Abstract
Background: Early-onset kidney cancer (EOKC) is often associated with genetic factors and a high risk of metastasis. However, there is a lack of accurate prediction models for the prognosis of EOKC. The aim of this study is to establish an effective nomogram for predicting and evaluating the prognosis of patients with EOKC. Methods: The patients with EOKC were selected from the latest SEER database during 2004-2015. Patients between 2004 and 2014 were randomly divided into a training cohort and a validation cohort at a ratio of 7:3, and patients in 2015 were used for temporal external validation. Additionally, we included patients from First Hospital of Shanxi Medical University between 2013 and 2021 for spatial external validation. The performance of the nomogram was assessed using the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Patients were stratified based on the nomogram, and Kaplan-Meier (KM) curves were plotted to compare the survival probability of patients. Results: In the temporal and spatial external validation cohort, the C-index of the nomogram for OS was 0.872 and 0.875, respectively, and the C-index of the nomogram for CSS were 0.872 and 0.851, respectively. In the temporal external validation cohort, the 1-year, 3-year and 5-year AUC of the nomogram for OS were 0.906, 0.899 and 0.876, respectively. In addition, the AUC showed that the nomogram had also high predictive ability for CSS. The calibration curves and DCA also indicated that the nomogram had a strong clinical utility. The KM curve revealed that patients in the low-risk group had a better prognosis than those in the high-risk group. Conclusion: Our study developed a novel high-performance nomogram for assessing the prognosis of patients with EOKC, and it has great potential for clinicians to assess patient prognosis and formulate effective intervention and follow-up strategies.