Scholarlink

<p>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 &#xa0;benign prostatic hyperplasia&#xa0;[BPH]) were analyzed using quantitative PCR for 20 RNA biomarkers, including 2 long noncoding RNAs, 1 fusion gene, and 17 miRNAs. Among six ML classifiers evaluated, a Gradient Boosting model using an optimized 9-biomarker panel achieved the highest diagnostic performance (AUC: 0.99), with robust cross-validation results (Stratified-K-Fold: 0.912; LOOCV: 0.890). Notably, this classifier retained high accuracy in patients within the PSA gray zone (3–10&#xa0;ng/mL), where clinical decision-making is often ambiguous. Our results demonstrate that ML-based classifiers using DRE-free urinary RNA biomarkers showed improved performance through robust internal validation, providing a basis for future validation studies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月25日

查看全文
  • Hyunseop Goh,
  • Taeyang Heo,
  • Jeongwon Kim,
  • Yunlim Kim,
  • Bumjin Lim,
  • Myung Kyun Woo,
  • Kwan Yong Choi,
  • Choung-Soo Kim,
  • Jongmin Kim

摘要

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 biomarkers, including 2 long noncoding RNAs, 1 fusion gene, and 17 miRNAs. Among six ML classifiers evaluated, a Gradient Boosting model using an optimized 9-biomarker panel achieved the highest diagnostic performance (AUC: 0.99), with robust cross-validation results (Stratified-K-Fold: 0.912; LOOCV: 0.890). Notably, this classifier retained high accuracy in patients within the PSA gray zone (3–10 ng/mL), where clinical decision-making is often ambiguous. Our results demonstrate that ML-based classifiers using DRE-free urinary RNA biomarkers showed improved performance through robust internal validation, providing a basis for future validation studies.