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Updated: Apr 18, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Accuracy of 3 Tesla pelvic phased-array multiparametric MRI in diagnosing prostate cancer at repeat biopsy
Pietro Pepe1, Antonio Garufi, Giandomenico Priolo
1Urology Unit, Cannizzaro Hospital, Catania. piepepe@hotmail.com.
Introduction:
Multiparametric pelvic magnetic resonance imaging (mpMRI) accuracy in prostate cancer (PCa) diagnosis was evaluated.
Materials And Methods:
From June 2011 to December 2013, 168 patients (median 65 years) with negative digital rectal examination underwent repeat transperineal saturation biopsy (SPBx; median 28 cores) for persistently high or increasing PSA values, PSA >10 ng/ml or PSA values between 4.1-10 o r 2.6-4 ng/ml with free/total PSA < 25% and < 20%, respectively. All patients underwent mpMRI using a 3.0 Tesla scanner equipped with surface 16 channels phased-array coil and lesions suspicious for PCa were submitted to additional targeted biopsies.
Results:
A T1c PCa was found in 66 (39%) cases; SPBx and mpMRI-suspicious targeted biopsy diagnosed 60 (91%) and 52 (78.8%) cancers missing 6 (all of the anterior zone) and 14 cancers (12 and 2 of the lateral margins and anterior zone), respectively; in detail, mpMRI missed 12 (18.1%) PCa charaterized by microfocal (1 positive core with greatest percentage of cancer and Gleason score equal to 5% and 6, respectively) disease at risk for insignificant cancer. The diameter of the suspicious mpMRI lesion was directly correlated to the diagnosis of PCa with poor Gleason score (p < 0.05); detection rate of cancer for each suspicious mpMRI core was 35.3%. Diagnostic accuracy, sensitivity, specificity, positive and negative predictive value of mpMRI in diagnosing PCa was 75.7%, 82.5%, 71.8%, 78.9%, 87.9%, respectively.
Conclusion:
Multiparametric pMRI improved SPBx accuracy in diagnosing significant anterior PCa; the diameter of mpMRI suspicious lesion resulted significantly predictive of aggressive cancers.

