Bladder cancer screening: The new selection and prediction model.

Area of Science:

• Urology
• Oncology
• Public Health

Background:

• Bladder cancer (BC) screening requires effective methods for identifying at-risk individuals and asymptomatic cases.
• Early detection and prevention are crucial for improving BC outcomes.

Purpose of the Study:

• To develop and validate a novel approach for selecting individuals with asymptomatic bladder cancer (BC) and those at high risk of developing BC.
• To establish a tool for optimizing BC screening protocols at the population level.

Main Methods:

• A hospital-based, matched case-control study involving 100 newly diagnosed BC patients and 100 controls.
• Statistical analysis included t-tests, univariate and multivariate logistic regression, and scoring models.
• Six significant variables were identified: age over 45, Caucasian ethnicity, smoking history, carcinogen exposure, macrohematuria, urinary difficulty, and family history of BC.

Main Results:

• The developed model demonstrated high accuracy with an area under the ROC curve of 0.913.
• The model achieved a sensitivity of 91% and a specificity of 78% for identifying individuals with BC or at high risk.
• Positive predictive value was 80.5% and negative predictive value was 89.7%.

Conclusions:

• The validated model provides an easy and fast selection tool for asymptomatic BC patients and high-risk individuals.
• This approach can aid in primary and primordial prevention strategies for bladder cancer.
• The study represents the initial phase of a larger BC screening protocol, with ongoing urine analysis research.