The aim of this contribution is to outline how methods of system analysis, control theory and computer science can be applied to simulate malignant and normal cell growth and to optimize cancer treatment. Based on biological observations and cell kinetic data, our group has constructed three types of computer models: 1) A cell cycle model describing the spatial (3D) and temporal growth of tumor spheroids; 2) A compartment model describing the growth of rapidly proliferating normal cells; 3) A compartment model simulating slowly proliferating normal tissues. These growth models have been extended by an irradiation model based on the linear-quadratic survival function. Different clinical fractionation schemes (standard-, super-, hyperfractionation and weekly high single dose) have been applied to the tissues mentioned above. The simulation results show that in the case of irradiating a rapidly growing tumor spheroid the hyperfractionation (3 x 1-1.5 Gy per day) leads to a particularly good anti-tumor effectiveness. On the other hand, the radiogenic response of rapidly growing normal tissue to a hyperfractionated treatment schedule is severe. The same result is observed when simulating the late reaction on slowly growing parenchymal tissue. Therefore, this therapeutic modality is ensured only if the overall dose is reduced from DTOTAL = 60 Gy to DTOTAL = 50 Gy.(ABSTRACT TRUNCATED AT 250 WORDS)
Previous studies have shown that systems analysis, control theory and computer science can stimulate new approaches to interpret cancer as an unstable closed-loop control circuit, to study tumor growth, and to optimize tumor treatment. The aim of this paper is: 1. modeling the growth of tumor spheroids; 2. simulating different clinical treatment schedules applied to irradiation of in-vitro tumor spheroids; 3. considering the side effects on normal tissue. A comparison of the simulation results with clinical experience demonstrates that the clinical reality can qualitatively be represented by the model. This method enables a reduction of timeconsuming studies prior to clinical therapy.
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