Lv Dingyang, Wang Qiwei, Sun Ke, Li Jinshuai, Zhou Huiyu, Wen Jie, Shuang Weibing
Department of Urology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China.
First Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi Province, China.
J Cancer. 2025 Jan 13;16(4):1189-1201. doi: 10.7150/jca.104569. eCollection 2025.
Early-onset kidney cancer (EOKC) is often associated with genetic factors and a high risk of metastasis. However, there is a lack of accurate prediction models for the prognosis of EOKC. The aim of this study is to establish an effective nomogram for predicting and evaluating the prognosis of patients with EOKC. The patients with EOKC were selected from the latest SEER database during 2004-2015. Patients between 2004 and 2014 were randomly divided into a training cohort and a validation cohort at a ratio of 7:3, and patients in 2015 were used for temporal external validation. Additionally, we included patients from First Hospital of Shanxi Medical University between 2013 and 2021 for spatial external validation. The performance of the nomogram was assessed using the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Patients were stratified based on the nomogram, and Kaplan-Meier (KM) curves were plotted to compare the survival probability of patients. In the temporal and spatial external validation cohort, the C-index of the nomogram for OS was 0.872 and 0.875, respectively, and the C-index of the nomogram for CSS were 0.872 and 0.851, respectively. In the temporal external validation cohort, the 1-year, 3-year and 5-year AUC of the nomogram for OS were 0.906, 0.899 and 0.876, respectively. In addition, the AUC showed that the nomogram had also high predictive ability for CSS. The calibration curves and DCA also indicated that the nomogram had a strong clinical utility. The KM curve revealed that patients in the low-risk group had a better prognosis than those in the high-risk group. Our study developed a novel high-performance nomogram for assessing the prognosis of patients with EOKC, and it has great potential for clinicians to assess patient prognosis and formulate effective intervention and follow-up strategies.
早发性肾癌（EOKC）通常与遗传因素及高转移风险相关。然而，目前缺乏针对EOKC预后的准确预测模型。本研究旨在建立一种有效的列线图，用于预测和评估EOKC患者的预后。EOKC患者选自2004 - 2015年最新的SEER数据库。2004年至2014年的患者按7:3的比例随机分为训练队列和验证队列，2015年的患者用于时间外部验证。此外，我们纳入了山西医科大学第一医院2013年至2021年的患者进行空间外部验证。使用一致性指数（C指数）、受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估列线图的性能。根据列线图对患者进行分层，并绘制Kaplan-Meier（KM）曲线以比较患者的生存概率。在时间和空间外部验证队列中，总生存期（OS）列线图的C指数分别为0.872和0.875，癌症特异性生存期（CSS）列线图的C指数分别为0.872和0.851。在时间外部验证队列中，OS列线图的1年、3年和5年曲线下面积（AUC）分别为0.906、0.899和0.876。此外，AUC表明列线图对CSS也具有较高的预测能力。校准曲线和DCA也表明列线图具有较强的临床实用性。KM曲线显示，低风险组患者的预后优于高风险组患者。我们的研究开发了一种用于评估EOKC患者预后的新型高性能列线图，对临床医生评估患者预后及制定有效的干预和随访策略具有巨大潜力。
