ArticleCancer management and research2020
Development and Validation of a Model for Predicting Urethral Recurrence in Male Patients with Muscular Invasive Bladder Cancer After Radical Cystectomy Combined with Urinary Diversion.
Article in Cancer management and research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Development of a machine learning-based model to predict urethral recurrence following radical cystectomy: a multicentre retrospective study and updated meta-analysis.Scientific reports · 2025Pooled it
- Carbon ion radiotherapy for bladder cancer: A case report.World journal of clinical cases · 2021Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeRadical cystectomy (RC) is the primary treatment strategy for patients with muscular invasive bladder cancer (MIBC). However, the prognosis is poor and tumor recurrence is not rare, in particular, urethral recurrence (UR) in male patients who underwent RC combined with urinary diversion. Here, we have developed and validated a model for predicting UR in these patients. PATIENTS AND
methodsThe development cohort comprised 310 patients who underwent RC combined with urinary diversion at our center between 1 January 2007 and 31 December 2015. Clinicopathologic data of patients were comprehensively recorded. Multivariate Cox proportional hazard regression was used for building a predictive model with regression coefficients and backward stepwise selection applied by utilizing the likelihood ratio test with Akaike's information criterion as the stopping rule. An independent cohort consisting of 131 consecutive patients treated from 1 January 2016 to 31 December 2017 was used for validation. The performance of this predictive model was assessed with respect to discrimination, calibration, and clinical usefulness.
resultsThe predictors of this model included body mass index, history of transurethral resection of bladder tumor, tumor grade, tumor stage, and concomitant carcinoma in situ. In the validation cohort, the model showed good discrimination with a concordance index of 0.777 (95% CI, 0.618 to 0.937) and calibration. Decision curve analysis also demonstrated the clinical utility of the model.
conclusionThe predictive model facilitated postoperative individualized prediction of UR in male patients with MIBC after RC combined with urinary diversion and it may therefore serve to improve follow-up strategies.
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