ReviewCanadian Urological Association journal = Journal de l'Association des urologues du Canada2024
Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer A comprehensive systematic review and meta-analysis.
Review in Canadian Urological Association journal = Journal de l'Association des urologues du Canada, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Beyond the Rotisserie: Dispelling the Myths of Intravesical Therapy and Pioneering a Precision Future for Bladder Cancer.Urology research & practice · 2026Article
- Neoadjuvant Immunotherapy in Bladder Cancer: Ushering in a New Era of Treatment-A Systematic Review of Current Evidence.European urology open science · 2025Review
- Non-Invasive Tumor Budding Evaluation and Correlation with Treatment Response in Bladder Cancer: A Multi-Center Cohort Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- The Use of Artificial Intelligence in Urologic Oncology: Current Insights and Challenges.Research and reports in urology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionNeoadjuvant cisplatin-based combination chemotherapy (NAC) followed by radical cystectomy is the standard of care for cisplatin-fit patients harboring muscle-invasive bladder cancer (MIBC). Prediction of response to NAC is essential for clinical decision-making regarding alternatives in case of non-response, and bladder-sparing in case of complete response. This research aimed to assess the performance of machine learning in predicting therapeutic response following NAC treatment in patients with MIBC.
methodsA systematic review adhering to the PRISMA guidelines was conducted until July 2023. The study integrated articles relating to artificial intelligence and NAC response in MIBC from various databases. The quality of articles was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies 2 (QUADAS-2). A meta-analysis was subsequently performed on selected studies to determine the sensitivity and specificity of machine learning algorithms in predicting NAC response.
resultsOf 655 articles identified, 12 studies comprising 1523 patients were included, and four studies were eligible for meta-analysis. The sensitivity and specificity of the studies were 0.62 (95% confidence interval [CI] 0.50-0.72) and 0.82 (95% CI 0.72-0.89), respectively, with a heterogeneity score (I
conclusionsMachine-learning algorithms, especially those using computed tomography, genetic, and pathologic data, demonstrate significant potential for predicting NAC response in MIBC. Standardization of methodologic data analysis and response criteria are needed as validation studies.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.