Yi Dan, Fan Libin, Zhu Jianbo, Yao Jincao, Peng Chanjuan, Xu Dong
1Department of Ultrasound, Shaoxing People's Hospital, Shaoxing, China.
Zhejiang Chinese Medical University, Hangzhou, China.
Front Oncol. 2022 Aug 18;12:970758. doi: 10.3389/fonc.2022.970758. eCollection 2022.
To establish and verify a nomogram based on multimodal ultrasonography (US) for the assessment of the malignancy risk of thyroid nodules and to explore its value in distinguishing benign from malignant thyroid nodules.
From September 2020 to December 2021, the data of 447 individuals with thyroid nodules were retrieved from the multicenter database of medical images of the National Health Commission's Capacity Building and Continuing Education Center, which includes data from more than 20 hospitals. All patients underwent contrast-enhanced US (CEUS) and elastography before surgery or fine needle aspiration. The training set consisted of three hundred datasets from the multicenter database (excluding Zhejiang Cancer Hospital), and the external validation set consisted of 147 datasets from Zhejiang Cancer Hospital. As per the pathological results, the training set was separated into benign and malignant groups. The characteristics of the lesions in the two groups were analyzed and compared using conventional US, CEUS, and elastography score. Using multivariate logistic regression to screen independent predictive risk indicators, then a nomogram for risk assessment of malignant thyroid nodules was created. The diagnostic performance of the nomogram was assessed utilizing calibration curves and receiver operating characteristic (ROC) from the training and validation cohorts. The nomogram and The American College of Radiology Thyroid Imaging, Reporting and Data System were assessed clinically using decision curve analysis (DCA).
Multivariate regression showed that irregular shape, elastography score (≥ 3), lack of ring enhancement, and unclear margin after enhancement were independent predictors of malignancy. During the training (area under the ROC [AUC]: 0.936; 95% confidence interval [CI]: 0.902-0.961) and validation (AUC: 0.902; 95% CI: 0.842-0.945) sets, the multimodal US nomogram with these four variables demonstrated good calibration and discrimination. The DCA results confirmed the good clinical applicability of the multimodal US nomogram for predicting thyroid cancer.
As a preoperative prediction tool, our multimodal US-based nomogram showed good ability to distinguish benign from malignant thyroid nodules.
建立并验证基于多模态超声（US）的列线图，用于评估甲状腺结节的恶性风险，并探讨其在鉴别甲状腺良恶性结节中的价值。
2020年9月至2021年12月，从国家卫生健康委能力建设和继续教育中心医学影像多中心数据库中检索447例甲状腺结节患者的数据，该数据库包含20多家医院的数据。所有患者在手术或细针穿刺前均接受了超声造影（CEUS）和弹性成像检查。训练集由多中心数据库中的300个数据集组成（不包括浙江省肿瘤医院），外部验证集由浙江省肿瘤医院的147个数据集组成。根据病理结果，将训练集分为良性和恶性组。使用传统超声、CEUS和弹性成像评分分析并比较两组病变的特征。采用多因素logistic回归筛选独立的预测风险指标，然后创建甲状腺恶性结节风险评估列线图。利用训练和验证队列的校准曲线和受试者操作特征（ROC）曲线评估列线图的诊断性能。使用决策曲线分析（DCA）对列线图和美国放射学会甲状腺影像报告和数据系统进行临床评估。
多因素回归显示，形态不规则、弹性成像评分（≥3）、无环状强化及强化后边缘不清是恶性的独立预测因素。在训练集（ROC曲线下面积[AUC]：0.936；95%置信区间[CI]：0.902-0.961）和验证集（AUC：0.902；95%CI：0.842-0.945）中，包含这四个变量的多模态超声列线图显示出良好的校准和鉴别能力。DCA结果证实了多模态超声列线图在预测甲状腺癌方面具有良好的临床适用性。
作为一种术前预测工具，我们基于多模态超声的列线图在鉴别甲状腺良恶性结节方面表现出良好的能力。
