UNASSIGNED:远处转移(DM)是分化型甲状腺癌(DTC)的重要预后因素,并决定了治疗过程。本研究旨在建立一个预测列线图模型,该模型可以单独估计DM的风险并分析女性DTC患者(FDTC)的预后。
UNASISIGNED:从监视中回顾性搜索了26,998个FDTC,流行病学,2010年至2018年的最终结果(SEER)数据库,随机分为验证和培训队列。进行单变量和多变量分析以筛选预后因素并构建预测列线图。通过受试者工作特征曲线下面积(AUC)评估列线图的性能,一致性指数(C指数),和校准曲线。通过Kaplan-Meier(K-M)分析评估总生存期(OS)和癌症特异性生存期(CSS)。
未经证实:据报道,共有263例(0.97%)FDTC患有DM。K-M分析显示多器官转移和脑受累与患者生存率较低相关(P<0.001)。肿瘤大小,诊断时的年龄,甲状腺切除术,N1级,T3-4级,病理类型是FDTC中DM的独立预测因素(均P<0.001)。同样,诊断时的年龄,黑色,DM,T3-4级,甲状腺切除术,肺转移是FDTC的独立预后因素(均P<0.001)。基于上述因素建立了几个预测列线图。C指数,AUC,和校准曲线证明了这些列线图模型的良好性能。
未经评估:我们的研究成功地建立和验证了可以预测DM的列线图,以及基于大型研究队列的FDTC个体患者的CSS和OS。这些列线图可以使外科医生对FDTC进行个性化的生存评估和风险分层。
UNASSIGNED: Distant metastasis (DM) is an important prognostic factor in differentiated thyroid cancer (DTC) and determines the course of treatment. This study aimed to establish a predictive nomogram model that could individually estimate the risk of DM and analyze the prognosis of female DTC patients (FDTCs).
UNASSIGNED: A total of 26,998 FDTCs were retrospectively searched from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018 and randomly divided into validation and training cohorts. Univariate and multivariate analyses were performed to screen for prognostic factors and construct a prediction nomogram. The performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), concordance index (C-index), and a calibration curve. The overall survival (OS) and cancer-specific survival (CSS) were evaluated by Kaplan-Meier (K-M) analysis.
UNASSIGNED: A total of 263 (0.97%) FDTCs were reported to have DM. K-M analysis showed the association of multiple-organ metastases and brain involvement with lower survival rates (P < 0.001) in patients. Tumor size, age at diagnosis, thyroidectomy, N1 stage, T3-4 stage, and pathological type were independent predictive factors of DM in FDTCs (all P < 0.001). Similarly, age at diagnosis, Black, DM, T3-4 stage, thyroidectomy, and lung metastasis were determined as independent prognostic factors for FDTCs (all P < 0.001). Several predictive nomograms were established based on the above factors. The C-index, AUC, and calibration curves demonstrated a good performance of these nomogram models.
UNASSIGNED: Our study was successful in establishing and validating nomograms that could predict DM, as well as CSS and OS in individual patients with FDTC based on a large study cohort. These nomograms could enable surgeons to perform individualized survival evaluation and risk stratification for FDTCs.