Department of Breast and Thyroid Surgery, Shaoxing Central Hospital, The Central Affiliated Hospital, Shaoxing University, Shaoxing, China.
Department of gynecology, Shaoxing Central Hospital, The Central Affiliated Hospital, Shaoxing University, Shaoxing, China.
Front Endocrinol (Lausanne). 2024 Sep 13;15:1375274. doi: 10.3389/fendo.2024.1375274. eCollection 2024.
The real-time prognostic data of patients with poorly differentiated thyroid carcinoma (PDTC) after surviving for several years was unclear. This study aimed to employ a novel method to dynamically estimate survival for PDTC patients.
A total of 913 patients diagnosed with PDTC between 2014 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database, was recruited in our study. Kaplan-Meier method was used to estimate the overall survival (OS). The conditional survival (CS) outcomes of PDTC were analyzed and CS rates were calculated using the formula CS(y/x) = OS(y+x)/OS(x), whereby CS(y/x) denotes the probability of a patient enduring an additional y years subsequent to surviving x years following the diagnosis of PDTC. The least absolute shrinkage and selection operator (LASSO) regression was employed to identify prognostic predicters and multivariate Cox regression was utilized to develop a CS-nomogram. Finally, the performance of this model was evaluated and validated.
Kaplan-Meier survival analysis unveiled patient outcomes demonstrating an OS rate of 83%, 75%, and 60% respectively at the end of 3, 5, and 10 years. The novel CS analysis highlighted a progressive enhancement in survival over time, with the 10-year cumulative survival rate progressively augmenting from its initiation of 60% to 66%, 69%, 73%, 77%, 81%, 83%, 88%, 93%, and finally 97% (after surviving for 1-9 years, respectively) each year. And then 11 (11/15) predictors including age at diagnosis, sex, histology type, SEER stage, T stage, N stage, M stage, tumor size, coexistence with other malignancy, radiotherapy and marital status, were selected by LASSO analysis under the condition of lambda.min. Multivariate Cox regression analysis further highlighted the significant impact of all these predictors on the OS of PDTC and we successfully established and validated a novel CS-nomogram for real-time and dynamic survival prediction.
This was the first study to analyze the CS pattern and demonstrate a gradual improvement in CS over time in long-term PDTC survivors. We then successfully developed and validated a novel CS-nomogram for individualized, dynamic, and real-time survival forecasting, empowering clinicians to adapt and refine the patient-tailored treatment strategy promptly with consideration of evolving risks.
患有低分化甲状腺癌（PDTC）并存活数年的患者的实时预后数据尚不清楚。本研究旨在采用一种新方法对 PDTC 患者的生存情况进行动态估计。
本研究共纳入了来自 SEER 数据库中 2014 年至 2015 年间诊断为 PDTC 的 913 例患者。采用 Kaplan-Meier 法估计总生存率（OS）。分析 PDTC 的条件生存率（CS）结果，并使用公式 CS(y/x) = OS(y+x)/OS(x)计算 CS 率，其中 CS(y/x)表示 PDTC 诊断后患者再存活 y 年的概率。采用最小绝对收缩和选择算子（LASSO）回归识别预后预测因子，采用多变量 Cox 回归建立 CS 列线图。最后，对该模型的性能进行了评估和验证。
Kaplan-Meier 生存分析显示，患者的 OS 率分别在 3、5 和 10 年时为 83%、75%和 60%。新的 CS 分析显示，生存时间随时间逐渐改善，10 年累积生存率从 60%开始逐渐升高，分别为 66%、69%、73%、77%、81%、83%、88%、93%和 97%（分别在 1-9 年内存活）。然后，在 lambda.min 的条件下，LASSO 分析选择了 11（11/15）个预测因子，包括诊断时的年龄、性别、组织学类型、SEER 分期、T 分期、N 分期、M 分期、肿瘤大小、合并其他恶性肿瘤、放疗和婚姻状况。多变量 Cox 回归分析进一步强调了这些预测因子对 PDTC OS 的显著影响，我们成功建立并验证了一个用于实时动态生存预测的新型 CS 列线图。
这是第一项分析 CS 模式并证明 PDTC 长期幸存者 CS 随时间逐渐改善的研究。然后，我们成功地开发并验证了一个用于个体化、动态和实时生存预测的新型 CS 列线图，使临床医生能够及时考虑不断变化的风险，调整和完善针对患者的治疗策略。
