TY - JOUR
T1 - Multiparametric magnetic resonance imaging and clinical variables
T2 - Which is the best combination to predict reclassification in active surveillance patients?
AU - Roscigno, Marco
AU - Stabile, Armando
AU - Lughezzani, Giovanni
AU - Pepe, Pietro
AU - Dell'Atti, Lucio
AU - Naselli, Angelo
AU - Naspro, Richard
AU - Nicolai, Maria
AU - La Croce, Giovanni
AU - Muhannad, Aljoulani
AU - Perugini, Giovanna
AU - Guazzoni, Giorgio
AU - Montorsi, Francesco
AU - Balzarini, Luca
AU - Sironi, Sandro
AU - Da Pozzo, Luigi F.
N1 - Funding Information:
The authors acknowledgeDr. Manuela Scarcello for her help in data collection and managing.
Publisher Copyright:
© 2020
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - Introduction & objectives: We tested the role of multiparametric magnetic resonance imaging (mpMRI) in disease reclassification and whether the combination of mpMRI and clinicopathological variables could represent the most accurate approach to predict the risk of reclassification during active surveillance. Materials & methods: Three-hundred eighty-nine patients (pts) underwent mpMRI and subsequent confirmatory or follow-up biopsy according to the Prostate Cancer Research International Active Surveillance (PRIAS) protocol. Pts with negative (−) mpMRI underwent systematic random biopsy. Pts with positive (+) mpMRI [Prostate Imaging Reporting and Data System, version 2 (PI-RADS-V2) score ≥3] underwent targeted + systematic random biopsies. Multivariate analyses were used to create three models predicting the probability of reclassification [International Society of Urological Pathology ≥ Grade Group 2 (GG2)]: a basic model including only clinical variables (age, prostate-specific antigen density, and number of positive cores at baseline), an Magnetic resonance imaging (MRI) model including only the PI-RADS score, and 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 negative (−) was recorded in 127 (32.6%) pts; mpMRI positive (+) was recorded in 262 pts: 72 (18.5%) had PI-RADS 3, 150 (38.6%) PI-RADS 4, and 40 (10.3%) PI-RADS 5 lesions. At a median follow-up of 12 months, 125 pts (32%) were reclassified to GG2 prostate cancer. The rate of reclassification to GG2 prostate cancer was 17%, 35%, 38%, and 52% for mpMRI (−), PI-RADS 3, 4, and 5, respectively (P < 0.001). The PA was 69% and 64% in the basic and MRI models, respectively. The full model had the best PA of 74%: older age (P = 0.023; Odds ratio (OR) = 1.040), prostate-specific antigen density (P = 0.037; OR = 1.324), number of positive cores at baseline (P = 0.001; OR = 1.441), and PI-RADS 3, 4, and 5 (overall P = 0.001; OR = 2.458, 3.007, and 3.898, respectively) were independent predictors of reclassification. Conclusions: Disease reclassification increased according to the 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 clinicopathological variables still represents the most accurate approach to pts on active surveillance.
AB - Introduction & objectives: We tested the role of multiparametric magnetic resonance imaging (mpMRI) in disease reclassification and whether the combination of mpMRI and clinicopathological variables could represent the most accurate approach to predict the risk of reclassification during active surveillance. Materials & methods: Three-hundred eighty-nine patients (pts) underwent mpMRI and subsequent confirmatory or follow-up biopsy according to the Prostate Cancer Research International Active Surveillance (PRIAS) protocol. Pts with negative (−) mpMRI underwent systematic random biopsy. Pts with positive (+) mpMRI [Prostate Imaging Reporting and Data System, version 2 (PI-RADS-V2) score ≥3] underwent targeted + systematic random biopsies. Multivariate analyses were used to create three models predicting the probability of reclassification [International Society of Urological Pathology ≥ Grade Group 2 (GG2)]: a basic model including only clinical variables (age, prostate-specific antigen density, and number of positive cores at baseline), an Magnetic resonance imaging (MRI) model including only the PI-RADS score, and 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 negative (−) was recorded in 127 (32.6%) pts; mpMRI positive (+) was recorded in 262 pts: 72 (18.5%) had PI-RADS 3, 150 (38.6%) PI-RADS 4, and 40 (10.3%) PI-RADS 5 lesions. At a median follow-up of 12 months, 125 pts (32%) were reclassified to GG2 prostate cancer. The rate of reclassification to GG2 prostate cancer was 17%, 35%, 38%, and 52% for mpMRI (−), PI-RADS 3, 4, and 5, respectively (P < 0.001). The PA was 69% and 64% in the basic and MRI models, respectively. The full model had the best PA of 74%: older age (P = 0.023; Odds ratio (OR) = 1.040), prostate-specific antigen density (P = 0.037; OR = 1.324), number of positive cores at baseline (P = 0.001; OR = 1.441), and PI-RADS 3, 4, and 5 (overall P = 0.001; OR = 2.458, 3.007, and 3.898, respectively) were independent predictors of reclassification. Conclusions: Disease reclassification increased according to the 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 clinicopathological variables still represents the most accurate approach to pts on active surveillance.
KW - Active surveillance
KW - Magnetic resonance imaging
KW - MRI-TRUS fusion
KW - Prostate biopsy
KW - Prostate cancer
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U2 - 10.1016/j.prnil.2020.05.003
DO - 10.1016/j.prnil.2020.05.003
M3 - Article
AN - SCOPUS:85087210880
VL - 8
SP - 167
EP - 172
JO - Prostate International
JF - Prostate International
SN - 2287-8882
IS - 4
ER -