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Updated: Nov 28, 2025

Use of Magnetic Resonance Imaging and Biopsy Data to Guide Sampling Procedures for Prostate Cancer Biobanking
Published on: October 10, 2019
Who Can Avoid Biopsy of Magnetic Resonance Imaging-Negative Lobes without Compromising Significant Cancer Detection
Yasukazu Nakanishi1, Masaya Ito1, Madoka Kataoka1
1Department of Urology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan.
Objectives:
To assess whether biopsy of multiparametric magnetic resonance imaging (MRI)-negative lobes can be avoided without compromising significant cancer (SC) detection among men with unilateral MRI-positive lobes.
Methods:
From April 2013 to April 2019, 322 men with elevated prostate-specific antigen (PSA <20 ng/mL) and unilateral MRI-positive lobes underwent targeted 4-core and systematic 14-core biopsy. MRI findings were prospectively collected and evaluated according to the Prostate Imaging-Reporting and Data System (PI-RADS) version 2, and scores ≥3 were considered positive. SC was defined as Gleason score ≥3 + 4 or maximal cancer length ≥5 mm. We developed predictive models of overall cancer and SC in MRI-negative lobes and evaluated the performance of these models.
Results:
Detection rates of overall cancer/SC were 69%/61% for the overall cohort, 58%/48% for MRI-positive lobes, and 36%/20% for MRI-negative lobes. Age ≥75 years, PSA density ≥0.3, and PI-RADS ≥4 were independently predictive of both overall cancer and SC in MRI-negative lobes; 1 point was assigned for each risk factor, and the predictive score was defined as the sum of points (0-3) for both overall cancer and SC. Areas under the curve of the model for overall cancer/SC were 0.67/0.71. In the decision curve analysis, the model was of value above the threshold probability of 13%/6% for detecting overall cancer/SC in MRI-negative lobes. Of 40 men with score 0, overall cancer/SC was detected in the MRI-negative lobe in 4 (10%)/1 (2.5%).
Conclusion:
Biopsies of MRI-negative lobes may be avoided without compromising SC detection using our predictive model.
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