Iterated birth and death process as a model of radiation cell survival.
The iterated birth and death process is defined as an n-fold iteration of a stochastic process consisting of the combination of instantaneous random killing of individuals in a certain population with a given survival probability s with a Markov birth and death process describing subsequent population dynamics. A long standing problem of computing the distribution of the number of clonogenic tumor cells surviving a fractionated radiation schedule consisting of n equal doses separated by equal time intervals tau is solved within the framework of iterated birth and death processes. For any initial tumor size i, an explicit formula for the distribution of the number M of surviving clonogens at moment tau after the end of treatment is found. It is shown that if i-->infinity and s-->0 so that is(n) tends to a finite positive limit, the distribution of random variable M converges to a probability distribution, and a formula for the latter is obtained. This result generalizes the classical theorem about the Poisson limit of a sequence of binomial distributions. The exact and limiting distributions are also found for the number of surviving clonogens immediately after the nth exposure. In this case, the limiting distribution turns out to be a Poisson distribution.
Hanin
L G
LG
Department of Mathematics, Idaho State University, Pocatello, ID 83209-8085, USA. hanin@isu.edu
D002470:Cell Survival / Q000528:radiation effects; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D009369:Neoplasms / Q000473:pathology* / Q000532:radiotherapy*; D016012:Poisson Distribution; D018787:Radiation Oncology; D013269:Stochastic Processes