Beyond staging: toward a layered risk architecture after radical cystectomy for bladder cancer
Year: 2026
Type: article
Abstract: Radical cystectomy (RC) and the unfinished problem of risk predictionRC remains one of the most consequential interventions in urologic oncology: potentially curative, biologically revealing, and accompanied by substantial perioperative morbidity and long-term prognostic uncertainty (1).Once the bladder has been removed and final pathology is available, the central question is no longer whether the patient had high-risk disease, but how multiple signals of risk should be integrated into postoperative decisionmaking (1,2).Pathologic T stage, nodal involvement, lymphovascular invasion (LVI), surgical margin status, systemic treatment exposure, frailty, comorbidity, inflammatory status, and host reserve are all considered in daily practice (1-3).Yet this integration is often implicit rather than structured, and rarely occurs within a transparent, validated, clinically actionable framework.This is the context in which the systematic review and meta-analysis by Artiles Medina and colleagues should be read (4).The authors did not simply summarize isolated prognostic variables after RC.Rather, they focused on multivariable survival prediction models and synthesized adjusted hazard ratios (HRs) from published Cox proportional hazards models.Their review included 77 studies, comprising models for overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS) (4).The resulting analysis provides an important overview of clinicopathologic factors that retain independent prognostic relevance across heterogeneous cohorts.The study does not deliver a ready-to-use clinical nomogram.Nor should it be judged by that standard.Its more important contribution is to define an adjusted prognostic architecture after RC.In other words, it shows which variables repeatedly remain informative after multivariable adjustment, and which domains of risk should be carried forward into the next generation of post-
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7204747694