Prostate cancer diagnosis using sensitive and sophisticated machine learning classifiers based on non-invasive urinary RNA biomarkers (PCASSO).

ABSTRACT: Prostate cancer (PCa) is the second most prevalent malignancy among men worldwide. However, current screening tools such as serum prostate-specific antigen (PSA) tests and digital rectal examination (DRE) are limited by low specificity and high false-positive rates, often leading to unnecessary biopsies and overtreatment. To address this clinical challenge, we developed a novel diagnostic framework termed PCASSO (Prostate CAncer diagnosis using Sensitive and Sophisticated ML classifiers based on nOn-invasive urinary RNA biomarkers), which integrates machine learning (ML) algorithms with non-invasive urinary RNA biomarker profiles obtained from DRE-free whole urine. A total of 163 urine samples (112 PCa, 51  benign prostatic hyperplasia [BPH]) were analyzed using quantitative PCR for 20 RNA

SUBMITTER: Goh H 

PROVIDER: S-EPMC12820356 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature