Non-Invasive Detection of Prostate Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI

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.

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Publication Details

Journal
Journal of Imaging
Published
2026-01-22
DOI
https://doi.org/10.3390/jimaging12010053
Primary Topic
Prostate Cancer Diagnosis and Treatment
Type
article
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article

Non-Invasive Detection of Prostate Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI

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Journal of Imaging
Prostate Cancer Diagnosis and Treatment
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Non-Invasive Detection of Prostate Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI

Gonzalo Cárdenas, Claudio Sotomayor, Baltasar Ramos, Constanza Vásquez-Venegas, Cristián Garrido, Haotian Li, Santiago Gelerstein Claro, Iván Gallegos, Víctor Castañeda, Cristian Acevedo, Paulette Narváez
article en

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.

Journal of ImagingVol. 12(1)
Universidad de Santiago de Chile (CL), San Sebastián University (CL), Colegio Médico de Chile (CL), Hospital Clínic de Barcelona (ES), Hospital Clínico de la Universidad de Chile (CL), Clínica Dávila (CL), Millennium Institute for Integrative Biology (CL), University of Chile (CL), Universitat de Barcelona (ES), Zhejiang University (CN)
Good health and well-being
Openalex Percentile: Top 79%
Prostate Cancer Diagnosis and Treatment
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