Department of Education, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
Drug Clinical Trial Agency, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China.
BMC Cancer. 2020 Nov 4;20(1):1066. doi: 10.1186/s12885-020-07586-7.
Heterogeneity of metastatic renal cell carcinoma (RCC) constraints accurate prognosis prediction of the tumor. We therefore aimed at developing a novel nomogram for accurate prediction of overall survival (OS) of patients with metastatic RCC.
We extracted 2010 to 2016 data for metastatic RCC patients in the Surveillance, Epidemiology, and End Results (SEER) database, and randomly stratified them equally into training and validation sets. Prognostic factors for OS were analyzed using Cox regression models, and thereafter integrated into a 1, 3 and 5-year OS predictive nomogram. The nomogram was validated using the training and validation sets. The performance of this model was evaluated by the Harrell's concordance index (C-index), calibration curve, integrated discrimination improvement (IDI), category-free net reclassification improvement (NRI), index of prediction accuracy (IPA), and decision curve analysis (DCA).
Overall, 2315 metastatic RCC patients in the SEER database who fulfilled our inclusion criteria were utilized in constructing a nomogram for predicting OS of newly diagnosed metastatic RCC patients. The nomogram incorporated eight clinical factors: Fuhrman grade, lymph node status, sarcomatoid feature, cancer-directed surgery and bone, brain, liver, and lung metastases, all significantly associated with OS. The model was superior to the American Joint Committee on Cancer (AJCC) staging system (7th edition) both in training (C-indices, 0.701 vs. 0.612, P < 0.001) and validation sets (C-indices, 0.676 vs. 0.600, P < 0.001). The calibration plots of the nomogram corresponded well between predicted and observed values. NRI, IDI, and IPA further validated the superior predictive capability of the nomogram relative to the AJCC staging system. The DCA plots revealed reliable clinical application of our model in prognosis prediction of metastatic RCC patients.
We developed and validated an accurate nomogram for individual OS prediction of metastatic RCC patients. This nomogram can be applied in design of clinical trials, patient counseling, and rationalizing therapeutic modalities.
转移性肾细胞癌（RCC）的异质性限制了肿瘤准确预后的预测。因此，我们旨在开发一种新的列线图，以准确预测转移性 RCC 患者的总生存期（OS）。
我们从监测、流行病学和最终结果（SEER）数据库中提取了 2010 年至 2016 年转移性 RCC 患者的数据，并将其平均随机分为训练集和验证集。使用 Cox 回归模型分析 OS 的预后因素，并将其整合到 1、3 和 5 年 OS 预测列线图中。使用训练集和验证集验证该列线图。该模型的性能通过 Harrell 一致性指数（C 指数）、校准曲线、综合判别改善（IDI）、无分类净重新分类改善（NRI）、预测准确性指数（IPA）和决策曲线分析（DCA）进行评估。
总体而言，我们利用 SEER 数据库中符合纳入标准的 2315 例转移性 RCC 患者构建了一个预测新诊断的转移性 RCC 患者 OS 的列线图。该列线图纳入了 8 个临床因素：Fuhrman 分级、淋巴结状态、肉瘤样特征、癌症定向手术以及骨、脑、肝和肺转移，所有这些因素均与 OS 显著相关。该模型在训练集（C 指数，0.701 比 0.612，P<0.001）和验证集（C 指数，0.676 比 0.600，P<0.001）中均优于美国癌症联合委员会（AJCC）分期系统（第 7 版）。列线图的校准图在预测值和观察值之间对应良好。NRI、IDI 和 IPA 进一步验证了该列线图相对于 AJCC 分期系统的优越预测能力。DCA 图表明，我们的模型在预测转移性 RCC 患者的预后方面具有可靠的临床应用。
我们开发并验证了一种用于预测转移性 RCC 患者个体 OS 的准确列线图。该列线图可应用于临床试验设计、患者咨询和治疗方式的合理化。
