多模态超声影像组学鉴别甲状腺木乃伊结节与乳头状癌
本研究纳入120例甲状腺乳头状癌与84例木乃伊结节，提取常规超声及CEUS影像组学特征并联合临床因素建模。结果显示US+CEUS影像组学模型诊断效能最佳，训练集AUC为0.936，测试集AUC为0.881。加入临床特征未进一步提升诊断效能，提示应依据疾病特点合理组合模态。
要点
- US+CEUS影像组学模型对MTN与PTC鉴别诊断效能较高，测试集AUC达0.881。
- 模型以CEUS特征为主（7个CEUS特征、3个US特征），应重视CEUS在鉴别中的价值。
- 加入临床特征未提高诊断效能，常规临床因素在本鉴别场景中贡献有限。
核心结论
- US+CEUS影像组学模型对区分木乃伊甲状腺结节与甲状腺乳头状癌具有较高诊断价值，模型以CEUS特征为主导。
- 与临床特征联合建模未能改善诊断效能，应根据具体疾病恰当选择模态组合。
研究设计与样本
- 研究纳入120例甲状腺乳头状癌及84例木乃伊甲状腺结节，提取常规超声、CEUS影像组学特征及临床信息。
- 采用Logistic Regression与支持向量机算法建模，通过ROC曲线、校准曲线及决策曲线分析评价诊断性能与临床效用。
- 使用列线图对最终模型进行可视化呈现。
诊断标准与关键数值
- US+CEUS影像组学模型训练集AUC为0.936，测试集AUC为0.881，由7个CEUS特征及3个US特征构成。
- 决策曲线分析显示US+CEUS影像组学模型在阈值概率范围0.07至0.97之间获得较高净获益。
临床应用与阅片要点
- 常规超声与CEUS联合的影像组学模型可作为甲状腺结节管理中介入决策的辅助工具。
- 阅片与报告时可重点关注模型所依赖的CEUS特征，在鉴别MTN与PTC时提高对CEUS征象的判读权重。
- 临床特征在本鉴别任务中增益有限，报告中无需过度强调以临床指标修正影像判断。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
OBJECTIVES: This study aimed to establish a machine-learning model that integrates contrast-enhanced ultrasound (CEUS) radiomics, conventional ultrasound (US) radiomics and clinical features for distinguishing mummified thyroid nodules (MTNs) from papillary thyroid carcinomas (PTCs). The goal is to provide strategies for subsequent clinical decision-making.
METHODS: This study included 120 PTCs and 84 MTNs. Clinical information was obtained from the patients to identify independent risk factors. Radiomics features were extracted from the images. After feature selection, Logistic Regression (LR) and support vector machine (SVM) algorithms were used to build various models. Finally, the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the diagnostic performance and clinical utility of the models. A nomogram was used to visualize the final model.
RESULTS: The developed US+CEUS radiomics model demonstrated the best diagnostic performance. With seven CEUS and three US features, it achieved an area under the curve (AUC) of 0.936 in the training set and 0.881 in the test set. The DCA indicated that the US+CEUS radiomics model achieved a higher net benefit within the threshold probability range of 0.07-0.97.
CONCLUSIONS: The US+CEUS radiomics model shows high diagnostic value in differentiating PTCs from MTNs with the model dominated by CEUS features. However, combining it with clinical features did not improve the diagnostic performance of the model, so we should combine modalities appropriately according to the specific disease.
引用
- Li, Yan, Li, et al. Differentiating Mummified Thyroid Nodules From Papillary Thyroid Carcinoma: A Machine Learning Approach Using Multi-Modal Ultrasound Radiomics. Ultrasound Med Biol. 2026. PMID: 42225518. doi:10.1016/j.ultrasmedbio.2026.05.007
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。