Non-Invasive Detection of Prostate Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI
Key Points
To assess the diagnostic accuracy of a new AI-based MRI technique for detecting clinically significant prostate cancer.
Prospective observational study design
Development of an AI workflow incorporating time-dependent diffusion metrics
Comparison with PI-RADS v2.1 scoring system
Use of MRI-targeted prostate biopsy as reference standard
Enhanced characterization of clinically significant prostate cancer over standard mpMRI
Reduced false-positive rates compared to traditional methods
Improved inter-observer agreement in csPCa detection
Abstract
Prostate cancer (PCa) is the most common malignancy in men worldwide. Multiparametric MRI (mpMRI) improves the detection of clinically significant PCa (csPCa); however, it remains limited by false-positive findings and inter-observer variability. Time-dependent diffusion (TDD) MRI provides microstructural information that may enhance csPCa characterization beyond standard mpMRI. This prospective observational diagnostic accuracy study protocol describes the evaluation of PROS-TD-AI, an in-house developed AI workflow integrating TDD-derived metrics for zone-aware csPCa risk prediction. PROS-TD-AI will be compared with PI-RADS v2.1 in routine clinical imaging using MRI-targeted prostate biopsy as the reference standard.
Non-Invasive Detection of Prostate Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI | Synapse
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