Prognostic factors and nomogram development for survival in renal cell carcinoma patients with multiple primary cancers: a retrospective study.

Abstract

Background: Patients with renal cell cancer have an increased risk of developing multiple primary cancers (MPCs) due to improved survival rates. The purpose of this study was to evaluate the clinicopathological features of MPCs and to generate a useful tool for predicting cancer-specific survival (CSS) in these patients.

Methods: A retrospective analysis was conducted on data from renal cell carcinoma (RCC) who were diagnosed with MPCs between 2001 and 2021 from the Surveillance, Epidemiology, and End Results (SEER) database. Patients with RCC meeting the criteria were selected for Kaplan-Meier (KM) survival analysis. The main outcome of this study was CSS, defined as the time from the initial diagnosis to either death due to cancer or the last follow-up. The Cox regression model was used to analyze the CSS factors of MPCs, the results of the multivariate analysis were displayed in a forest map, and the significant variables identified in the multivariate Cox analysis were used to construct the nomogram. Area under the curve (AUC) and calibration plots were used to evaluate the predictive performance of the nomogram.

Results: A total of 2,078 cases of renal cancer with MPCs diagnosed between 2001 and 2021 were included. Age and grade were determined through both univariate and multivariate analyses to be independent prognostic factors affecting CSS. Based on clinical practice, the final nomogram was constructed using the variables: sex, age, grade, summary stage, tumor-node-metastasis (TNM) stage and tumor size to predict CSS at 60, 120, and 180 months. The concordance index (C-index) for the CSS nomogram was 0.670 [95% confidence interval (CI): 0.642-0.698]. The model demonstrated a good predictive performance. To assess the consistency between observed and predicted values, a calibration curve was developed.

Conclusions: This study identified risk factors for CSS in patients with clear cell RCC (ccRCC) with MPCs and developed a nomogram to predict CSS in these patients. The model demonstrates strong clinical applicability and can serve as a valuable clinical decision-making tool for physicians and patients.