Non-Invasive Detection of Prostate Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI.
Authors
Ramos B,Garrido C,Narváez P,Gelerstein Claro S,Li H,Salvador R,Vásquez-Venegas C,Gallegos I,Castañeda V,Acevedo C,Cárdenas G,Sotomayor CG
Affiliations (11)
Affiliations (11)
- Radiology Department, Clinical Hospital of the University of Chile, University of Chile, Independencia 8380453, Chile.
- Urology Department, Clínica Dávila, Santiago 8431657, Chile.
- School of Medicine, Faculty of Medicine, University of Chile, Santiago 8380453, Chile.
- Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, 38 Zheda Road, Hangzhou 310027, China.
- Radiology Department, Hospital Clinic, Universitat de Barcelona, 170, 08036 Barcelona, Spain.
- Laboratory for Scientific Image Analysis SCIAN-Lab, Integrative Biology Program, Institute of Biomedical Sciences ICBM, Faculty of Medicine, University of Chile, Santiago 8380453, Chile.
- Pathology Department, Clinical Hospital of the University of Chile, University of Chile, Independencia 8380453, Chile.
- Medical Technology Department, Faculty of Medicine, University of Chile, Santiago 8380453, Chile.
- Urology Department, Clinical Hospital of the University of Chile, University of Chile, Independencia 8380453, Chile.
- Anatomy and Developmental Biology Program, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile.
- Faculty of Medicine, San Sebastián University, Campus Los Leones, Santiago 7510157, Chile.
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.
Topics
Journal Article