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Space Radiation Carcinogenesis: Reducing the Uncertainties.    Robert L. Ullrich. Colorado State University, Fort Collins, CO 80523.

   It is recognized that one of the primary risks from radiation during space flight is radiation-induced cancer.  Understanding of potential cancer risks is hampered by the fact that there are no quantitative data from human populations exposed to the types of radiations encountered in deep space.  As a result, estimates must rely on data derived from experimental systems.  Using this approach requires adequate quantitative data for tumor induction in animal models and sufficient understanding of mechanisms so that appropriate models can be developed to allow extrapolation of risks.  Currently, such quantitative data and insight into carcinogenic mechanisms, especially with respect to potential unique biological effects of the types of radiation encountered during space flight, are lacking. Quantitative understanding of cancer risks of ionizing radiation remains a difficult task despite the many years of study of human populations exposed under a variety of conditions. Data derived from experimental systems have provided important insights but fundamental questions remain.  Early experimental studies in biology related to radiation-induced cancer were largely descriptive in nature.  This was mainly related to technical limitations in biological research.  However, recent advances in techniques in cell and molecular biology and their application in cancer research are increasing the ability to directly approach important questions and provide a better understanding of cancer risks.  This information should provide the information necessary for appropriate biologically-based models to be developed for extrapolation of risks.  Currently, there are only a limited number of animal models amenable to this approach that may be of direct relevance to cancer risks in humans. This paper will describe available data, current views of mechanisms involved in radiation-induced cancer, and emerging information from new model systems.