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Erschienen in:
01.11.2015 | Original Article
Risk stratification for locoregional recurrence after radical cystectomy for urothelial carcinoma of the bladder
Erschienen in: World Journal of Urology | Ausgabe 11/2015
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Purpose
To externally validate the Christodouleas risk model incorporating pathological tumor stage, lymph node (LN) count and soft tissue surgical margin (STSM) and stratifying patients who develop locoregional recurrence (LR) after radical cystectomy (RC) for urothelial carcinoma of the bladder (UCB). In addition, we aimed to generate a new model including established clinicopathological features that were absent in the Christodouleas risk model.
Methods
Prospectively assessed multicenter data from 565 patients undergoing RC for UCB in 2011 qualified for final analysis. For the purpose of external validation, risk group stratification according to Christodouleas was performed. Competing-risk models were calculated to compare the cumulative incidences of LR after RC.
Results
After a median follow-up of 25 months (interquartile range 19–29), the LR-rate was 11.5 %. The Christodouleas model showed a predictive accuracy of 83.2 % in our cohort. In multivariable competing-risk analysis, tumor stage ≥pT3 (HR 4.32, p < 0.001), positive STSM (HR 2.93, p = 0.005), lymphovascular invasion (HR 3.41, p < 0.001), the number of removed LNs <10 (HR 2.62, p < 0.001) and the administration of adjuvant chemotherapy (HR 0.40, p = 0.008) independently predicted the LR-rate. The resulting risk groups revealed significant differences in LR-rates after 24 months with 4.8 % for low-risk patients, 14.7 % for intermediate-risk patients and 38.9 % for high-risk patients (p < 0.001 for all), with a predictive accuracy of 85.6 %, respectively.
Conclusions
The Christodouleas risk model has been successfully externally validated in the present prospective series. However, this analysis finds that overall model performance may be improved by incorporating lymphovascular invasion. After external validation of the newly proposed risk model, it may be used to identify patients who benefit from an adjuvant therapy and suit for inclusion in clinical trials.