Data Sheet 1_Trends in incidence, mortality, and conditional survival of anaplastic thyroid cancer over the last two decades in the USA.zip

BackgroundAnaplastic thyroid carcinoma (ATC) is a highly aggressive malignancy, and there is currently a lack of up-to-date epidemiological data. Traditional survival analysis fails to capture the dynamic changes in prognosis for long-term survivors, while conditional survival (CS) analysis, a critical tool for adaptive risk stratification, remains underexplored in ATC.
MethodsPatients diagnosed with ATC between 2000 and 2021 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Temporal trends in age-adjusted incidence and incidence-based mortality were analyzed using Joinpoint regression to calculate annual percentage changes (APCs) with 95% confidence intervals (CIs). Overall survival (OS) was estimated using the Kaplan-Meier method. CS rates were calculated using the formula: CS(y/x) = OS(y+x)/OS(x). Prognostic factors were identified using Best Subset Regression (BSR), LASSO, and univariate and multivariate Cox regression analyses, and these factors were incorporated into a CS-nomogram model. The predictive performance of the model was validated using evaluation metrics, including the area under the receiver operating characteristic curve (AUC). Point values were assigned to the model’s predictive factors, and a risk stratification system was developed based on the optimal threshold of the total score.
ResultsFrom 2000 to 2021, the age-adjusted incidence of ATC increased from 0.066 to 0.077 per 100,000 (APC: 2.308%, 95% CI: 1.187–3.441), peaking at 0.119 in 2018. Mortality trends paralleled this rise, with age-adjusted mortality increasing from 0.037 to 0.051 per 100,000 (APC: 2.380%, 95% CI: 1.129–3.646). CS analysis demonstrated a progressive increase in survival rates over time, with the 24-month cumulative survival rate rising from 14.0% to 93.8%, with the most pronounced temporal changes observed in patients with distant disease. Prognostic factors identified through BSR, LASSO, and Cox regression included age, SEER stage, and treatment. A novel CS-nomogram was successfully developed and validated for dynamic real-time survival prediction, enabling identification of high- and low-risk patient groups.
ConclusionThe incidence and incidence-based mortality of ATC have increased over the past few decades. The CS rates of ATC patients have dynamically improved over time. The CS-nomogram, integrating age, SEER stage, and treatment, provides clinicians with a personalized, dynamic, and real-time survival prediction tool that helps alleviate survivors’ psychological distress, reduces anxiety, and optimizes precision follow-up strategies.

研究背景：间变性甲状腺癌（Anaplastic Thyroid Carcinoma, ATC）是一种高侵袭性恶性肿瘤，目前仍缺乏最新的流行病学数据。传统生存分析无法捕捉长期幸存者的预后动态变化，而作为适应性风险分层关键工具的条件生存（Conditional Survival, CS）分析，在间变性甲状腺癌中的应用仍有待深入挖掘。
研究方法：本研究从监测、流行病学与最终结果（Surveillance, Epidemiology, and End Results, SEER）数据库中筛选出2000年至2021年间确诊的间变性甲状腺癌患者。采用Joinpoint回归分析年龄调整发病率及基于发病的死亡率的时间趋势，计算年度百分比变化（Annual Percentage Changes, APCs）及95%置信区间（Confidence Intervals, CIs）。采用Kaplan-Meier法估算总生存期（Overall Survival, OS），条件生存率通过公式CS(y/x) = OS(y+x)/OS(x)计算得出。采用最佳子集回归（Best Subset Regression, BSR）、LASSO以及单因素和多因素Cox回归分析筛选预后因素，并将这些因素整合至CS列线图模型中。采用包括受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic Curve, AUC）在内的评估指标验证模型的预测性能。为模型的预测因素赋予分值，并基于总评分的最佳截断值构建风险分层系统。
研究结果：2000年至2021年，间变性甲状腺癌的年龄调整发病率从每10万人0.066升至0.077（APC：2.308%，95% CI：1.187~3.441），2018年达到峰值0.119。死亡率趋势与发病率上升趋势一致，年龄调整死亡率从每10万人0.037升至0.051（APC：2.380%，95% CI：1.129~3.646）。条件生存分析显示，生存率随时间推移逐步提升，24个月累积生存率从14.0%升至93.8%，其中远处转移患者的时间动态变化最为显著。经最佳子集回归、LASSO及Cox回归筛选出的预后因素包括年龄、SEER分期及治疗方式。本研究成功构建并验证了一款新型CS列线图，可用于动态实时生存预测，能够区分高风险与低风险患者群体。
研究结论：近数十年来，间变性甲状腺癌的发病率及基于发病的死亡率均呈上升趋势。间变性甲状腺癌患者的条件生存率随时间动态提升。整合年龄、SEER分期及治疗方式的CS列线图可为临床医师提供一款个性化、动态且实时的生存预测工具，有助于缓解幸存者的心理痛苦、减轻焦虑情绪，并优化精准随访策略。