AUTHOR=Hu Yongtao , Xu Shun , Qi Qiao , Wang Xuhong , Meng Jialin , Zhou Jun , Hao Zongyao , Liang Qianjun , Feng Xingliang , Liang Chaozhao 

TITLE=A novel nomogram and risk classification system predicting the overall survival of patients with papillary renal cell carcinoma after nephrectomy: A population-based study

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.989566

DOI=10.3389/fpubh.2022.989566

ISSN=2296-2565

ABSTRACT=Background: Papillary renal cell carcinoma (pRCC) is the largest histologic subtype of nonclear-cell RCC. To date, there is no reliable nomogram to predict the prognosis of patients with pRCC after nephrectomy. We aimed to first establish an effective nomogram to predict the overall survival (OS) of pRCC patients after nephrectomy.

Methods: A total of 3,528 eligible pRCC patients after nephrectomy were identified from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2015. Patients were randomized into the training cohort (n=2,472) and the validation cohort (n=1,056) at a 7:3 ratio. 122 real-world samples from our institute (titled the AHMU-pRCC cohort) were used as the external validation cohort. Univariate and subsequent multivariate Cox regression analyses were conducted to identify OS-related prognostic factors, which were further used to establish a prognostic nomogram for predicting the 1-, 3-, and 5-year OS probabilities. The performance of the nomogram was evaluated by the concordance index (C-index), receiver operating characteristic curve (ROC), calibration plot and decision curve analysis (DCA).  

Results: Multivariate Cox analysis showed that age, race, marital status, TNM stage, tumor size and surgery were significant OS-related prognostic factors. A prognostic model consisting of these clinical parameters was developed and virtualized by a nomogram. High C-index and area under the ROC curve (AUC) values of the nomogram at 1, 3, and 5 years were found in the training, validation and AHMU-pRCC cohorts. The calibration plot and DCA also showed that the nomogram had a satisfactory clinical application value. A risk classification system was established to risk-stratify pRCC patients.

Conclusion: Based on a large cohort from the public SEER database, a reliable nomogram predicting the OS of pRCC patients after nephrectomy was constructed, which could optimize the survival assessment and clinical treatment.