Development and Validation of a Nomogram to Predict Cancer-Specific Survival in Elderly Patients With Papillary Renal Cell Carcinoma - Zhanghuang, Chenghao; Wang, Jinkui; Yao, Zhigang; Li, Li; Xie, Yucheng; Tang, Haoyu; Zhang, Kun; Wu, Chengchuang; Yang, Zhen; Yan, Bing (Frontiers in Public Health, 2022)
Objective: Papillary renal cell carcinoma (pRCC) is the second most common type of renal cell carcinoma and an important disease affecting older patients. We aimed to establish a nomogram to predict cancer-specific survival (CSS) in elderly patients with pRCC. Methods: Patient information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) project, and we included all elderly patients with pRCC from 2004 to 2018. All patients were randomly divided into a training cohort and a validation cohort. Proportional subdistribution hazard (SH) model were used to identify patient independent risk factors. We constructed a nomogram based on a multivariate Cox regression model to predict CSS for 1-, 3-, and 5- years in elderly patients with pRCC. A series of validation methods were used to validate the accuracy and reliability of the model, including consistency index (C-index), calibration curve, net reclassification improvement(NRI), integrated discrimination improvement (IDI), and the area under the Subject operating curve (AUC). Results: A total of 13,105 elderly patients with pRCC were enrolled. SH analysis suggested that age, tumour size, histological grade, TNM stage, surgery, radiotherapy and chemotherapy were independent risk factors for survival. We constructed a nomogram to predict patients' CSS. The training and validation cohort's C-index were 0.862 (95%CI:0.852-0.872) and 0.861(95%CI: 0.855-0.867), respectively, suggesting that the model had good discrimination ability. The AUC showed the same results. The calibration curve also indicates that the model has good accuracy. The NRI, IDI, and DCA of the nomogram showed that it performs better than TNM staging. Conclusions: In this study, we constructed a nomogram to predict the CSS of elderly pRCC patients, which has good accuracy and reliability and can help doctors and patients make clinical decisions.