Department of Urology, Fujian Medical University Union Hospital, Fuzhou, China.
The Graduate School of Fujian Medical University, Fuzhou, China.
Aging (Albany NY). 2024 Feb 19;16(4):3823-3836. doi: 10.18632/aging.205562.
This study was aimed to integrate tumor size with other prognostic factors into a prognostic nomogram to predict cancer-specific survival (CSS) in locally advanced (≥pT3a Nany M0) renal cell carcinoma (RCC) patients. Based on the Surveillance, Epidemiology, and End Results (SEER) database, 10,800 patients diagnosed with locally advanced RCC were collected. They were randomly divided into a training cohort ( = 7,056) and a validation cohort ( = 3,024). X-tile program was used to identify the optimal cut-off value of tumor size and age. The cut-off of age at diagnosis was 65 years old and 75 years old. The cut-off of tumor size was 54 mm and 119 mm. Univariate and multivariate Cox regression analyses were performed in the training cohort to identify independent prognostic factors for construction of nomogram. Then, the nomogram was used to predict the 1-, 3- and 5-year CSS. The performance of nomogram was evaluated by using concordance index (C-index), area under the Subject operating curve (AUC) and decision curve analysis (DCA). Moreover, the nomogram and tumor node metastasis (TNM) staging system (AJCC 8th edition) were compared. 10 variables were screened to develop the nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) indicated satisfactory ability of the nomogram. Compared with the AJCC 8th edition of TNM stage, DCA showed that the nomogram had improved performance. We developed and validated a nomogram for predicting the CSS of patients with locally advanced RCC, which was more precise than the AJCC 8th edition of TNM staging system.
本研究旨在将肿瘤大小与其他预后因素整合到一个预后列线图中,以预测局部进展期(≥pT3a Nany M0)肾细胞癌(RCC)患者的癌症特异性生存(CSS)。基于监测、流行病学和最终结果(SEER)数据库,共收集了 10800 例局部进展期 RCC 患者。他们被随机分为训练队列(n=7056)和验证队列(n=3024)。X-tile 程序用于确定肿瘤大小和年龄的最佳截断值。诊断时年龄的截断值为 65 岁和 75 岁。肿瘤大小的截断值为 54mm 和 119mm。在训练队列中进行单因素和多因素 Cox 回归分析,以确定构建列线图的独立预后因素。然后,使用列线图预测 1、3 和 5 年 CSS。通过一致性指数(C-index)、受试者工作特征曲线下面积(AUC)和决策曲线分析(DCA)评估列线图的性能。此外,还比较了列线图和肿瘤淋巴结转移(TNM)分期系统(AJCC 第 8 版)。该列线图共筛选出 10 个变量。受试者工作特征曲线(ROC)下面积(AUC)表明该列线图具有良好的能力。与 AJCC 第 8 版 TNM 分期相比,DCA 表明该列线图具有更好的性能。我们开发并验证了一个用于预测局部进展期 RCC 患者 CSS 的列线图,其预测性能优于 AJCC 第 8 版 TNM 分期系统。