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Impact of MRI and Targeted Biopsies on Eligibility and Disease Reclassification in MRI-positive Candidates for Active Surveillance on Systematic Biopsies

Published:December 30, 2019DOI:https://doi.org/10.1016/j.urology.2019.10.039

      Abstract

      OBJECTIVE

      To assess the impact of concomitant targeted biopsies (TB) for predicting final disease reclassification in MRI-positive low-risk prostate cancer patients eligible for active surveillance (AS) on systematic biopsies (SB).

      MATERIALS AND METHODS

      From a prospective database, we included all prebiopsy MRI-positive men fulfilling AS criteria at diagnosis (Toronto [n = 114], UCSF [n = 82], or PRIAS [n = 60] criteria) on SB. All patients underwent a combination of SB and software-based fusion TB, and an immediate radical prostatectomy. The primary endpoints were the pathologic upgrading and upstaging rates.

      RESULTS

      Biopsy grade group was upgraded to grade group (GG) 2 and to GG≥3 on TB in 65.9%-76.7% and in 12.2-16.7%, respectively. The rate of GG ≥3 in radical prostatectomy specimens varied from 31.6% to 43.3% with no relation between strictest criteria and lower upgrading rates. The proportion of not organ-confined disease (35%-39%) was comparable among the AS cohorts. Negative TB was strongly associated with the absence of final GG ≥3. Tumor grade on TB was significantly correlated with the risk of final GG ≥3 in both Toronto and UCSF cohorts, not in the PRIAS cohort. In the PRIAS cohort, the only independent predictive factor for GG ≥3 disease was the maximal tumor length in any core (P = .034).

      CONCLUSION

      In MRI-positive patients, the risk of disease reclassification was comparable whatever the SB-based AS criteria used. TB were predictive of final upgrading, with a varied impact according to the AS criteria. SB features remained relevant for reclassification prediction even in case of positive TB. The risk of upstaged disease remains important, approximately one third, and neither TB/SB parameters nor MRI findings could accurately predict it.
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