Search

Purpose: We determined whether endorectal multiparametric magnetic resonance imaging at 1.5 Tesla could predict tumor target volume in the perspective of focal therapy of prostate cancer. Materials and Methods: A total of 84 consecutive patients underwent multiparametric magnetic resonance imaging before radical prostatectomy. The volume of each suspicious area detected on magnetic resonance imaging and of all surgical histological foci was determined by planimetry. We first used each magnetic resonance imaging sequence (T2-weighted, diffusion weighted and dynamic contrast enhanced) and then the sequence showing the largest tumor area (multiparametric volume). Finally, the largest area of any sequence was used to calculate a target volume according to the volume of a cylinder. Agreement between magnetic resonance imaging and pathological findings was assessed by linear regression and residual analysis. Results: Histology revealed 99 significant tumors with a volume of greater than 0.2 cc and/or a Gleason score of greater than 6. Of the tumors 16 (16.2%) were undetected by multiparametric magnetic resonance imaging. Linear regression analysis showed that tumor volume estimated by T2-weighted or diffusion weighted imaging correlated significantly with pathological volume (r2 = 0.82 and 0.83, respectively). Residuals from diffusion weighted imaging volume estimations did not significantly differ from 0. Nevertheless, diffusion weighted imaging underestimated pathological volume in 43 of 87 cases (49%) by a mean of 0.56 cc (range 0.005 to 2.84). Multiparametric and target volumes significantly overestimated pathological volume by a mean of 16% and 44% with underestimation in 28 (32%) and 15 cases (17%), respectively. Volume underestimation was significantly higher for tumor foci less than 0.5 cc. The percent of Gleason grade 4 did not influence tumor volume estimation. Conclusions: Magnetic resonance imaging can detect most significant tumors. However, delineating a target volume may require further adjustment before planning magnetic resonance imaging targeted focal treatment. [ABSTRACT FROM AUTHOR]

The accurate simulation of meteorological conditions, especially within the planetary boundary layer (PBL), is of major importance for air quality modeling. In the present work, we have used the Weather Research and Forecast (WRF) model coupled with the chemistry transport model (CTM) CHIMERE to understand the impact of physics parameterizations on air quality simulation during a short-term pollution episode on the Paris region. A lower first model layer with a 4 m surface layer could better reproduce the transport and diffusion of pollutants in a real urban environment. Three canopy models could better reproduce a 2 m temperature (T2) in the daytime but present a positive bias from 1 to 5 °C during the nighttime; the multi-urban canopy scheme "building effect parameterization" (BEP) underestimates the 10 m windspeed (W10) around 1.2 m s−1 for the whole episode, indicating the city cluster plays an important role in the diffusion rate in urban areas. For the simulation of pollutant concentrations, large differences were found between three canopy schemes, but with an overall overestimation during the pollution episode, especially for NO2 simulation, the average mean biases of NO2 prediction during the pollution episode were 40.9, 62.2, and 29.7 µg m−3 for the Bulk, urban canopy model (UCM), and BEP schemes, respectively. Meanwhile, the vertical profile of the diffusion coefficients and pollutants indicated an important impact of the canopy model on the vertical diffusion. The PBL scheme sensitivity tests displayed an underestimation of the height of the PBL when compared with observations issued from the Lidar. The YonSei University scheme YSU and Boulac PBL schemes improved the PBL prediction compared with the Mellor–Yamada–Janjic (MYJ) scheme. All the sensitivity tests, except the Boulac–BEP, could not fairly reproduce the PBL height during the pollution episode. The Boulac–BEP scheme had significantly better performances than the other schemes for the simulation of both the PBL height and pollutants, especially for the NO2 and PM2.5 (particulate matter 2.5 micrometers or less in diameter) simulations. The mean bias of the NO2, PM2.5, and PM10 (particulate matter 10 micrometers or less in diameter) prediction were −5.1, 1.2, and −8.6 µg m−3, respectively, indicating that both the canopy schemes and PBL schemes have a critical effect on air quality prediction in the urban region. [ABSTRACT FROM AUTHOR]