A study presented at SERAM 2026 suggests that biparametric MRI (bpMRI) can detect clinically significant prostate cancer with diagnostic performance similar to conventional multiparametric MRI (mpMRI), while reducing examination time and costs.
Researchers led by Dr. María Elena Savino Ramos from Policlínica Metropolitana in Caracas, Venezuela, compared the two prostate MRI approaches in a retrospective cohort of patients who underwent MRI followed by targeted biopsy.
mpMRI is the standard imaging protocol for prostate cancer assessment and includes T2-weighted imaging, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. bpMRI omits DCE, which may simplify workflow and avoid gadolinium administration.
The investigators screened 1,017 patients evaluated for suspected prostate cancer between 2022 and 2025. The final analysis included 99 patients with 99 suspicious lesions who underwent cognitive targeted biopsy, with histopathology as the reference standard.
Multiparametric prostate MRI showing a PI-RADS 5 lesion in a patient with Gleason 3+4 prostate cancer. Contrast-enhanced imaging did not alter lesion assessment.Courtesy of SERAM 2026
Three radiologists first interpreted the full mpMRI examinations and then re-read the studies without DCE to simulate a bpMRI protocol. In this cohort, bpMRI achieved sensitivity of 88%, specificity of 80%, positive predictive value of 93%, and overall accuracy of 86%. mpMRI showed sensitivity of 92%, specificity of 76%, positive predictive value of 92%, and accuracy of 70%.
Lower cost and acquisition time
The authors noted that mpMRI was slightly more sensitive, but bpMRI was more specific and more accurate in their dataset. They also reported that DCE did not alter PI-RADS assessment in the illustrative cases shown, consistent with evidence that the contrast-enhanced sequence adds limited value in many routine cases.
Biparametric prostate MRI identified clinically significant Gleason 3+4 prostate cancer using T2-weighted imaging, diffusion-weighted imaging, and ADC mapping alone.Courtesy of SERAM 2026
Beyond diagnostic performance, bpMRI offered practical advantages. Eliminating DCE reduced acquisition time by about nine minutes and lowered costs by 30.56%.
The findings add to growing evidence that bpMRI may be sufficient for many patients undergoing prostate cancer evaluation, especially where scanner time and resources are limited.
The authors emphasized, however, that broader adoption should be supported by standardized protocols, quality assurance, and radiologist training. Larger prospective studies are still needed to confirm the results and define where bpMRI can safely replace mpMRI in clinical practice.

















![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)

