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Updated: Jan 25, 2026

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
Published on: April 9, 2019
Observed racial disparity in the negative predictive value of multi-parametric MRI for the diagnosis for prostate
Amr Mahran1,2, Kirtishri Mishra1,2, Laura Bukavina1,2
1Division of Urologic Oncology, Urology Institute, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Lakeside Building Suite 4954, Mailstop LKS 5046, Cleveland, OH, 44106, USA.
Objective:
To evaluate the trend that despite recent advances in the screening, diagnosis, and management of prostate cancer (PCa), African-Americans (AAs) continue to have poorer outcomes compared to their Caucasian (CAU) counterparts. The reason for this may be rooted in biological differences in the cancer between the two groups; however, there may be some inherent disparities within the efficacy of the screening modalities. In this study, we aim to evaluate the negative predictive value (NPV) of multi-parametric MRI (mpMRI) between AA compared to CAUs.
Methods:
All mpMRI between January 2014 and June 2017 were evaluated. The MRIs were read by dedicated genitourinary radiologists. Subsequently, the readings were correlated to final pathology after the patients underwent radical prostatectomy. The NPV and negative likelihood ratios (-LR) of mpMRI were evaluated in AAs versus CAUs based on four cutoffs (≥ Grade I, ≥ Grade II, ≥ Grade III and ≥ Grade IV).
Results:
The mpMRI was almost equally as effective between AAs and CAUs in excluding Grade III (NPV = 89 and 94, respectively), and Grade IV or above (NPV = 96 and 98, respectively) PCa; however, the NPV of mpMRI was significantly lower for Grade I (NPV = 32 and 52, respectively) and Grade II (NPV = 50 and 79, respectively) PCa.
Conclusion:
Despite advances in the screening for PCa, there are disparities noted in the efficacy of screening tools between AAs and CAUs. For this reason, patients should be risk stratified and their screening results should be evaluated with consideration given to their baseline risk.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

