Multiparametric Magnetic Resonance Imaging and clinical variables. Which is the best combination to predict reclassification in Active Surveillance patients

Abstract Introduction & objectives: We tested the role of multiparametric MRI (mpMRI) in disease reclassification, and whether the combination of mpMRI and clinico-pathological variables could represent the most accurate approach to predict the risk of reclassification during Active Surveillance (AS). Materials & methods Three-hundred eightynine patients (pts) underwent mpMRI and subsequent confirmatory or follow-up biopsy according to PRIAS protocol. Pts with negative (-) mpMRI underwent systematic random biopsy. Pts with positive (+) mpMRI (PI-RADS-V2 score≥3) underwent targeted + systematic random biopsies. Multivariate analyses was used to create three models predicting the probability of reclassification (ISUP≥GG2): a basic model including only clinical variables (age, PSAD and number of positive cores at baseline); an MRI model including only PI-RADS score; a full model including both the previous ones. The predictive accuracy (PA) of each model was quantified using the area under the curve. Results mpMRI (-) was recorded in 127 (32.6%) patients; mpMRI (+) was recorded in 262 patients: 72 (18.5%) had PIRADS 3, 150 (38.6%) PIRADS 4, and 40 (10.3%) PIRADS 5 lesions. At a median follow-up of 12 months, 125 patients (32%) were reclassified to GG2 PCa. The rate of reclassification to GG2 PCa was 17%, 35%, 38%, 52% for mpMRI (-), PI-RADS 3, 4, and 5, respectively (p Conclusions Disease reclassification increased according to PI-RADS score increase, at confirmatory or follow-up biopsy. However, a no-negligible rate of reclassification was found also in cases of mpMRI (-). The combination of mpMRI and clinico-pathological variables still represents the most accurate approach to patients on AS.