The degree of association of the various factors with the survival outcome was calculated by using Kaplan-Meier estimator and Cox proportional hazard model techniques.

Results. A total of 95 patients were included in the analysis. The overall rate of recurrence (RR) was 34.7% (n = 33), with a median time to recurrence of 36 months (range 2-100 months). Progression-free survival (PFS) and overall survival rates at 5 years were 73.7% and 98.9%, respectively. Addition

of radiotherapy (RT) following resection significantly improved PFS (log-rank test, p = 0.008). In patients who underwent subtotal resection (STR), administering RT (STR + RT) improved outcome with the lowest failure rates (10.3%), superior to patients who underwent gross-total IWR-1-endo ic95 resection (GTR) alone (RR 43.1%; log-rank test, p < 0.001). Addition of RT to patients who underwent GTR was not beneficial (log-rank test, p = 0.628). In patients who had disseminated tumor selleck chemicals llc at presentation, adjuvant RT controlled the disease effectively. High-dose RT (>= 50 Gy) did not change PFS (log-rank test, p = 0.710).

Conclusions. Routine inclusion of RT in the treatment protocol for spinal MPEs in young patients should be considered. Complete

resection is always the goal of tumor resection. However, when complete resection does not seem to be possible in complex lesions, RT should be used as an adjunct to avoid aggressive resection and to minimize inadvertent injury to the surrounding neural tissues. High-dose RT (>= 50 Gy) did not provide additional survival benefits, although this association needs to be evaluated by prospective studies.”
“The purpose of neuroimaging meta-analysis is to localize the brain regions that are activated consistently in response to a certain intervention. As a commonly used technique, current coordinate-based meta-analyses (CBMA) of neuroimaging studies utilize relatively sparse information from published CDK phosphorylation studies, typically only using (x,y,z) coordinates of the activation peaks. Such CBMA methods have several limitations. First,

there is no way to jointly incorporate deactivation information when available, which has been shown to result in an inaccurate statistic image when assessing a difference contrast. Second, the scale of a kernel reflecting spatial uncertainty must be set without taking the effect size (e.g., Z-stat) into account. To address these problems, we employ Gaussian-process regression (GPR), explicitly estimating the unobserved statistic image given the sparse peak activation “”coordinate”" and “”standardized effect-size estimate”" data. In particular, our model allows estimation of effect size at each voxel, something existing CBMA methods cannot produce. Our results show that GPR outperforms existing CBMA techniques and is capable of more accurately reproducing the (usually unavailable) full-image analysis results.

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