Ultrasound in medicine & biology · 2025
Enhancing Thyroid Nodule Characterization: Integrating TI-RADS With Ultrasound Viscosity Imaging for Improved Diagnostic Accuracy.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wang Lin, Xuan Zhidong, Qian Xingyu, Cai Chang, Xu Mengting, Qin Yue
Аннотация
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.
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.