Growth of a virtual tumour using probabilistic methods of cell generation.
A study into treatment enhancement in combined chemo-radiotherapy for unresectable head and neck cancer has initiated the development of a computer model of tumour growth. The model is based on biological parameters, and characterises tumour growth prior to chemo-radiotherapy. Tumour growth starting from a single stem cell is modelled using the Monte Carlo method. The type of the cell function, their relative proportions on mitosis, their proliferative capacity, the duration of the four phases of the cell cycle, the mean cell cycle time, and the cell loss due to natural causes are the main parameters of the basic model. A Gaussian distribution function operates in establishing the cell cycle time, with a mean value of 33 hours, while the cell type is sampled from a uniform distribution. With the established model, the sensitivity of the developed tumour's cell population to the stem, proliferative and nonproliferative ratio at mitosis was assessed. The present model accurately reflects the exponential distribution of cells along the cell cycle (70% cells in GI phase, 15% in S, 10% in G2, 5% in M) of a developed tumour as described in the literature. The proportion of stem, finitely proliferating and resting cells during tumour growth is maintained within their biological limits (2% stem, 13% finitely proliferating, 85% nonproliferating cells). The ratio (R = 3) between the time necessary to develop a clinically detectable tumour (10(9) cells) and the further time to grow to its lethal size (10(12) cells) is in accordance with the biological data when tumour volume is compared for the two periods (30 doublings and 10 doublings respectively). In conclusion, computer simulation can illustrate the biological growth of a tumour and the cell distribution along the cell cycle. These distributions may then be used in the assessment of tumour response to radiotherapy and to specific chemotherapeutic agents.
Marcu
L
L
Department of Physics and Mathematical Physics, University of Adelaide, Adelaide, SA. lmarcu@mail.rah.sa.gov.au
van Doorn
T
T
Zavgorodni
S
S
Olver
I
I
D002452:Cell Count; D002453:Cell Cycle; D002455:Cell Division; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D009369:Neoplasms / Q000473:pathology* / Q000503:physiopathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity