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Related Experiment Video

Updated: Feb 20, 2026

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

Development and Validation of an AI-Assisted Predictive Model Integrating R2* Mapping and Clinical Indicators for

Xin Li¹, Yonggui Shi¹, Jing Fang¹

¹Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Cancer Medicine

|February 18, 2026

Summary

This summary is machine-generated.

This study developed an AI nomogram using MRI data to accurately detect clinically significant prostate cancer (csPCa). The tool integrates AI-assisted S-PI-RADS v2.1 and R2* mapping for improved noninvasive prediction.

Keywords:

R2* mapping artificial intelligence biparameter nomogram prostate cancer prostate imaging reporting and data system

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Last Updated: Feb 20, 2026

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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

Area of Science:

Radiology
Oncology
Artificial Intelligence

Background:

Limited evidence on AI-assisted Simplified Prostate Imaging Reporting and Data System version 2.1 (S-PI-RADS v2.1) combined with quantitative MRI for clinically significant prostate cancer (csPCa) detection.
Need for improved diagnostic tools for csPCa.

Purpose of the Study:

To develop and validate a nomogram for csPCa prediction.
Incorporate AI-assisted S-PI-RADS v2.1 (biparametric MRI) and R2* mapping into the nomogram.

Main Methods:

Prospective study of 345 patients (115 csPCa, 230 non-csPCa).
Analysis of clinical (age, BMI, PSA) and imaging (PV, S-PI-RADS, R2*) parameters.
Logistic regression for predictor identification, nomogram development (R software, DynNom), and validation (1000 bootstrap iterations).

Main Results:

Independent csPCa predictors: BMI, PSA ≥ 10 ng/mL, PV, S-PI-RADS 4-5, and R2* (all p < 0.05).
Full model (BMI+PSA+PV+S-PI-RADS+R2*) showed superior discrimination (AUC=0.915) vs. baseline (AUC=0.891).
Achieved 85.2% sensitivity and 80.9% specificity; robust internal validation (C-index=0.884).

Conclusions:

AI-enhanced nomogram accurately predicts csPCa noninvasively using clinical and multiparametric MRI data.
R2* significantly improved prediction performance.
Tool facilitates personalized clinical decision-making for prostate cancer.