Nomograms to predict overall and cancer-specific survival in patients with upper tract urothelial carcinoma: a large...

To develop and validate survival nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in upper tract urothelial carcinoma (UTUC) patients.

Patients diagnosed with UTUC from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively enrolled. Clinical characteristics and survival outcomes were respectively collected from the included patients. Then, eligible patients were divided into the training cohort and the validation cohort. Additionally, survival nomograms were developed based on the results of multivariate Cox analysis in the training cohort. Furthermore, Kaplan-Meier (KM) survival curves were generated to assess the actual effect of each variable. Lastly, the nomograms were validated using the concordance index (C-index), the area under the receiver operating characteristic (ROC) curve and calibration curves.

Totally, 3,556 patients were included, with 2,492 in the training cohort and 1,064 in the validation cohort. No significant differences were detected in comparisons in clinical characteristics between two cohorts. Based on the results of uni- and multivariate Cox regression analysis, seven factors (age, TNM stage, use of surgery/radiation and marital status) for OS and six factors (age, TNM stage and use of surgery/radiation) for CSS were selected to develop the survival nomograms. The C-index for OS and CSS was 0.763 and 0.793 in the training cohort, and 0.759 and 0.784 in the validation cohort. Additionally, the 3- and 5-year AUCs for OS were 0.808 and 0.780 in the training cohort, and 0.785 and 0.778 in the validation group. As for CSS, it was 0.833 and 0.803 in the training cohort, and 0.815 and 0.810 in the validation cohort. Lastly, the calibration curves indicated a good consistency between the actual survival and the predictive survival.

It was the first time to conduct survival models for UTUC patients with predictive performance. It might be valuable of clinical application and further exploration with more studies in the future.