Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth.

Reliably predicting the future spread of brain tumors using imaging data and on a subject-specific basis requires quantifying uncertainties in data, biophysical models of tumor growth, and spatial heterogeneity of tumor and host tissue. This work introduces a Bayesian framework to calibrate the two-/three-dimensional spatial distribution of the parameters within a tumor growth model to quantitative magnetic resonance imaging (MRI) data and demonstrates its implementation in a pre-clinical model of glioma. The framework leverages an atlas-based brain segmentation of grey and white matter to establish subject-specific priors and tunable spatial dependencies of the model parameters in each region. Using this framework, the tumor-specific parameters are calibrated from quantitative MRI measurements early in the course of tumor development in four rats and used to predict the spatial development of the tumor at later times. The results suggest that the tumor model, calibrated by animal-specific imaging data at one time point, can accurately predict tumor shapes with a Dice coefficient 0.89. However, the reliability of the predicted volume and shape of tumors strongly relies on the number of earlier imaging time points used for calibrating the model. This study demonstrates, for the first time, the ability to determine the uncertainty in the inferred tissue heterogeneity and the model-predicted tumor shape.

Effect of pocket irrigation with antimicrobial on prevention of pacemaker pocket infection: a meta-analysis.

BACKGROUND: The presence of cardiac implantable electronic devices (CIEDs) pocket infection is difficult to treat, causing serious clinical outcomes, but little is known for prevention. Results from some studies suggested that pocket irrigation could reduce infection while others showed conflicting results. We pooled the effects of pocket irrigations on the prevention of pocket infection by meta-analysis methods. METHOD: Relevant studies published before June, 2017 were retrieved mainly by the computer-based search of PubMed, Cochrane, EMBASE, Web of Science, Chinese BioMedical, Global Health and BIOSIS Previews databases. Estimations of relative ratios (RRs) and 95% confidence intervals (95% CIs) were pooled. Subgroup analyses according to potential key factors affecting the effects were conducted, which was confirmed by meta-regression. Sensitivity analysis and test for publication bias were also performed. RESULTS: We identified 10 studies providing data of 5467 patients receiving CIEDs implantations. Pooled infection rates were 1.48 and 3.49% respectively for medication and saline irrigation groups. Meta-analysis showed that medication irrigation conferred protection to pocket infection (RR = 0.44, 95% CI: 0.31-0.63). Subgroup analysis showed that antibiotics, rather than non-antibiotics (antiseptics) exerting the protection. The first and second lines antibiotics against staphylococcus aureus, which is the main pathogen for pocket infection, were both effective (RR = 0.42, 95% CI: 0.24-0.75 and RR = 0.34, 95% CI: 0.20-0.58 respectively for first line and second line therapies). Meta-regression revealed that region and class of irrigation medication completely explained the variance among studies and implied that effects of region were masked by medication types. Sensitivity analysis did not showed any significant change of the result and publication bias were not statistical significance. CONCLUSION: Pocket irrigation with antibiotics were effective for reducing pocket infection and should be encouraged in CIEDs implantation.