Hu Ang, Li Yin, Wang Zhongyu, Tian Jiahe, Jiang Ke, Chen Jun, Jiang Mingjie, Li Qiuli
Department of Head and Neck Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, P. R. China.
Endocrine. 2025 May;88(2):511-522. doi: 10.1007/s12020-025-04177-z. Epub 2025 Feb 3.
Predicting the likelihood of papillary thyroid carcinoma (PTC) recurrence is crucial for improving patient outcomes. The association between the BRAF V600E (BRAF) mutation and PTC recurrence remains controversial. Our goal was to determine prognostic features of PTC patients and construct models for predicting recurrence risk according to BRAF mutation status.
A total of 811 PTC patients whose clinical information and survival data were available were included in this study. Independent prognostic variables of PTC identified by screening via LASSO-Cox regression analysis were then used to construct nomograms. The performance of the predictive models was assessed according to the C-index, ROC curve, validation curve, and decision curve analyses. Kaplan-Meier curves were used to analyze differences between patients grouped according to prognostic factors and relapse risk.
Multivariate Cox regression analysis demonstrated that extrathyroidal extension (ETE), vascular tumor thrombus, and lymph node yield (LNY) were correlated with recurrence-free survival (RFS) in the BRAF mutation-negative group, while extranodal extension (ENE), number of metastatic lymph node (NMLN), pathological stage, and vascular tumor thrombus were correlated with RFS in the BRAF mutation-positive group. The mutation-stratified predictive models demonstrated better performance than the model without stratification, as indicated by the greater C-index values (0.880 vs. 0.859 vs. 0.753), AUC values (1-year AUC: 0.946 vs. 0.947 vs. 0.758; 3-year AUC: 0.889 vs. 0.871 vs. 0.760; 5-year AUC: 0.845 vs. 0.793 vs. 0.758), and net clinical benefit. The calibration curves at 1 year, 3 years, and 5 years showed good consistency. The bootstrap internal validation had good AUC values exceeding 0.8 and showed a well-fitting calibration curve. Significant differences in RFS were observed between the low-risk and high-risk groups (P < 0.001).
Stratifying patients based on their BRAF mutation status can facilitate the development of better and more targeted postoperative management strategies. Nomograms for BRAF mutation positive and negative patients were developed to precisely and consistently predict recurrence risk in PTC patients.
预测甲状腺乳头状癌（PTC）复发的可能性对于改善患者预后至关重要。BRAF V600E（BRAF）突变与PTC复发之间的关联仍存在争议。我们的目标是确定PTC患者的预后特征，并根据BRAF突变状态构建预测复发风险的模型。
本研究纳入了811例有临床信息和生存数据的PTC患者。通过LASSO-Cox回归分析筛选出的PTC独立预后变量随后用于构建列线图。根据C指数、ROC曲线、验证曲线和决策曲线分析评估预测模型的性能。采用Kaplan-Meier曲线分析根据预后因素和复发风险分组的患者之间的差异。
多因素Cox回归分析表明，甲状腺外侵犯（ETE）、血管内瘤栓和淋巴结收获量（LNY）与BRAF突变阴性组的无复发生存期（RFS）相关，而结外侵犯（ENE）、转移淋巴结数量（NMLN）、病理分期和血管内瘤栓与BRAF突变阳性组的RFS相关。突变分层预测模型的性能优于未分层模型，C指数值更高（0.880对0.859对0.753）、AUC值更高（1年AUC：0.946对0.947对0.758；3年AUC：0.889对0.871对0.760；5年AUC：0.845对0.793对0.758）以及净临床获益表明了这一点。1年、3年和5年的校准曲线显示出良好的一致性。自举内部验证具有超过0.8的良好AUC值，并显示出拟合良好的校准曲线。低风险组和高风险组之间的RFS存在显著差异（P＜0.001）。
根据BRAF突变状态对患者进行分层有助于制定更好、更有针对性的术后管理策略。为BRAF突变阳性和阴性患者开发了列线图，以准确、一致地预测PTC患者的复发风险。
