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Background: Multiparametric MRI (Mp-MRI) is a key tool to screen for Prostate Cancer (Pca) and Clinically Significant Prostate Cancer (CsPca). It primarily includes T2-Weighted imaging (T2w), diffusion-weighted imaging (DWI), and Dynamic Contrast-Enhanced imaging (DCE). Despite its improvements in CsPca screening, concerns about the cost-effectiveness of DCE persist due to its associated side effects, increased cost, longer acquisition time, and limitations in patients with poor kidney function. Recent studies have explored Biparametric MRI (Bp-MRI) as an alternative that excludes DCE.
Objectives: The main objective of this study is to compile and evaluate updated results of Bp-MRI as a diagnostic alternative to detect CsPca.
Methods: A systematic review was conducted using PubMed, Central Cochrane, and ClinicalTrialls.gov registry. Inclusion criteria was focused on observational and experimental studies that assessed a direct comparison of Bp-MRI and Mp-MRI for CsPca detection. The primary outcomes included were necessary to create a contingency 2×2 table and CsPca prevalence from each study. The secondary outcomes included were demographic data and imaging protocol features. The statistical analysis used a Bivariate Random-Effect model to estimate the pooled sensitivity, specificity, and area under the curve (AUC). An univariate random-effect model was conducted to estimate the positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies -2 tool.
Results: From 534 articles initially identified, 19 studies met the inclusion criteria with a total of 5075 patients. The pooled sensitivity estimated was 0.89, pooled specificity was 0.73, and AUC was 0.90; these results showed a slight increase compared to previous studies.
Conclusion: The results obtained showed that Bp-MRI is a feasible alternative to detect CsPca, which demonstrates high diagnostic accuracy and avoids the drawbacks associated with DCE.
Registry: This is a sub-analysis of the protocol registered at PROSPERO (CRD42024552125).
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http://dx.doi.org/10.1016/j.urolonc.2024.12.262 | DOI Listing |
JAMA
September 2025
Division of Surgery and Interventional Science, UCL, London, United Kingdom.
Importance: Multiparametric magnetic resonance imaging (MRI), with or without prostate biopsy, has become the standard of care for diagnosing clinically significant prostate cancer. Resource capacity limits widespread adoption. Biparametric MRI, which omits the gadolinium contrast sequence, is a shorter and cheaper alternative offering time-saving capacity gains for health systems globally.
View Article and Find Full Text PDFJAMA
September 2025
Section of Urologic Oncology, Department of Urology, Michigan Medicine, Ann Arbor.
World J Hepatol
August 2025
Department of Radiology, Third Affiliated Hospital of Soochow University: Changzhou First People's Hospital, Changzhou 213003, Jiangsu Province, China.
Background: Hepatocellular carcinoma (HCC) is a prevalent and life-threatening cancer with increasing incidence worldwide. High Ki-67 risk stratification is closely associated with higher recurrence rates and worse outcomes following curative therapies in patients with HCC. However, the performance of radiomic and deep transfer learning (DTL) models derived from biparametric magnetic resonance imaging (bpMRI) in predicting Ki-67 risk stratification and recurrence-free survival (RFS) in patients with HCC remains limited.
View Article and Find Full Text PDFJU Open Plus
July 2025
Indiana University, Indianapolis, Indiana.
Purpose: To quantify population-specific differences in prostate cancer (PCa) presentation between African American (AA) and White (W) men on MRI using radiomics.
Materials And Methods: We identified N = 149 men with PCa who underwent 3T MRI, a confirmatory biopsy and for whom self-reported race was available. Patient studies were partitioned into training (D) and hold-out test set (D).
J Imaging
July 2025
Medical Physics Laboratory, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece.
Prostate cancer (PCa) is the most common malignancy in men. Precise grading is crucial for the effective treatment approaches of PCa. Machine learning (ML) applied to biparametric Magnetic Resonance Imaging (bpMRI) radiomics holds promise for improving PCa diagnosis and prognosis.
View Article and Find Full Text PDF