Development of a nomogram for predicting cancer-specific survival in patients with renal pelvic cancer following surgery.

Abstract

Objective: The aim of this study is to construct a post-operative nomogram for renal pelvic cancer, thereby addressing a gap in the current academic literature and offering a valuable tool for predicting patient outcomes following surgical intervention.

Methods: This study utilized data from the Surveillance, Epidemiology, and End Results program (2004-2017) to analyze patients diagnosed with renal pelvic cancer who underwent surgery. Variables analyzed included demographics, histology, grade, stage, and treatment modalities. Statistical analysis involved Kaplan-Meier and Cox models, developing a nomogram to predict 1-, 3-, and 5-year cancer-specific survival (CSS), validated through receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA) to assess clinical utility.

Results: The training cohort consisted of 1,486 patients, and the validation cohort comprised 637 patients. Factors affecting CSS, analyzed through univariate and multivariate models, included age, histology, cancer grade, stage, and treatment modalities. The developed nomogram, tested via ROC curves and calibration plots, showed robust predictive accuracy for CSS across both cohorts, with its clinical utility demonstrated through DCA.

Conclusion: Age, histology, grade, and stage were significant risk factors for CSS in renal pelvic urothelial carcinoma post-surgery. A nomogram utilizing these factors aids in evidence-based clinical decision-making.