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Related Experiment Video

Updated: Oct 25, 2025

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System
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A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

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Developing non-invasive bladder cancer screening methodology through potentiometric multisensor urine analysis.

Regina Belugina1, Evgenii Karpushchenko2, Aleksandr Sleptsov2

  • 1ITMO University, St Petersburg, Russia.

Talanta
|August 8, 2021
PubMed
Summary

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This study shows a simple electrochemical sensor can help distinguish bladder cancer urine samples. Machine learning models achieved 72% accuracy, improving to 80% sensitivity in older adults.

Area of Science:

  • Biomedical Engineering
  • Oncology
  • Analytical Chemistry

Background:

  • Bladder cancer diagnosis relies on invasive procedures.
  • There is a need for non-invasive, accurate diagnostic tools.
  • Electrochemical sensors offer potential for rapid, point-of-care diagnostics.

Purpose of the Study:

  • To evaluate the feasibility of an electrochemical multisensor system for bladder cancer detection.
  • To differentiate urine samples from bladder cancer patients and healthy individuals using machine learning.
  • To assess the performance of various machine learning algorithms for this diagnostic task.

Main Methods:

  • Collected urine samples from 36 bladder cancer patients and 51 healthy volunteers.
  • Utilized a simple potentiometric electrochemical multisensor system.
Keywords:
Bladder cancerClassificationMachine learningMultisensor systemNon-invasive screening“Electronic tongue”

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  • Applied machine learning algorithms (logistic regression, random forest, XGBoost, SVM, voting classifier) to sensor data.
  • Evaluated classifier performance using Monte-Carlo cross-validation.

    • Main Results:

    • The best model achieved 72% accuracy, 71% sensitivity, and 58% specificity across all participants.
    • Performance improved to 76% accuracy, 80% sensitivity, and 75% specificity when focusing on an age group of 50-88 years.
    • The system demonstrated potential in distinguishing between cancer patients and healthy controls.

    Conclusions:

    • The electrochemical multisensor system is a feasible tool for bladder cancer screening.
    • Machine learning enhances the diagnostic capability of the sensor system.
    • The proposed non-invasive screening method shows promise for further research and development.