Related Experiment Video
Updated: Feb 6, 2026
An Orthotopic Bladder Tumor Model and the Evaluation of Intravesical saRNA Treatment
Published on: July 28, 2012
Predicting Response to Intravesical Therapy in Non-muscle-invasive Bladder Cancer
Mahmoud Alameddine1, Omer Kineish1, Chad Ritch2
1Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA.
Context:
The ability to predict response to intravesical therapy (IVT) following transurethral resection in non-muscle-invasive bladder cancer holds important prognostic information. However, few predictive tools are available to guide urologists.
Objective:
We reviewed the most recent studies investigating the predictors of response to IVT.
Evidence Acquisition:
A literature search was conducted using PubMed database from January 1, 2013 to April 1, 2018 following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria. For our search strategy, we used the combination of the MeSH terms of "Administration, Intravesical" and "Urinary Bladder Neoplasms" with any of the following words: "Biomarkers," "Predictive Value of Tests," "response," "recurrence," and "progression." We limited our search to the English language.
Evidence Synthesis:
Risk stratification models utilizing clinicopathological features are the most cost-effective and widely used tools currently available to predict response to IVT. Additionally, urinary fluorescence in situ hybridization testing and urinary cytokine-based nomograms (Cytokine Panel for Response to Intravesical Therapy) may enhance predictive ability. Protein-based biomarkers have been associated with predicting recurrence. Several gene-based biomarkers quantifying mutations in DNA damage repair genes may have predictive ability. However, genomic data are relatively new and lack validation.
Conclusions:
Clinicopathological criteria remain the most widely utilized tool for predicting IVT response. Further research to validate protein- and genomic-based biomarkers are needed before adoption in clinical practice.
Patient Summary:
We reviewed contemporary studies that investigated how to predict response to medication instilled in the bladder (intravesical therapy) for bladder cancer. We found that most predictive tools use clinical data, such as tumor stage and grade, to determine the outcome. Newer biological (gene, protein, cytokines) marker tests are being studied. We concluded that the combination of clinical data with levels of certain experimental markers (fluorescence in situ hybridization test or urinary cytokines) may improve predictive ability. Genetic testing methods may also yield additional predictive markers in the future, but this needs more validation.
Related Concept Videos
02:49Cancer Therapies
However, cancer treatments can pose several challenges, as therapies used to kill cancer cells are generally also toxic to normal cells. Moreover, cancer cells mutate rapidly and can develop resistance to chemical agents or radiation therapy. Besides, all types of cancer cells may not respond to the same therapy. Some cancer cells respond to one...
02:57Targeted Cancer Therapies
There are several types of targeted therapies against...
01:14Design Example: Frog Muscle Response
When the switch connecting the RL circuit is closed, a brief muscle contraction is observed. This is because, at a steady state, the inductor acts like a short...
02:27Predicting Molecular Geometry
01:23Urinary Bladder
In males, the bladder is situated in front of the rectum, while in females, it is positioned anterior to the vagina and uterus. The bladder floor contains an inverted triangular area called the trigone, defined by the two ureteric...
01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.