Postoperative Nomogram for Relapse-Free Survival in Patients with High Grade Upper Tract Urothelial Carcinoma

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We developed a prognostic nomogram for patients with high grade urothelial carcinoma of the upper urinary tract after extirpative surgery.

Materials and Methods:

Clinical data were available for 2,926 patients diagnosed with high grade urothelial carcinoma of the upper urinary tract who underwent extirpative surgery. Cox proportional hazard regression models identified independent prognosticators of relapse in the development cohort (838). A backward step-down selection process was applied to achieve the most informative nomogram with the least number of variables. The L2-regularized logistic regression was applied to generate the novel nomogram. Harrell’s concordance indices were calculated to estimate the discriminative accuracy of the model. Internal validation processes were performed using bootstrapping, random sampling, tenfold cross-validation, LOOCV, Brier score, information score and F1 score. External validation was performed on an external cohort (2,088). Decision tree analysis was used to develop a risk classification model. Kaplan-Meier curves were applied to estimate the relapse rate for each category.


Overall 35.3% and 30.7% of patients experienced relapse in the development and external validation cohort. The final nomogram included age, pT stage, pN stage and architecture. It achieved a discriminative accuracy of 0.71 and 0.76, and the AUC was 0.78 and 0.77 in the development and external validation cohort, respectively. Rigorous testing showed constant results. The 5-year relapse-free survival rates were 88.6%, 68.1%, 40.2% and 12.5% for the patients with low risk, intermediate risk, high risk and very high risk disease, respectively.


The current nomogram, consisting of only 4 variables, shows high prognostic accuracy and risk stratification for patients with high grade urothelial carcinoma of the upper urinary tract following extirpative surgery, thereby adding meaningful information for clinical decision making.

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