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Novel liquid immunocytochemistry with machine learning analysis for bladder cancer detection
Ankush U Patel1, Samir Atiya2, Yi Song3
1Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
Journal of Histotechnology
|August 15, 2025
Summary
A new liquid-based test using immunocytochemistry and machine learning improves bladder cancer detection. This workflow-optimized platform achieves 100% sensitivity and specificity for urothelial carcinoma in urine samples.
Area of Science:
- Urology
- Oncology
- Diagnostics
Background:
- Bladder cancer diagnosis faces challenges from invasive monitoring and workflow inefficiencies.
- Current methods can impact diagnostic reliability and patient experience.
Purpose of the Study:
- To evaluate a novel liquid-based immunocytochemistry platform with integrated machine learning for detecting urothelial carcinoma in voided urine.
- To address workflow inefficiencies and improve diagnostic reliability in bladder cancer detection.
Main Methods:
- Prospective study of 150 patients (January 2020-December 2022).
- Utilized a liquid-based immunocytochemistry platform with machine learning for analyzing voided urine cytology.
- Evaluated performance of individual markers (hTERT, GATA-3, CK17) and multi-marker analysis.
Main Results:
- The analytic cohort included 142 patients (115 urothelial carcinoma, 27 benign).
- Multi-marker analysis achieved 100% sensitivity (95% CI 96.8-100) when any marker was positive.
- Multi-marker analysis achieved 100% specificity (95% CI 87.3-100) when all three markers were positive.
Conclusions:
- A workflow-optimized platform standardizes specimen preparation and interpretation for urine-based bladder cancer diagnostics.
- Prioritizing workflow and specimen integrity with advanced biomarkers and AI yields high diagnostic performance.
- This approach offers a model for developing clinically practical diagnostic innovations, reducing invasive procedures and improving accuracy.
