Model-based control of cancer diseases

OData support
Supervisor:
Dr. Harmati István
Department of Control Engineering and Information Technology

Cancer is a broad group of various diseases, all involving unregulated cell growth. In several cases, after the diagnosis is set up, the cancer leads to death in a short time. The used treatment is based on the tumor’s properties (like type, size and location) and the status of the patient. The most commonly used therapies are surgical procedure, chemotherapy and radiation therapy. A new and unorthodox way of treatment is the antiangiogenic therapy which is a more specific, targeted treatment; one of the Targeted Molecular Therapies (TMT). Antiangiogenic drugs prevent tumors from forming new blood vessels. In order to develop the therapies more effective one must analyze the process of the growth of the tumor, and locate points of effective intervention. It is a new approach in modern medical sciences which heavily relies on mathematical models.

In my work I review the most important characteristics of the tumors and the therapies most commonly applied nowadays. I give a summary of the Targeted Molecular Therapies, with a focus on the significance of antiangiogenic therapies. The growth of tumors under antiangiogenic inhibition was described with a mathematical model by Hahnfeldt. In my research I have examined the original and the simplified version of the Hahnfeldt model.

Magnetic Resonance Imaging (MRI) is an extremely effective in vivo imaging technique for visualize internal structures of the body in detail applying nuclear magnetic resonance phenomena. MRI provides excellent soft-tissue contrast, which makes it especially useful in imaging tumors in a non-invasiv way. In research objective analyzing the dynamics of the tumor growth and monitoring the effects of angiogenic inhibitors is important to create and validate mathematical models.

My tasks were getting knowledge of the principles of magnetic resonance phenomena, policy and biological motivation of MRI. After carrying out MRI measurements I processed the acquired images to examine subcutan mouse tumors. Based on flood fill algorithm I developed software in Matlab environment which can help the segmentation of tumor area. As result of my work tumor volume can be calculated and a good estimation can be provided compared to caliper-measured data.

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