Wang Fei, Wang Pan, Wang Xihao, Lu Hengming, Han Yuchun, Wang Lianqu, Li Zhihui
Department of Reproductive Medicine, Central Hospital of Zhumadian, Henan, China.
Department of Urology and Male Reproductive Health, Maternal and Child Health Hospital, Luoyang, China.
Front Med (Lausanne). 2024 Dec 3;11:1464589. doi: 10.3389/fmed.2024.1464589. eCollection 2024.
Current studies on the establishment of prognostic model for renal cell carcinoma (RCC) with liver metastases (LM) were scarce. This study aimed to develop nomograms to predict the prognosis of RCC with LM.
Patients diagnosed with RCC between 2010 and 2021 from the Surveillance, Epidemiology, and End Results (SEER) database were selected. The eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning algorithms were used to screen for the most influential factors affecting prognosis, and the Venn diagram method was employed for further refinement. Subsequently, a nomogram related to brain metastases was constructed. The performance of the nomograms was evaluated through receiver operating characteristics (ROC) curves, calibration plots, C-index, time-dependent C-index, and decision curve analysis (DCA). Kaplan-Meier (K-M) survival curves were used to provide additional verification of the clinical efficacy of the nomogram.
This research comprised 2,395 RCC patients with LM. The Venn diagram demonstrated that age, histological type, grade, AJCC T stage, AJCC N stage, surgery, chemotherapy, marital status, and lung metastasis were highly relevant variables to patients with LM. The AUC, C-index, calibration curves, and DCA curves showed excellent performance of the nomogram. Additionally, the prognostic nomogram accurately classified RCC with LM patients into low- and high-risk groups for mortality.
This study developed a novel nomogram to predict the prognostic factors of RCC with LM, providing a valuable reference for making accurate clinical decisions.
目前关于建立肾细胞癌（RCC）伴肝转移（LM）预后模型的研究较少。本研究旨在开发列线图以预测RCC伴LM的预后。
选取2010年至2021年来自监测、流行病学和最终结果（SEER）数据库中诊断为RCC的患者。采用极端梯度提升（XGBoost）和随机森林（RF）机器学习算法筛选影响预后的最具影响力因素，并采用维恩图方法进行进一步优化。随后，构建了与脑转移相关的列线图。通过受试者操作特征（ROC）曲线、校准图、C指数、时间依赖性C指数和决策曲线分析（DCA）评估列线图的性能。采用Kaplan-Meier（K-M）生存曲线对列线图的临床疗效进行额外验证。
本研究纳入了2395例RCC伴LM患者。维恩图显示，年龄、组织学类型、分级、美国癌症联合委员会（AJCC）T分期、AJCC N分期、手术、化疗、婚姻状况和肺转移是与LM患者高度相关的变量。AUC、C指数、校准曲线和DCA曲线显示列线图具有优异的性能。此外，预后列线图准确地将RCC伴LM患者分为低死亡风险组和高死亡风险组。
本研究开发了一种新型列线图来预测RCC伴LM的预后因素，为做出准确的临床决策提供了有价值的参考。
