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Monte Carlo Simulation for four CRC Incidence Models
A Monte Carlo Simulator determines the risk of colorectal cancer (CRC) development in a population by calculating multiple, individual patient trajectories.
Version 2.0 - published on 12 Dec 2008
This tool is closed source.
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| Abstract |
There are four options for a model framework – three models from the literature and one flexible structure. The parameters of each model, such as mutation rates, cell birth rates, time it takes for a small adenoma to become a large adenoma, etc, can be adjusted to alter the patient colon dynamics. For a given model framework and parameter set, the behavior of a population can be approximated by accumulating the results of multiple Monte Carlo trials representing the numerous patients. In the tool, these trajectories can also be short-circuited by selecting patient ages when the colon is observed. If pre-cancerous lesions are present, the efficiency of their detection and removal is another adjustable parameter. Approximate simulation times depend on the selected model framework and the number of Monte Carlo trials. The default model is the Loeve model, and the default number of trials is 200. The table below lists the models and their execution times for 100, 200, 1000, and 10,000 trials.
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| references | Shah BH, Borwanker JD, and Ramkrishna D. “Monte Carlo simulation of microbial population growth,” Mathematical Biosciences, 31: 1-23, 1976. Nordling, C.O., "A new theory on cancer-inducing mechanism."British Journal of Cancer, 7, 68-72, 1953. Luebeck, E.G. and Moolgavkar, S.H., "Multistage carcinogenesis and the incidence of colorectal cancer." PNAS, 99: 15095-15100, 2002. Loeve F., Boer R., van Oortmarssen G.J., van Ballegooijen M., and Habbema J.DF. "The MISCAN-COLON simulation model for the evaluation of colorectal cancer screening". Computers and Biomedical Research, 32: 13-33, 1999. |
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| Cite this work | Researchers should cite this work as follows: |
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| Tags |
In the Monte Carlo simulation tool, each trial is a random simulation of the colon dynamics which represents the behavior of an individual patient. These dynamics are governed by an underlying mathematical model of the colon, where both the model framework and its parameters affect the rates of transformation and growth of neoplastic lesions.