Abstract
Background: In this study, we aimed to propose a validated prediction model for disease-free survival (DFS) after radical nephroureterectomy (RNU) in a Korean population with upper urinary tract urothelial carcinoma (UTUC). Methods: We performed a retrospective review of 1561 cases of UTUC who underwent either open RNU (ONU, n = 906) or laparoscopic RNU (LNU, n = 615) from five tertiary Korean institutions between January 2000 and December 2012. Data were used to develop a prediction model using the Cox proportional hazards model. Prognostic factors were selected using the backward variable selection method. The prediction model performance was investigated using Harrell's concordance index (C-index) and Hosmer-Lemeshow type 2 statistics. Internal validation was performed using a bootstrap approach, and the National Cancer Center data set (n = 128) was used for external validation. Results: A best-fitting prediction model with seven significant factors was developed. The C-index and two Hosmer-Lemeshow type statistics of the prediction model were 0.785 (95% CI, 0.755-0.815), 4.810 (P = 0.8506), and 5.285 (P = 0.8088). The optimism-corrected estimate through the internal validation was 0.774 (95% CI, 0.744-0.804) and the optimism-corrected calibration curve was close to the ideal line with mean absolute error = 0.012. In external validation, the discrimination was 0.657 (95% CI, 0.560-0.755) and the two calibration statistics were 0.790 (P = 0.9397) and 3.103 (P = 0.5408), respectively. Conclusion: A validated prediction model based on a large Korean RNU cohort was developed with acceptable performance to estimate DFS in patients with UTUC.
Original language | English |
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Pages (from-to) | 4967-4975 |
Number of pages | 9 |
Journal | Cancer medicine |
Volume | 8 |
Issue number | 11 |
DOIs | |
Publication status | Published - 2019 Sep 1 |
Keywords
- nephroureterectomy
- prediction model
- prognosis
- survival
- urothelial carcinoma
ASJC Scopus subject areas
- Oncology
- Radiology Nuclear Medicine and imaging
- Cancer Research