Sequential Application and Post-Test Probability for Screening of Bladder Cancer Using Urinary Proteomic Biomarkers: A Review based Probabilistic Analysis

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Abstract

Background: Bladder cancer is one of the most common cancers in the world, with men being affected more than women. Diagnosis by cystoscopy, cytology and biopsy is invasive. Urine cytology, a non-invasive modality is not sensitive. This study is undertaken to evaluate whether non-invasive urinary proteomic profiling is more sensitive, specific for bladder cancer. Objective: To evaluate the sensitivity and specificity of various urinary proteomic biomarkers as a screening tool for bladder cancer. Methods: PubMed database was searched from 4th December 2011 to 30th November 2021 using Mesh terms and n = 10,364 articles were found. PRISMA guidelines were followed and Review articles, animal studies, Urinary tract infections, non-bladder cancer and other irrelevant articles were excluded. All studies who have reported mean/median (SD/IQR), sensitivity, specificity, cut off values (ROC analysis) were included (n=5). Post-test probability of various biomarkers was calculated using sequential approach. Pooled analysis was depicted using Forest plot. Results: Analysis of diagnostic studies of bladder cancer showed the post-test probability of CYFRA21-1 was 36.6%. Using sequential approach, the panel of biomarkers CYFRA 21-1, CA-9, APE-1, COL13A1 has post-test probability of 95.10% to diagnose bladder cancer. Analysis of two observational studies with APOE (n= 447) showed non-significant increase of APO-E levels in bladder cancer cases (WMD: 66.41with 95% CI 52.70-185.51; p=0.27, I2 92.4%). Conclusion: In patients presenting with hematuria, a panel of CYFRA 21-1, CA-9, APE-1, COL13A1 markers can be considered for screening of bladder cancer.

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Bhongir, A. V., Sampath, S., Bonthapally, R. K., Gudivada, K. K., & Ramaswamy, G. (2023). Sequential Application and Post-Test Probability for Screening of Bladder Cancer Using Urinary Proteomic Biomarkers: A Review based Probabilistic Analysis. Asian Pacific Journal of Cancer Prevention, 24(6), 2021–2027. https://doi.org/10.31557/APJCP.2023.24.6.2021

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