Department of Thyroid and Breast Surgery, Binzhou Medical University Hospital, Binzhou, China.
Department of Gastroenterology, Yantai Affiliated Hospital of Binzhou Medical University, China.
Asian J Surg. 2023 Oct;46(10):4169-4177. doi: 10.1016/j.asjsur.2022.10.012. Epub 2022 Oct 26.
Parathyroid carcinoma is a rare endocrine malignancy. Considering that clinicians develop appropriate treatment strategies based on patients' survival expectations. Therefore, the present study aimed to develop a survival prediction model to guide clinical decision-making.
We retrospectively analyzed 362 parathyroid carcinoma patients diaagnosed in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Correlations between outcome events and variables were analyzed using univariate and multifactorial Cox regression, and variables screened by the multifactorial Cox risk proportional model were used to construct a survival prediction model. The model was evaluated using Receiver Operating Characteristic (ROC) curves, decision curve analysis (DCA), and C-index and calibration curves.
Univariate and multifactorial COX analyses revealed five independent prognostic factors for parathyroid carcinoma patients, which were subsequently used to develop the nomogram prediction model. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.747 (0.686-0.808), the area under the receiver operator characteristics curve(AUC)values of the nomogram in prediction of the 3, 5, and 10-year OS were 0.718 (0617-0.819), 0.711 (0.614-0.808) and 0.706 (0.610-0.803), respectively. In the validation cohort, the C-index was 0.740 (0.645-0.835), The AUC for 3, 5, and 10-years OS were 0.736 (0.584-0888), 0.698 (0.551-0.845) and 0.767 (0.647-0.887), respectively. The C-index, time-dependent ROC curve, calibration curve, and DCA showed that the Nomogram had a clear advantage.
The developed nomogram can be applied in clinical practice to help clinicians to assess patient prognosis.
甲状旁腺癌是一种罕见的内分泌恶性肿瘤。由于临床医生根据患者的生存预期制定适当的治疗策略，因此本研究旨在开发一种生存预测模型以指导临床决策。
我们回顾性分析了 2004 年至 2015 年间 SEER 数据库中诊断的 362 例甲状旁腺癌患者。使用单因素和多因素 Cox 回归分析结局事件与变量之间的相关性，使用多因素 Cox 风险比例模型筛选出的变量构建生存预测模型。使用受试者工作特征（ROC）曲线、决策曲线分析（DCA）和 C 指数和校准曲线评估模型。
单因素和多因素 COX 分析显示甲状旁腺癌患者有五个独立的预后因素，随后用于开发列线图预测模型。在训练队列中，列线图预测总生存（OS）的 C 指数为 0.747（0.686-0.808），列线图预测 3、5 和 10 年 OS 的 ROC 曲线下面积（AUC）值分别为 0.718（0.617-0.819）、0.711（0.614-0.808）和 0.706（0.610-0.803）。在验证队列中，C 指数为 0.740（0.645-0.835），预测 3、5 和 10 年 OS 的 AUC 分别为 0.736（0.584-0.888）、0.698（0.551-0.845）和 0.767（0.647-0.887）。C 指数、时间依赖性 ROC 曲线、校准曲线和 DCA 表明列线图具有明显优势。
所开发的列线图可应用于临床实践，帮助临床医生评估患者的预后。
