Abstract：
BACKGROUND: To develop a novel nomogram for predicting 2-year and 5-year disease-free survival (DFS) and overall survival (OS) in patients with cT1-clear cell renal cell carcinoma (ccRCC) undergoing partial nephrectomy (PN).
METHODS: A retrospective study was conducted across five urological centers, including 940 patients who underwent PN for cT1N0M0-ccRCC. Four centers were randomly selected to constitute the training group, while the remaining center served as the testing group. We employed the LASSO and multivariate Cox regression to develop new nomograms. The 1,000 bootstrap-corrected c-index, net reclassification improvement (NRI) and receiver operating characteristic curve were employed to compare the predictive abilities of new nomograms with the widely used UUIS and SSIGN models. Finally, the novel nomograms underwent external validation.
RESULTS: The training group included 714 patients, while the testing group consisted of 226 patients. The bootstrap-corrected c-indexes for the DFS and OS model were 0.870 and 0.902, respectively. In the training cohort, the AUC for the DFS and OS models at 2 years and 5 years were 0.953, 0.902, 0.988, and 0.911, respectively. These values were also assessed in the testing cohort. The predictive capabilities of the new nomograms surpassed those of the UUIS and SSIGN models (NRI > 0). Decision curve analysis demonstrated that the novel nomograms provide greater net benefits compared to the UUIS and SSIGN models.
CONCLUSIONS: Our novel nomograms demonstrated strong predictive ability for forecasting oncological outcomes in cT1-ccRCC patients after PN. These user-friendly nomograms are simple and convenient for clinical application, providing tangible clinical benefits.
METHODS: A retrospective study was conducted across five urological centers, including 940 patients who underwent PN for cT1N0M0-ccRCC. Four centers were randomly selected to constitute the training group, while the remaining center served as the testing group. We employed the LASSO and multivariate Cox regression to develop new nomograms. The 1,000 bootstrap-corrected c-index, net reclassification improvement (NRI) and receiver operating characteristic curve were employed to compare the predictive abilities of new nomograms with the widely used UUIS and SSIGN models. Finally, the novel nomograms underwent external validation.
RESULTS: The training group included 714 patients, while the testing group consisted of 226 patients. The bootstrap-corrected c-indexes for the DFS and OS model were 0.870 and 0.902, respectively. In the training cohort, the AUC for the DFS and OS models at 2 years and 5 years were 0.953, 0.902, 0.988, and 0.911, respectively. These values were also assessed in the testing cohort. The predictive capabilities of the new nomograms surpassed those of the UUIS and SSIGN models (NRI > 0). Decision curve analysis demonstrated that the novel nomograms provide greater net benefits compared to the UUIS and SSIGN models.
CONCLUSIONS: Our novel nomograms demonstrated strong predictive ability for forecasting oncological outcomes in cT1-ccRCC patients after PN. These user-friendly nomograms are simple and convenient for clinical application, providing tangible clinical benefits.
摘要：
背景：开发一种新的列线图，用于预测接受部分肾切除术（PN）的cT1透明细胞肾细胞癌（ccRCC）患者的2年和5年无病生存期（DFS）和总生存期（OS）。
方法：在五个泌尿外科中心进行了一项回顾性研究，包括940例因cT1N0M0-ccRCC接受PN治疗的患者。随机抽取四个中心组成训练组,而其余的中心作为测试组。我们采用LASSO和多变量Cox回归来开发新的列线图。1000个引导校正的c指数，采用净重新分类改进（NRI）和受试者工作特征曲线来比较新的列线图与广泛使用的UUIS和SSIGN模型的预测能力。最后,新的列线图经过外部验证.
结果：训练组包括714名患者，而测试组由226名患者组成。DFS和OS模型的自举校正c指数分别为0.870和0.902。在训练组中，DFS和OS模型在2年和5年的AUC分别为0.953,0.902,0.988和0.911.这些值也在测试队列中进行评估。新的列线图的预测能力超过了UUIS和SSIGN模型(NRI>0)。决策曲线分析表明，与UUIS和SSIGN模型相比，新颖的列线图提供了更大的净收益。
结论：我们的新的列线图显示了预测PN后cT1-ccRCC患者肿瘤结局的强大预测能力。这些用户友好的列线图是简单和方便的临床应用，提供切实的临床效益。
方法：在五个泌尿外科中心进行了一项回顾性研究，包括940例因cT1N0M0-ccRCC接受PN治疗的患者。随机抽取四个中心组成训练组,而其余的中心作为测试组。我们采用LASSO和多变量Cox回归来开发新的列线图。1000个引导校正的c指数，采用净重新分类改进（NRI）和受试者工作特征曲线来比较新的列线图与广泛使用的UUIS和SSIGN模型的预测能力。最后,新的列线图经过外部验证.
结果：训练组包括714名患者，而测试组由226名患者组成。DFS和OS模型的自举校正c指数分别为0.870和0.902。在训练组中，DFS和OS模型在2年和5年的AUC分别为0.953,0.902,0.988和0.911.这些值也在测试队列中进行评估。新的列线图的预测能力超过了UUIS和SSIGN模型(NRI>0)。决策曲线分析表明，与UUIS和SSIGN模型相比，新颖的列线图提供了更大的净收益。
结论：我们的新的列线图显示了预测PN后cT1-ccRCC患者肿瘤结局的强大预测能力。这些用户友好的列线图是简单和方便的临床应用，提供切实的临床效益。
