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Background and Objective: The O 6 -methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. However, determination of the MGMT promoter methylation status requires tissue obtained via surgical resection or biopsy. The aim of this study was to assess the ability of quantitative and qualitative imaging variables in predicting MGMT methylation status noninvasively., Methods: A retrospective analysis of MR images from GBM patients was conducted. Multivariate prediction models were obtained by machine-learning methods and tested on data from The Cancer Genome Atlas (TCGA) database., Results: The status of MGMT promoter methylation was predicted with an accuracy of up to 73.6%. Experimental analysis showed that the edema/necrosis volume ratio, tumor/necrosis volume ratio, edema volume, and tumor location and enhancement characteristics were the most significant variables in respect to the status of MGMT promoter methylation in GBM., Conclusions: The obtained results provide further evidence of an association between standard preoperative MRI variables and MGMT methylation status in GBM., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

The goal of this paper is to provide both the basic scientist and the clinician with an advanced computational tool for performing in silico experiments aiming at supporting the process of biological optimisation of radiation therapy. Improved understanding and description of malignant tumour dynamics is an additional intermediate objective. To this end an advanced three-dimensional (3D) Monte-Carlo simulation model of both the avascular development of multicellular tumour spheroids and their response to radiation therapy is presented. The model is based upon a number of fundamental biological principles such as the transition between the cell cycle phases, the diffusion of oxygen and nutrients and the cell survival probabilities following irradiation. Efficient algorithms describing tumour expansion and shrinkage are proposed and applied. The output of the biosimulation model is introduced into the (3D) visualisation package AVS-Express, which performs the visualisation of both the external surface and the internal structure of the dynamically evolving tumour based on volume or surface rendering techniques. Both the numerical stability and the statistical behaviour of the simulation model have been studied and evaluated for the case of EMT6/Ro spheroids. Predicted histological structure and tumour growth rates have been shown to be in agreement with published experimental data. Furthermore, the underlying structure of the tumour spheroid as well as its response to irradiation satisfactorily agrees with laboratory experience.