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Related Experiment Video

Updated: May 10, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

Risk Stratification of Suspected Clinically Localized Prostate Cancer Using Multi-Indicator Functional

Liuying Li¹, Yijing Xu¹, Mingjing Chen¹

¹Chongqing Key Laboratory of Molecular Oncology and Epigenetics, The First Affiliated Hospital of Chongqing Medical University, 400016 Chongqing, China.

Archivos Espanoles De Urologia

|May 9, 2026

Summary

A new nomogram accurately predicts significant prostate cancer in men with borderline PSA levels. This tool integrates biomarkers and MRI to improve risk stratification and guide treatment decisions.

Keywords:

PCA3 score nomograms prostate cancer prostate health index risk stratification

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Last Updated: May 10, 2026

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Published on: March 21, 2025

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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Area of Science:

Urology
Oncology
Medical Imaging

Background:

Accurate risk stratification is vital for localized prostate cancer, especially with total prostate-specific antigen (tPSA) in the 4-10 ng/mL grey zone.
Distinguishing clinically insignificant prostate cancer from significant disease is challenging.

Purpose of the Study:

Develop and validate a predictive model for prostate cancer risk stratification.
Integrate multi-dimensional indicators for improved diagnostic accuracy.

Main Methods:

Retrospective cohort study of 242 patients with suspected localized prostate cancer.
Analysis of biomarkers: free prostate-specific antigen/tPSA ratio (%fPSA), Prostate Health Index (PHI), Prostate Cancer Antigen 3 (PCA3) score.
Inclusion of multiparametric magnetic resonance imaging (mp-MRI) parameters, including PI-RADS scores.

Main Results:

High-risk group showed lower %fPSA and prostate volume, higher PHI, PCA3, and PI-RADS findings (p < 0.05).
Multivariate analysis identified %fPSA, PHI, PCA3, prostate volume, PI-RADS score ≥ 4, and index lesion diameter as independent predictors.
Developed nomogram achieved high discrimination (AUC = 0.885 development, 0.863 validation).

Conclusions:

A novel nomogram effectively integrates biomarkers and MRI for personalized prostate cancer risk stratification.
This multi-modal approach offers superior accuracy compared to single-modality methods.
The tool aids in distinguishing significant from insignificant prostate cancer, guiding clinical management.