ArticleProstate international2020
Multiparametric magnetic resonance imaging and clinical variables: Which is the best combination to predict reclassification in active surveillance patients?
Article in Prostate international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association between baseline prostate MRI PI-RADS classification and risk of Gleason upgrade during active surveillance: a systematic review and meta-analysis.World journal of urology · 2026Pooled it
- Does protocol heterogeneity in active surveillance influence clinical outcomes? Insights from a multicenter prostate cancer cohort.Investigative and clinical urology · 2026Article
- Predictive models for biopsy upgrading in active surveillance for prostate cancer: a scoping review.World journal of urology · 2026Article
- Values of multiparametric and biparametric MRI in diagnosing clinically significant prostate cancer: a multivariate analysis.BMC urology · 2024Article
- Could 68Ga-PSMA PET/CT Evaluation Reduce the Number of Scheduled Prostate Biopsies in Men Enrolled in Active Surveillance Protocols?Journal of clinical medicine · 2022Article
- PI-RADS v2.1 Combined With Prostate-Specific Antigen Density for Detection of Prostate Cancer in Peripheral Zone.Frontiers in oncology · 2022Article
- Role of multi-parametric magnetic resonance imaging fusion biopsy in active surveillance of prostate cancer: a systematic review.Therapeutic advances in urologyReview
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION &
objectivesWe 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 &
methodsThree-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.
resultsmpMRI 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 (
conclusionsDisease 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.
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