Affiliated Hospital of Chengde Medical University, Chengde, Hebei 067000, China.
Qingdao University Medical College, Qingdao, Shandong 266000, China.
Biomed Res Int. 2020 Nov 17;2020:9501760. doi: 10.1155/2020/9501760. eCollection 2020.
Brain metastasis (BM) is a typical type of metastasis in renal cell carcinoma (RCC) patients. The early detection of BM is likely a crucial step for RCC patients to receive appropriate treatment and prolong their overall survival. The aim of this study was to identify the independent predictors of BM and construct a nomogram to predict the risk of BM. Demographic and clinicopathological data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database for RCC patients between 2010 and 2015. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors, and then, a visual nomogram was constructed. Multiple parameters were used to evaluate the discrimination and clinical value. We finally included 42577 RCC patients. Multivariate logistic regression analysis showed that histological type, tumor size, bone metastatic status, and lung metastatic status were independent BM-associated risk factors for RCC. We developed a nomogram to predict the risk of BM in patients with RCC, which showed favorable calibration with a -index of 0.924 (0.903-0.945) in the training cohort and 0.911 (0.871-0.952) in the validation cohort. The calibration curves and decision curve analysis (DCA) also demonstrated the reliability and accuracy of the clinical prediction model. The nomogram was shown to be a practical, precise, and personalized clinical tool for identifying the RCC patients with a high risk of BM, which not only will contribute to the more reasonable allocation of medical resources but will also enable a further improvements in the prognosis and quality of life of RCC patients.
脑转移（BM）是肾细胞癌（RCC）患者中典型的转移类型。早期发现 BM 可能是 RCC 患者接受适当治疗并延长总体生存时间的关键步骤。本研究旨在确定 BM 的独立预测因素，并构建一个列线图来预测 BM 的风险。从 2010 年至 2015 年的监测、流行病学和最终结果（SEER）数据库中获取 RCC 患者的人口统计学和临床病理数据。进行单因素和多因素逻辑回归分析以确定独立的危险因素，然后构建可视化列线图。使用多个参数评估鉴别力和临床价值。我们最终纳入了 42577 例 RCC 患者。多因素逻辑回归分析显示，组织学类型、肿瘤大小、骨转移状态和肺转移状态是 RCC 发生 BM 的独立相关危险因素。我们开发了一个预测 RCC 患者 BM 风险的列线图，在训练队列中的 -指数为 0.924（0.903-0.945），在验证队列中的 -指数为 0.911（0.871-0.952），显示出良好的校准度。校准曲线和决策曲线分析（DCA）也证明了临床预测模型的可靠性和准确性。该列线图是一种实用、精确且个性化的临床工具，可用于识别 BM 风险较高的 RCC 患者，这不仅有助于更合理地分配医疗资源，还有助于改善 RCC 患者的预后和生活质量。
