Downgrading of Grade Group After Radical Prostatectomy: Comparison of Multiparametric Magnetic Resonance Imaging Guided Fusion Biopsy and Standard 12-Core Biopsy.

OBJECTIVE: To analyze the factors associated with Grade group (GG) downgrading post-radical prostatectomy. PATIENTS AND METHODS: We performed a retrospective analysis of 536 patients who underwent robot-assisted laparoscopic radical prostatectomy from February 2014 to October 2015. We have analyzed the clinical, radiological, and pathologic factors associated with GG downgrading in final pathology. Downgrading was defined as those patients who downgraded from GG 3, 4, or 5 on biopsy to GG 1 or 2 on final pathology as well as patients who downgraded from GG 2 on biopsy to GG 1 on final pathology. Categorical values were compared with chi-square and Fischer's exact tests. Mann-Whitney U and Kruskal-Wallis were used for analysis of independent variables associated with GG downgrading. RESULTS: Ninety-three patients underwent fusion biopsy (FB) and 443 underwent the standard 12 core biopsy. Baseline clinical characteristics were similar between the 2 groups except for race (P = .009). Downgrading was observed in 76 patients (14.1%). Rate of downgrading was higher in the FB group (n = 22, 23.7% vs n = 54, 12.2%, P = .008). In multivariable logistic regression analysis, FB (OR:2.39, P = .004) and maximum percentage of core involvement (OR:1.01, P = .013) were associated with downgrading after robot-assisted laparoscopic radical prostatectomy. After 1:2 propensity score matching, FB was still associated with an increased rate of downgrading (P = .034). Downgrading had no significant effect on pathologic outcome. CONCLUSION: FB and maximum percentage of core involvement are the only factors associated with GG downgrading in final pathology. However, downgrading did not influence surgical outcome.

Development of a geospatial approach for the quantitative analysis of trauma center access.

INTRODUCTION: Decisions around trauma center (TC) designation have become contentious in many areas. There is no consensus regarding the ideal number and location of TC and no accepted metrics to assess the effect of changes in system structure. We aimed to develop metrics of TC access, using publicly available data and analytic tools. We hypothesize that geospatial analysis can provide a reproducible approach to quantitatively asses potential changes in trauma system structure. METHODS: A region in New York State was chosen for evaluation. Geospatial data and analytic tools in ArcGIS Online were used. Transport time polygons were created around TC, and the population covered was estimated by summing the census tracts within these polygons. Transport time from each census tract to the nearest TC was calculated. The baseline model includes the single designated TC. Model 1 includes one additional TC, and Model 2 includes two additional TC, chosen to maximize coverage. The population covered, population-weighted distribution of transport times, and population covered by a specific TC were calculated for each model. RESULTS: The baseline model covered 1.12 × 10 people. The median transport time was 19.2 minutes. In Model 1, the population covered increased by 14.4%, while the population catchment, and thus the estimated trauma volume, of the existing TC decreased by 12%. Median transport time to the nearest TC increased to 20.4 minutes. Model 2 increased coverage by 18% above baseline, while the catchment, and thus the estimated trauma volume, of the existing TC decreased by 22%. Median transport time to the nearest TC decreased to 19.6 minutes. CONCLUSIONS: Geospatial analysis can provide objective measures of population access to trauma care. The analysis can be performed using different numbers and locations of TC, allowing direct comparison of changes in coverage and impact on existing centers. This type of data is essential for guiding difficult decisions regarding trauma system design. LEVEL OF EVIDENCE: Care management, level IV.

Multiparametric Magnetic Resonance Imaging Features Identify Aggressive Prostate Cancer at the Phenotypic and Transcriptomic Level.

PURPOSE: Multiparametric magnetic resonance imaging is a diagnostic tool for prostate cancer with limited data on prognostic use. We sought to determine whether multiparametric magnetic resonance could predict aggressive prostate cancer features. MATERIALS AND METHODS: We retrospectively analyzed the records of 206 patients who underwent radical prostatectomy between 2013 and 2017. All patients had available RNA expression data on the final pathology specimen obtained from a location corresponding to a lesion location on multiparametric magnetic resonance imaging. The association between the PIRADS™ (Prostate Imaging Reporting and Data System) score and adverse pathology features were analyzed. We also performed differential transcriptomic analysis between the PIRADS groups. Factors associated with adverse pathology were analyzed using a multivariable logistic regression model. RESULTS: Lesion size (p = 0.03), PIRADS score (p = 0.02) and extraprostatic extension (p = 0.01) associated significantly with the Decipher® score. Multivariable analysis showed that the PIRADS score (referent PIRADS 3, OR 8.1, 95% CI 1.2-57.5, p = 0.04), the Gleason Grade Group (referent 3, OR 5.6, 95% CI 1.5-21.1, p = 0.01) and prostate specific antigen (OR 1.103, 95% CI 1.011-1.203) were risk factors for adverse pathology findings. The difference between PIRADS 4 and 5 did not reach significance (OR 1.9, 95% CI 0.8-4.5, p = 0.12). However, the PI3K-AKT-mTOR, WNT-ß and E2F signaling pathways were more active in PIRADS 5 than in PIRADS 4 cases. CONCLUSIONS: The PIRADS score is associated with adverse pathology results, increased metastatic risk and differential genomic pathway activation.