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Urine Studies I: Urinalysis

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Urinalysis is a widely used diagnostic test that analyzes urine's physical, chemical, and microscopic characteristics. Healthcare providers use it to detect and monitor various health conditions, including renal disease, urinary tract infections (UTIs), diabetes, and metabolic or systemic disorders.Components of UrinalysisUrinalysis consists of three primary components: physical, chemical, and microscopic examination. Each provides unique insights into the urine sample and, by extension, the...

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A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

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Published on: January 12, 2017

Urine-Based Machine Learning Assay Detects Prostate Cancer.

Marvin S Hausman¹, Kyle Ambert², Abhignyan Nagesetti²

¹Genetics Institute of America, Delray Beach, FL 33445, USA.

Diagnostics (Basel, Switzerland)

|April 14, 2026

Summary

A novel urine test using engineered hydrogels and machine learning shows high sensitivity for prostate cancer detection. This non-invasive assay could improve prostate cancer screening accessibility and reduce unnecessary biopsies.

Keywords:

diagnostic liquid biopsy non-invasive prostate cancer random forest urine

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Area of Science:

Urology
Biochemistry
Computational Biology

Background:

Current prostate cancer screening methods like PSA testing and digital rectal exams have limitations in specificity and accessibility.
Barriers to access include cultural and geographic factors, impacting diverse populations.
There is a need for non-invasive, accurate, and accessible prostate cancer detection methods.

Purpose of the Study:

To develop and evaluate a non-invasive, urine-based liquid biopsy assay for prostate cancer detection.
To utilize engineered hydrogel arrays and machine learning for identifying disease-specific biochemical profiles in urine.
To assess the assay's sensitivity and specificity across different prostate cancer grades.

Main Methods:

Voided urine samples were collected from 283 participants prior to prostate biopsy across 26 U.S. urology practices.
An assay using engineered hydrogel arrays was developed to analyze urine for disease-specific biochemical profiles.
Random forest classifiers were trained on colorimetric signatures from 184 biopsy-confirmed cancer cases and 75 controls.

Main Results:

The urine-based assay demonstrated high overall sensitivity (97.8%) and moderate specificity (53.3%) across all Gleason grades (6-10).
For high-grade prostate cancers, the assay achieved 97.3% specificity.
For low-to-intermediate grade cancers, the assay showed 94.0% sensitivity.

Conclusions:

The developed urine-based liquid biopsy assay is a promising non-invasive tool for prostate cancer detection.
This accessible and culturally-appropriate platform has the potential to expand prostate cancer screening in diverse populations.
The assay could significantly reduce the number of unnecessary invasive prostate biopsies.