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In the present study, methods aiming at supporting the personalization of an Acute Lymphoblastic Leukemia (ALL) Model (ALL Oncosimulator), already in development by the In Silico Oncology Group, National Technical University of Athens, are provided. Specifically, a population pharmacokinetic model for orally administered prednisone in children with ALL is developed, and the ability of classification algorithms to predict the prednisone response using gene expression data is studied. [ABSTRACT FROM PUBLISHER]

Nephroblastoma is the most common malignant renal tumor in children. Today about 90 % of the patients can be cured by chemotherapy and surgery. Most of the patients are enrolled in prospective clinical trials. In the SIOP (International Society of Pediatric Oncology) approach all children do receive preoperative chemotherapy to shrink the tumor before surgery. Patients with an excellent response to preoperative chemotherapy have a better outcome than those with a poor response. Response is not only defined by tumor volume shrinkage but also by the vanishing of blastemal tumor cells. Imaging studies are used to calculate tumor volume and to define vital, necrotic and cystic areas within a tumor. In case of nephroblastoma it would be of most importance to further separate the vital tumor in blastemal and non-blastemal components. This would allow changing treatment at an early phase in case of remaining blastema. Using the DoctorEye software, tumors can be easily rendered and histograms of the signal intensities within a tumor are possible to calculate. Our preliminary results show that these histograms give further insight in the tumors in single patients by correlating them with the histological findings. [ABSTRACT FROM PUBLISHER]

Two blastemal nephroblastoma cases have been successfully clinically adapted under two simulation scenarios of a clinically-oriented multiscale computational model, providing insight into the tumor characteristics. [ABSTRACT FROM PUBLISHER]

The Continuous Mathematics Based Glioblastoma Oncosimulator is a platform for simulating, investigating, better understanding, and exploring the natural phenomenon of glioma tumor growth. Modelling of the diffusive-invasive behaviour of glioma tumour growth may have considerable therapeutic implications. A crucial component of the corresponding computational problem is the numerical treatment of the adiabatic Neumann boundary conditions imposed by the skull on the diffusive growth of gliomas and in particular glioblastoma multiforme (GBM). In order to become clinically acceptable such a numerical handling should ensure that no potentially life-threatening glioma cells disappear artificially due to oversimplifying assumptions applied to the simulated region boundaries. However, no explicit numerical treatment of the 3D boundary conditions under consideration has appeared in the literature to the best of the authors' knowledge. Therefore, this paper aims at providing an outline of a novel, explicit and thorough numerical solution to this problem. Additionally, a brief exposition of the numerical solution process for a homogeneous approximation of glioma diffusion-invasion using the Crank - Nicolson technique in conjunction with the Conjugate Gradient system solver is outlined. The entire mathematical and numerical treatment is also in principle applicable to mathematically similar physical, chemical and biological diffusion based spatiotemporal phenomena which take place in other domains for example embryonic growth and general tissue growth and tissue differentiation. A comparison of the numerical solution for the special case of pure diffusion in the absence of boundary conditions with its analytical counterpart has been made. In silico experimentation with various adiabatic boundary geometries and non zero net tumour growth rate support the validity of the corresponding mathematical treatment. Through numerical experimentation on a set of real brain imaging data, a simulated tumour has shown to satisfy the expected macroscopic behaviour of glioblastoma multiforme, on concrete published clinical imaging data, including the adiabatic behaviour of the skull. The paper concludes with a number of remarks pertaining to the potential and the limitations of the diffusion-reaction approach to modelling multiscale malignant tumour dynamics. [ABSTRACT FROM PUBLISHER]