甲状腺癌风险分层工具：整合超声、细胞学与临床风险因素模型的系统综述
该系统综述评估了整合临床、超声及细胞学特征的多变量风险分层工具对甲状腺结节患者的诊断性能与临床实用性。共纳入7项研究描述5种工具，其中TiPS敏感度96.2%、特异度97.5%、AUC>0.9，CUT评分AUC亦>0.9，但多数模型源于单中心回顾性队列且缺乏外部验证。
要点
- TiPS敏感度96.2%、特异度97.5%、AUC>0.9，表现最佳。
- CUT评分AUC>0.9，在低至中危结节中表现良好。
- 多数模型为单中心回顾性研究，缺乏外部验证。
核心结论
- 整合多模态数据的风险分层工具可改善甲状腺结节风险分层，尤其在不明确细胞学病例中。
- 方法学局限与缺乏外部验证目前限制其临床实用性。
- 风险分层工具应作为临床判断与共享决策的辅助而非替代。
研究设计与样本
- 系统综述，检索PubMed、Embase和Cochrane数据库自建库至2026年4月13日。
- 纳入评估成人甲状腺结节多变量风险预测模型的研究，要求模型整合临床特征、超声发现和细胞学结果并经组织学验证。
- 共纳入7项研究，描述5种不同的风险分层工具。
主要发现
- 纳入的5种工具为甲状腺结节应用（TNAPP）、McGill甲状腺结节评分（MTNS）、CUT评分、纪念斯隆凯特琳癌症中心（MSKCC）列线图和甲状腺预测评分（TiPS）。
- TiPS敏感度96.2%、特异度97.5%、AUC>0.9。
- CUT评分AUC>0.9，尤其在低至中危结节中表现良好。
- TNAPP准确性50.5%、特异度27.5%。
- MTNS和MSKCC对不明确细胞学有前景但缺乏稳健验证。
关键数值与推荐
- TiPS：敏感度96.2%，特异度97.5%，AUC>0.9。
- CUT评分：AUC>0.9，尤其在低至中危结节中。
- TNAPP：准确性50.5%，特异度27.5%。
临床怎么用
- 风险分层工具可用于甲状腺结节评估的辅助，尤其在不明确细胞学病例中。
- 不应替代临床判断与共享决策。
- 临床使用前需考虑工具的外部验证情况。
局限性
- 多数模型源于单中心回顾性队列，限制普遍性。
- MTNS和MSKCC缺乏稳健验证。
- 需要前瞻性多人群评估以确定最有效和可推广的工具。
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）。
原文
BACKGROUND: The rising incidence of thyroid cancer presents a growing diagnostic and therapeutic challenge. Various risk stratification systems have sought to integrate clinical, ultrasonographic, and, in some cases, cytological features to aid malignancy prognostication. This systematic review aims to critically evaluate risk stratification tools (RSTs) for patients with thyroid nodules, which incorporate multimodal inputs to assess their diagnostic performance and clinical utility in supporting surgical decision-making. METHODS: PubMed, Embase, and Cochrane databases were searched from inception to 04/13/2026, identifying studies evaluating multivariable risk prediction models for adult patients undergoing assessment of thyroid nodules. Studies were excluded if the proposed tool failed to incorporate clinical features, ultrasound findings, and cytology results or was not validated with histology. Data extraction encompassed methodology of model development, performance metrics, and approaches to validation. Risk of bias was assessed using the PROBAST+AI tool. RESULTS: Seven studies describing five distinct RSTs met inclusion criteria Thyroid Nodule App (TNAPP), the McGill Thyroid Nodule Score (MTNS), CUT Score, Memorial Sloan Kettering Cancer Centre (MSKCC) nomogram, and Thyroid Prediction Score (TiPS). TiPS demonstrated the highest sensitivity (96.2%) and specificity (97.5%) with area under the curve (AUC) >0.9. The CUT score also showed strong performance (AUC >0.9), particularly in low-to-intermediate risk nodules. TNAPP underperformed (accuracy 50.5%; specificity 27.5%) despite broad clinical inputs. The MTNS and MSKCC, although promising for indeterminate cytology, lacked robust validation. Most models were derived from single-center, retrospective cohorts, limiting generalizability. CONCLUSIONS: RSTs integrating multimodal data may improve thyroid nodule risk stratification, particularly in cases of indeterminate cytology. However, methodological limitations and lack of external validation currently restrict clinical utility. Prospective evaluation in diverse populations is required to identify the most effective and generalizable tools. Until then, RSTs should be used as adjuncts to, not replacements for, clinical judgment and shared decision-making in thyroid nodule assessment.
引用
- Watts E, Sattar Z, Boelaert K, et al. Risk Stratification Tools for Thyroid Cancer: A Systematic Review of Models Combining Ultrasound, Cytology, and Clinical Risk Factors. Thyroid. 2026. PMID: 42090382. doi:10.1177/10507256261448300
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。