Chen Qiyang, Hu Minxia, Bao Feifan, Zhao Hanxue
Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Gland Surg. 2024 Jul 30;13(7):1188-1200. doi: 10.21037/gs-24-87. Epub 2024 Jul 24.
It is difficult to accurately assess the risk of Thyroid Imaging Reporting and Data System (TI-RADS) 4 thyroid nodules due to the overlap of benign and malignant conventional ultrasound (US) features of nodules. To reduce unnecessary needle biopsies and assist clinical decision-making, this study established a dynamic nomogram incorporating superb microvascular imaging (SMI) and shear wave elastography (SWE) for the risk evaluation of TI-RADS 4 thyroid nodules.
A total of 248 patients who underwent US, SMI, and SWE with cytological or histopathological results were included in this retrospective study, and were randomly divided into training (174 patients) and verification (74 patients) cohorts. The clinical characteristics and US, SMI, and SWE features of patients were analyzed in the training cohort. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to screen parameters and construct dynamic nomogram. The receiver operating characteristic (ROC) curves, calibration curve, and decision curve were used to evaluate the performance of the nomogram.
A dynamic nomogram was constructed based on age [odds ratio (OR) =0.954; P=0.005] , shape (OR =0.345; P=0.041), SMI (OR =9.511; P<0.001), and SWE (OR =3.670; P=0.001). The nomogram showed excellent discrimination both in the training [area under the curve (AUC): 0.848; 95% confidence interval (CI): 0.784-0.911] and validation (AUC: 0.862; 95% CI: 0.780-0.944) cohorts, and better than US, SMI, and SWE alone in all cohorts (P<0.05). The Nomo-score of each patient was calculated and the cut-off value was 0.607 which can be used to distinguish high-risk and low-risk patients.
The SMI and SWE show added predictive value on risk stratification in patients with TI-RADS 4 thyroid nodules and a dynamic nomogram was constructed to screen high-risk individuals and assist the clinical decision-making.
由于甲状腺影像报告和数据系统(TI-RADS)4类甲状腺结节的良恶性常规超声特征存在重叠,准确评估其风险较为困难。为减少不必要的穿刺活检并辅助临床决策,本研究建立了一种结合超微血管成像(SMI)和剪切波弹性成像(SWE)的动态列线图,用于评估TI-RADS 4类甲状腺结节的风险。
本回顾性研究纳入了248例行超声、SMI和SWE检查并获得细胞学或组织病理学结果的患者,随机分为训练组(174例患者)和验证组(74例患者)。在训练组中分析患者的临床特征以及超声、SMI和SWE特征。采用最小绝对收缩和选择算子(LASSO)回归及多因素逻辑回归筛选参数并构建动态列线图。采用受试者操作特征(ROC)曲线、校准曲线和决策曲线评估列线图的性能。
基于年龄(比值比[OR]=0.954;P=0.005)、形态(OR =0.345;P=0.041)、SMI(OR =9.511;P<0.001)和SWE(OR =3.670;P=0.001)构建了动态列线图。该列线图在训练组(曲线下面积[AUC]:0.848;95%置信区间[CI]:0.784-0.911)和验证组(AUC:0.862;95%CI:0.780-0.944)中均显示出良好的区分能力,且在所有队列中均优于单独的超声、SMI和SWE(P<0.05)。计算了每位患者的列线图得分,截断值为0.607,可用于区分高危和低危患者。
SMI和SWE在TI-RADS 4类甲状腺结节患者的风险分层中显示出额外的预测价值,构建了动态列线图以筛选高危个体并辅助临床决策。