Published in

MDPI, Journal of Clinical Medicine, 12(9), p. 3841, 2020

DOI: 10.3390/jcm9123841

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MRI Characteristics Accurately Predict Biochemical Recurrence after Radical Prostatectomy

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

Background: After radical prostatectomy (RP), biochemical recurrence (BCR) is associated with an increased risk of developing distant metastasis and prostate cancer specific and overall mortality. Methods: The two-centre study included 521 consecutive patients undergoing RP for positive pre-biopsy magnetic resonance imaging (MRI) and pathologically proven prostate cancer (PCa), after which a combination scheme of fusion-targeted biopsy (TB) and systematic biopsy was performed. We assessed correlations between MRI characteristics, International Society of Urological Pathology (ISUP) grade group in TB, and outcomes after RP. We developed an imaging-based risk classification for improving BCR prediction. Results: Higher Prostate Imaging and Reporting and Data System (PI-RADS) score (p = 0.013), higher ISUP grade group in TB, and extracapsular extension (ECE) on the MRI were significantly associated with more advanced disease (pTstage), higher ISUP grade group (p = 0.001), regional lymph nodes metastasis in RP specimens (p < 0.001), and an increased risk of recurrence after surgery. A positive margin status was significantly associated with ECE-MRI (p < 0.001). Our imaging-based classification included ECE on MRI, ISUP grade group on TB, and PI-RADS accurately predicted BCR (AUC = 0.714, p < 0.001). This classification had more improved area under the curve (AUC) than the standard d’Amico classification in our population. Validation was performed in a two-centre cohort. Conclusions: In this cohort, PI-RADS score, MRI stage, and ISUP grade group in MRI-TB were significantly predictive for disease features and recurrence after RP. Imaging-based risk classification integrating these three factors competed with d’Amico classification for predicting BCR.