Wang Hongxi, Li Qianrui, Tian Tian, Liu Bin, Tian Rong
Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu City, 610041, Sichuan Province, China.
J Clin Endocrinol Metab. 2025 Jan 21;110(2):534-541. doi: 10.1210/clinem/dgae465.
Various prognostic factors are expected to refine the American Thyroid Association recurrence risk stratification for patients with papillary thyroid cancer (PTC). However, it remains unclear to what extent integrating these factors improves patient treatment decision-making.
We developed 2 predictive models for structural incomplete response (SIR) at the 1-year follow-up visit, based on comprehensive clinical data from a retrospective cohort of 2539 patients. Model 1 included the recurrence risk stratification and lymph node features (ie, number and ratio of metastatic lymph nodes, N stage). Model 2 further incorporated preablation stimulated thyroglobulin (s-Tg). An independent cohort of 746 patients was used for validation analysis. We assessed the models' predictive performance compared to the recurrence risk stratification using the integrated discrimination improvement (IDI) and the continuous net reclassification improvement (NRI). The clinical utility of the models was evaluated using decision curve analysis.
Both model 1 and model 2 outperformed the recurrence risk stratification in predicting SIR, with improved correct classification rates (model 1: IDI = 0.02, event NRI = 42.31%; model 2: IDI = 0.07, event NRI = 53.54%). The decision curves indicated that both models provided greater benefits over the risk stratification system in clinical decision-making. In the validation set, model 2 maintained similar performance while model 1 did not significantly improve correct reclassification.
The inclusion of lymph node features and s-Tg showed potential to enhance the predictive accuracy and clinical utility of the existing risk stratification system for PTC patients.
多种预后因素有望优化美国甲状腺协会对甲状腺乳头状癌（PTC）患者的复发风险分层。然而，目前尚不清楚整合这些因素在多大程度上能改善患者的治疗决策。
我们基于2539例患者的回顾性队列综合临床数据，开发了两个用于预测1年随访时结构不完全缓解（SIR）的预测模型。模型1包括复发风险分层和淋巴结特征（即转移淋巴结的数量和比例、N分期）。模型2进一步纳入了消融前刺激甲状腺球蛋白（s-Tg）。使用746例患者的独立队列进行验证分析。我们使用综合判别改善（IDI）和连续净重新分类改善（NRI）评估了与复发风险分层相比模型的预测性能。使用决策曲线分析评估模型的临床实用性。
模型1和模型2在预测SIR方面均优于复发风险分层，正确分类率有所提高（模型1：IDI = 0.02，事件NRI = 42.31%；模型2：IDI = 0.07，事件NRI = 53.54%）。决策曲线表明，在临床决策中，这两个模型都比风险分层系统提供了更大的益处。在验证集中，模型2保持了相似的性能，而模型1在正确重新分类方面没有显著改善。
纳入淋巴结特征和s-Tg显示出提高现有PTC患者风险分层系统预测准确性和临床实用性的潜力。
