) curve-derived area under the curve (AUC) values. The diagnostic performance of TI-RADS alone versus TI-RADS combined with the optimal parameter was systematically evaluated and validated. RESULTS: Demographic characteristics (gender, mean age) and nodule size distributions showed no significant differences between DC and VC groups (all p > 0.05). In the DC, S1-Vmax (The maximum viscosity value of shell1mm around the nodule) emerged as the optimal parameter for malignancy differentiation, achieving an AUC of 0.831 (95% CI: 0.783-0.885) with an optimal cutoff value of 3.52 Pa·s. The integrated model combining S1-Vmax and TI-RADS significantly outperformed TI-RADS alone in diagnostic accuracy across all cohorts (overall, large and small nodules) within the DC (all p < 0.05), with consistent improvement validated in the VC. CONCLUSION: S1-Vmax demonstrates superior diagnostic performance as a viscosity-based parameter for thyroid nodule characterization. The integration of TI-RADS with USVI significantly enhances the differentiation accuracy between benign and malignant thyroid nodules, providing a clinically actionable tool for risk stratification.
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