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Updated: Aug 21, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Diagnosis of clinically significant prostate cancer after negative multiparametric magnetic resonance imaging
Fabio Zattoni1, Alessandro Morlacco1, Matteo Soligo2
1Urology Clinic, Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy.
Introduction:
The diagnostic pathway after a negative magnetic resonance imaging (nMRI) exam is not clearly defined. The aim of the present study is to define the risk of prostate adenocarcinoma (PCa) at the prostate biopsy after a negative multiparametric magnetic resonance imaging (mpMRI) exam.
Material And Methods:
Patients with nMRI Prostate Imaging Reporting & Data System (PI-RADS) ≤2 and without a previous diagnosis of PCa were identified among all patients undergoing mpMRI in a single referral center between 01/2016-12/2019. Detailed data about prostate biopsy after nMRI were collected, including any PCa diagnosis and clinically significant PCa diagnosis. [Gleason score (GS) ≥7]. In addition to descriptive statistics, uni and multivariable logistic regression assessed the potential predictors of any PCa and clinically significant prostate cancer (csPCa) at the biopsy after a negative mpMRI.
Results:
Of 410 patients with nMRI, 73 underwent saturation biopsy. Only prostate-specific antigen (PSA) levels were significantly higher in patients undergoing biopsy (5.2 ng/ml vs 6.4, p <0.001), while Prostate Cancer Research Foundation (SWOP - Stichting Wetenschappelijk Onderzoek Prostaatkanker) risk score and other variables did not differ. A total of 22 biopsies (30.1%) were positive for PCa, GS 6 was diagnosed in 14 patients, GS 7 in 3, GS 8 in 1 and GS 9-10 in 4. csPCa was found in 8 (11%) patients. No significant predictors of any PCa or csPCa were identified at multivariate regression analysis.
Conclusions:
Despite the good negative predictive value of mpMRI in the diagnosis of prostate cancer, 11% of the patients had csPCa. Specific predictive models addressing this setting would be useful.
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