Main Speakers
The scope of this summer school is to provide basic training on the new research dimension of computational oncology based on the experience of four European collaborative projects in this direction (ACGT, ContraCancrum, TUMOR and pmedicine).
The summer school will cover a wide range of multidisciplinary aspects of computational oncology including both clinical and engineering/basic science viewpoints. The aim is to provide students and young researchers from various backgrounds the opportunity to gain the necessary knowledge and skills required to understand the basics of both the multiscale cancer modeling and the biomedical informatics framework that are needed in order to bring this new technology closer to the clinical setting and decision making process.
The lectures will cover the following related themes:
- Clinical perspective of in silico oncology: In this theme, experienced clinicians will provide their own insight on developing clinically-driven, predictive oncology technologies aiming to optimize therapy delivery for the individual patient by selecting the best schema aided from computer simulations.
- Multiscale mathematical modeling of cancer and in silico oncology: This thematic area will cover most of the approaches for modeling the natural phenomenon of cancer including numerical simulations of cancer tissue evolution, molecular details of drug-receptor interactions based on patient genetic profiling, and predictions of response to therapy.
- Cancer image analysis and tissue biomechanics: To model cancer it is crucial to extract pathophysiological parameters from multiscale patient information ranging from medical images to cDNA array data, to compute the tissue biomechanical behavior that can affect tumor growth and to provide appropriate visualization tools and User Interfaces. This theme will provide an insight to all the image analysis and biomechanics tools that can be used in advantage for multiscale cancer growth or therapy response simulations.
- Cancer Biomedical Informatics: In order to translate predictive oncology technologies to the clinical setting, it is essential to understand the multiscale environment and the complexity of the heterogeneous data requirements. Specialized tools are needed to achieve seamless data integration, ensure interoperability of multi-scale modeling tools and the integration overall predictive workflow in clinically friendly front-end environments. This thematic area will provide a number of lectures based on successful European projects that have developed such integrative cancer research environments.
The number of participants is restricted. Candidate students\young researchers are encouraged to apply through the website
