Construction and Validation of Novel Prediction Tools Based on Large Population-Based Database to Predict the Prognosis of Urachal Cancer After Surgery

Abstract

Background: Urachal cancer is a rare neoplasm in the urological system. To our knowledge, no published study has explored to establish a model for predicting the prognosis of urachal cancer. The present study aims to develop and validate nomograms for predicting the prognosis of urachal cancer based on clinicopathological parameters.

Methods: Based on the data from the Surveillance, Epidemiology, and End Results database, 445 patients diagnosed with urachal cancer between 1975 and 2018 were identified as training and internal validation cohort; 84 patients diagnosed as urachal cancer from 2001 to 2020 in two medical centers were collected as external validation cohort. Nomograms were developed using a multivariate Cox proportional hazards regression analysis in the training cohort, and their performance was evaluated in terms of its discriminative ability, calibration, and clinical usefulness by statistical analysis.

Results: Three nomograms based on tumor-node-metastasis (TNM), Sheldon and Mayo staging system were developed for predicting cancer-specific survival (CSS) of urachal cancer; these nomograms all showed similar calibration and discrimination ability. Further internal (c-index 0.78) and external (c-index 0.81) validation suggested that Sheldon model had superior discrimination and calibration ability in predicting CSS than the other two models. Moreover, we found that the Sheldon model was able to successfully classify patients into different risk of mortality both in internal and external validation cohorts. Decision curve analysis proved that the nomogram was clinically useful and applicable.

Conclusions: The nomogram model with Sheldon staging system was recommended for predicting the prognosis of urachal cancer. The proposed nomograms have promising clinical applicability to help clinicians on individualized patient counseling, decision-making, and clinical trial designing.

Keywords: SEER; nomogram; predictors; prognosis; urachal cancer.

Figure 1

Flowchart illustrating patient selection for this study.

Figure 2

(A) Nomogram of Sheldon model for prediction of cancer specific survival (CSS) of urachal cancer. (B) C-index of three nomograms at different time points in the training cohort. (C) Calibration plot of three nomograms for prediction of CSS at 3 years in the training cohort.

Figure 3

(A) C-index of three nomograms in the internal validation cohort. (B) Calibration plot of three nomograms in the internal validation cohort. (C) C-index of three nomograms in the external validation cohort. (D) Calibration plot of three nomograms in the external validation cohort.

Figure 4

(A) Kaplan–Meier curves of different risk groups stratified by the Sheldon model in the internal training cohort. (B) Different risk groups stratified by the Sheldon model in the external validation cohort.

Figure 5

Decision curve analysis for the Sheldon model in the internal (A) and external (B) training cohort. The horizontal solid orange line represents the assumption that no patients will die, and the solid green line represents the assumption that all patients will die. On decision curve analyses, the nomogram showed superior net benefit across all range of threshold probabilities.