Department of Pain, The First People's Hospital of Chenzhou, The First Affiliated Hospital of Xiangnan University, Chenzhou, Hunan, China.
School of Basic Medicine, Xiangnan University, Chenzhou, Hunan, China.
Front Endocrinol (Lausanne). 2024 Aug 16;15:1417528. doi: 10.3389/fendo.2024.1417528. eCollection 2024.
The prevalence of papillary thyroid cancer is gradually increasing and the trend of youthfulness is obvious. Some patients may not be able to undergo surgery, which is the mainstay of treatment, due to physical or financial reasons. Therefore, the prediction of cancer-specific survival (CSS) in patients with non-operated papillary thyroid cancer is necessary.
Patients' demographic and clinical information was extracted from the Surveillance, Epidemiology, and End Results database. SPSS software was used to perform Cox regression analyses as well as propensity score matching analyses. R software was used to construct and validate the nomogram. X-tile software was used to select the best cutoff point for patient risk stratification.
A total of 1319 patients were included in this retrospective study. After Cox regression analysis, age, grade, T stage, M stage, radiotherapy, and chemotherapy were used to construct the nomogram. C-index, calibration curves, and receiver operating characteristic curves all verified the high predictive accuracy of the nomogram. The decision curve analysis demonstrated that patients could gain clinical benefit from this predictive model. Survival curve analysis after propensity score matching demonstrated the positive effects of radiotherapy on CSS in non-operated patients.
Our retrospective study successfully established a nomogram that accurately predicts CSS in patients with non-operated papillary thyroid cancer and demonstrated that radiotherapy for operated patients can still help improve prognosis. These findings can help clinicians make better choices.
甲状腺乳头状癌的发病率逐渐升高，且呈现年轻化趋势。部分患者可能由于身体或经济原因无法接受手术这一主要治疗手段。因此，对未行手术的甲状腺乳头状癌患者进行癌症特异性生存（CSS）预测是必要的。
从监测、流行病学和最终结果数据库中提取患者的人口统计学和临床信息。采用 SPSS 软件进行 Cox 回归分析和倾向评分匹配分析，使用 R 软件构建和验证列线图。使用 X-tile 软件选择患者风险分层的最佳截断值。
本回顾性研究共纳入 1319 例患者。经 Cox 回归分析，年龄、分级、T 分期、M 分期、放疗和化疗被用于构建列线图。C 指数、校准曲线和受试者工作特征曲线均验证了该列线图的高预测准确性。决策曲线分析表明，该预测模型可以为患者带来临床获益。倾向评分匹配后生存曲线分析表明，放疗对未手术患者的 CSS 有积极影响。
本回顾性研究成功建立了一个能够准确预测未手术甲状腺乳头状癌患者 CSS 的列线图，并表明放疗对手术患者仍有助于改善预后。这些发现有助于临床医生做出更好的选择。
