Title Comparing biparametric to multiparametric magnetic resonance of prostate cancer diagnosis
Translation of Title Comparing Biparametric to Multiparametric Magnetic Resonance of Prostate Cancer Diagnosis.
Authors Janzer, Bruno Valentin
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Pages 52
Keywords [eng] prostate cancer, biparametric MRI, multiparametric MRI, PI-RADS, clinically significant prostate cancer, apparent diffusion coefficient, diffusion-weighted imaging, Gleason score
Abstract [eng] ABSTRACT (ENGLISH) Background: Multiparametric magnetic resonance imaging (mpMRI) has become a central component of the pre-biopsy diagnostic pathway in prostate cancer. Biparametric MRI (bpMRI), which omits the dynamic contrast-enhanced (DCE) sequence, has been proposed as a faster and less costly alternative. Aim: To review current evidence regarding biparametric and multiparametric MRI in prostate cancer diagnosis and to explore associations between MRI-derived clinical variables and pathological outcomes in a small retrospective cohort. Methods: A literature review was conducted focusing on prostate MRI, PI-RADS, csPCa, and the diagnostic role of dynamic contrast enhancement. In addition, retrospective analysis of 12 biopsy- proven prostate cancer cases was performed. Associations between PI-RADS score, Grade Group, ADC values, lesion size, and PSA density were analysed descriptively and exploratively. Results: PI-RADS 5 lesions were significantly larger than PI-RADS 4 lesions. A non-significant trend toward lower ADC values was observed in higher PI-RADS categories (p = 0.073). No statistically significant association was found between PI-RADS score and Grade Group or between PSA density and tumour grade. Conclusions: Current literature suggests comparable diagnostic performance of bpMRI and mpMRI for detection of csPCa in selected settings. In this cohort, MRI-derived variables showed limited but clinically plausible associations with pathological findings. Larger prospective studies are needed.
Dissertation Institution Vilniaus universitetas.
Type Master thesis
Language English
Publication date 2026