Diagnostic Accuracy of Pi-Rads V2.1 in Detecting Clinically Significant Prostate Cancer on Multiparametric MRI

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Authors: Md Sohel Sarower, Md. Asif Chowdhury Nirzor, Md Mahbub Hossain, Sanwoar Rahman, Rubina Khandker, Most. Rowshan Ara Khandaker, Farzana Khanam
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Abstract

Background: Prostate cancer is a leading cause of cancer morbidity and mortality among men worldwide. Accurate detection of clinically significant prostate cancer is essential to reduce overtreatment and improve outcomes. Multiparametric MRI and the Prostate Imaging Reporting and Data System version 2.1 have standardized prostate imaging interpretation. However, regional validation data remain limited. This study aimed to evaluate the diagnostic accuracy of PI-RADS v2.1 in detecting clinically significant prostate cancer in a tertiary-care Bangladeshi population. Methods: This cross-sectional observational study was conducted in the Department of Radiology and Imaging, East West Medical College & Hospital and IBN Sina Diagnostic Center, Uttara and Dhanmondi, Dhaka, Bangladesh, from January to December 2025. The study population consisted of 200 consecutive male patients who underwent multiparametric MRI (mpMRI) of the prostate followed by histopathological confirmation. Lesions were categorized using PI-RADS v2.1. Clinically significant prostate cancer was defined as ISUP Grade Group ≥2. Diagnostic indices were calculated using a cutoff of PI-RADS ≥4. Results: Clinically significant prostate cancer was identified in 50.0% of patients. Detection rates increased progressively from 11.1% in PI-RADS 1–2 to 90.9% in PI-RADS 5. Using a cutoff of ≥4, sensitivity was 80.0%, specificity 78.0%, positive predictive value 78.4% and negative predictive value 79.6%. Overall diagnostic accuracy was 79.0%. Conclusion: PI-RADS v2.1 demonstrates balanced and clinically meaningful diagnostic performance for detecting clinically significant prostate cancer. A threshold of PI-RADS ≥4 is appropriate for guiding biopsy decisions in tertiary-care practice.

Fields of Study & Subjects

Prostate Cancer Diagnosis and Treatment MRI in cancer diagnosis Radiomics and Machine Learning in Medical Imaging

Publication details

Journal / source
Scholars Journal of Applied Medical Sciences
Volume
14
Issue
02
Pages
309–314
Publication date
28 February 2026
Language
EN