Li Yang, Zhang Guang-Hong, Tian Man, Hua Chuan, Zhai Jian-Ping, He Yan-Qiong, Zuo Xin-He
Thyroid Center of Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China.
Hubei Shizhen Laboratory, Wuhan, China.
Front Med (Lausanne). 2025 Jul 8;12:1623286. doi: 10.3389/fmed.2025.1623286. eCollection 2025.
Thyroid eye disease (TED) is an autoimmune disorder with complex inflammatory activity that remains challenging to assess accurately. Current method, mainly the Clinical Activity Score (CAS), exhibits limitations in objectivity and comprehensiveness. This study aimed to develop a multidimensional predictive model integrating clinical parameters, SPECT/CT imaging data, and serum biomarkers, to improve TED activity evaluation.
This retrospective research included 36 TED patients (72 eyes) diagnosed by EUGOGO criteria who underwent SPECT/CT examination. The Clinical Activity Score (CAS) was used to evaluate inflammatory activity. Variables with significant associations with CAS-defined activity were identified using univariate analysis, and Bayesian shrinkage regression (BSR) and the least absolute shrinkage and selection operator (LASSO) were utilized for variable selection in the primary cohort. Predictive models were constructed and evaluated using receiver operating characteristic (ROC) curves (internally validated via five-fold cross-validation), decision curve analysis (DCA), and calibration curves.
Five predictive models were constructed. The comprehensive Model 4, combining clinical, imaging [EX, maximal SPECT/CT uptake ratio (URmax)], and serum biomarkers (TRAb, RBC), achieved superior diagnostic accuracy (AUC: 91.18%; sensitivity: 0.91; specificity: 0.86). Model 5, retaining variables significant in univariate and multivariate analyses, demonstrated robust performance (AUC: 85.97%) with superior stability during cross-validation (ROC mean: 0.8417). Key predictors included male sex (OR = 11.74), TRAb levels, EX, URmax, and RBC count. SPECT/CT-derived URmax correlated strongly with disease activity, while serum biomarkers complemented imaging limitations.
Multidimensional integration of clinical, imaging, and biomarker data significantly enhances TED activity evaluation compared to single-modality approaches. The multidimensional model offers superior diagnostic accuracy, addressing the limitations of conventional methods. These findings advocate for a holistic approach in TED management.
甲状腺眼病（TED）是一种具有复杂炎症活动的自身免疫性疾病，准确评估仍具有挑战性。目前的方法，主要是临床活动评分（CAS），在客观性和全面性方面存在局限性。本研究旨在开发一种整合临床参数、SPECT/CT成像数据和血清生物标志物的多维预测模型，以改善TED活动评估。
这项回顾性研究纳入了36例经EUGOGO标准诊断并接受SPECT/CT检查的TED患者（72只眼）。采用临床活动评分（CAS）评估炎症活动。通过单因素分析确定与CAS定义的活动有显著关联的变量，并在主要队列中使用贝叶斯收缩回归（BSR）和最小绝对收缩和选择算子（LASSO）进行变量选择。使用受试者工作特征（ROC）曲线（通过五折交叉验证进行内部验证）、决策曲线分析（DCA）和校准曲线构建并评估预测模型。
构建了五个预测模型。综合模型4结合了临床、成像[EX，最大SPECT/CT摄取率（URmax）]和血清生物标志物（TRAb，RBC），具有更高的诊断准确性（AUC：91.18%；敏感性：0.91；特异性：0.86）。模型5保留了在单因素和多因素分析中显著的变量，在交叉验证期间表现出稳健的性能（AUC：85.97%），稳定性更高（ROC均值：0.8417）。关键预测因素包括男性（OR = 11.74）、TRAb水平、EX、URmax和RBC计数。SPECT/CT得出的URmax与疾病活动密切相关，而血清生物标志物弥补了成像的局限性。
与单一模式方法相比，临床、成像和生物标志物数据的多维整合显著增强了TED活动评估。多维模型具有更高的诊断准确性，解决了传统方法的局限性。这些发现支持在TED管理中采用整体方法。
