ArticleInternational urology and nephrology2026
Effect of frailty status and histopathological features on upstaging of non-muscle-invasive bladder cancer: a critical analysis based on machine learning.
Article in International urology and nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence and predictive tools in non-muscle invasive bladder cancer: a narrative review of current insights and advances.Translational andrology and urology · 2026Review
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7 authors.
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Abstract
aimThis study aimed to develop a machine learning (ML)-assisted model to predict the risk of upstaging (subsequent higher stage on repeat pathology) in non-muscle-invasive bladder cancer (NMIBC).
methodsA retrospective cohort study was conducted on 380 patients with NMIBC. Comprehensive data on demographics, comorbidities, frailty (modified frailty index, mFI), nutritional status (nutritional risk index, NRI), inflammatory indices, and detailed histopathological features were collected. The primary outcome was pathological upstaging, defined as a diagnosis of a higher T stage (from Ta to ≥ T1 or from T1 to ≥ T2) on a subsequent procedure. Multivariate logistic regression and six machine learning models were developed and evaluated using tenfold cross-validation, and their performances in predicting upstaging were directly compared.
resultsPathological upstaging occurred in 84 patients (22.1%). Multivariate logistic regression identified initial T1 stage (OR: 13.54), high tumor grade (OR: 4.60), lymphovascular invasion (LVI) (OR: 3.67), frailty (mFI ≥ 2, OR: 2.68), and lower estimated glomerular filtration rate (eGFR) as factors independently associated with upstaging (AUC: 0.702). Machine learning models, particularly the support vector machine (SVM), demonstrated superior predictive performance (AUC: 0.796). Analysis using supervised learning algorithms confirmed tumor grade as the strongest associated factor, followed by initial T stage, the presence of lymphovascular invasion on initial TURBT (transurethral resection of the bladder tumor), frailty, eGFR, and occupational exposure.
conclusionsThis study demonstrates that artificial intelligence models provide a superior framework for predicting subsequent pathological upstaging in NMIBC compared to traditional multivariate logistic regression. The ML-driven analysis adequately validated established clinical risk factors associated with upstaging. Among all variables evaluated, high tumor grade emerged as the most powerful and clinically significant factor associated with upstaging. Thus, ML-assisted tools may feasibly be integrated into clinical practice to enhance risk stratification.
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