Multifactorial, site-specific recurrence models after radical cystectomy for urothelial carcinoma: external validation in a cohort of Korean patients.
retrospective_cohort · Level III
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- Also identified by DOI 10.1371/journal.pone.0100491 and PMC identifier 4061079.
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Abstract
The aim of this study was to evaluate the accuracy of site-specific recurrence models after radical cystectomy in the Korean population. We conducted a review of an electronic medical record of 572 patients who underwent radical cystectomy for urothelial carcinoma of the bladder. Primary end point was the site-specific recurrence after radical cystectomy. The median follow-up in the validation cohort was 42.3 months (interquartile range: 23.0-89.3 months). During the follow-up period, there were 165 patients (28.8%), 85 (14.9%), 31 (5.4%), and 78 (13.6%) who recurred in abdomen/pelvis, thoracic region, upper urinary tract, and bone, respectively. The c-indices of abdomen/pelvis, thoracic region, upper urinary tract, and bone models 3 years after radical cystectomy were 0.69 (95% confidence interval [CI], 0.65-0.73), 0.69 (95% CI, 0.64-0.75), 0.61 (95% CI, 0.52-0.69), and 0.65 (95% CI, 0.59-0.71), respectively. Kaplan-Meier curves demonstrated that models discriminated well and log-rank test were all highly significant (all p<0.001), except upper urinary tract model (p = 0.366). Decision curve analysis revealed that the use of prediction models for abdomen/pelvis, thoracic region, and bone recurrence was associated with net benefit gains relative to the treat-all strategy, but not the model for upper urinary tract recurrence. Abdomen/pelvis, thoracic region, and bone models demonstrate moderate discrimination, adequate calibration, and meaningful net benefit gains, whereas upper urinary tract model does not seem applicable to patients from Asia because it has suboptimal accuracy.
Medical subject headings
- Bone Neoplasms
- Cystectomy
- Models, Statistical
- Neoplasm Recurrence, Local
- Pelvic Neoplasms
- Thoracic Neoplasms
- Urinary Bladder Neoplasms