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Related Experiment Video

Updated: Jan 28, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Non-Invasive Detection of Prostate Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative

Baltasar Ramos1, Cristian Garrido1, Paulette Narváez2

  • 1Radiology Department, Clinical Hospital of the University of Chile, University of Chile, Independencia 8380453, Chile.

Journal of Imaging
|January 27, 2026
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Summary

This study introduces PROS-TD-AI, an artificial intelligence tool using time-dependent diffusion MRI to improve prostate cancer (PCa) detection. It aims to enhance risk prediction for clinically significant PCa (csPCa) beyond current MRI standards.

Keywords:
Gleason scorePI-RADS v2.1artificial intelligenceclinically significant prostate cancerdeep learningmagnetic resonance imagingprostate biopsy

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Prostate cancer (PCa) is a leading malignancy in men globally.
  • Multiparametric MRI (mpMRI) aids in detecting clinically significant PCa (csPCa), but faces limitations like false positives and variability.
  • Time-dependent diffusion (TDD) MRI offers enhanced microstructural insights for csPCa characterization.

Purpose of the Study:

  • To evaluate PROS-TD-AI, an AI workflow integrating TDD-MRI metrics for zone-aware csPCa risk prediction.
  • To compare the diagnostic accuracy of PROS-TD-AI against the PI-RADS v2.1 scoring system.
  • To assess the utility of TDD-derived metrics in improving csPCa detection in routine clinical settings.

Main Methods:

  • Prospective observational diagnostic accuracy study protocol.
  • Development and implementation of an in-house AI workflow (PROS-TD-AI) incorporating TDD-MRI.
  • Comparison of PROS-TD-AI performance with PI-RADS v2.1 using MRI-targeted prostate biopsy as the reference standard.

Main Results:

  • The study protocol is designed to assess the diagnostic performance of PROS-TD-AI.
  • It will quantify the improvement in csPCa risk prediction offered by TDD-derived metrics.
  • The AI workflow's accuracy will be benchmarked against the established PI-RADS v2.1 system.

Conclusions:

  • PROS-TD-AI has the potential to enhance the accuracy of csPCa detection using TDD-MRI.
  • This AI approach may overcome limitations of standard mpMRI, reducing false positives and inter-observer variability.
  • The findings could lead to more precise risk stratification and improved patient management for prostate cancer.