Radiotherapy-Induced Carcinogenesis and Leukemogenesis: Mechanisms and Quantitative Modeling

  • David J. Brenner
  • Igor Shuryak
  • Rainer K. Sachs
Part of the Medical Radiology book series (MEDRAD)


Biologically-based modeling of spontaneous and radiation-induced carcinogenesis has a history spanning several decades. Such models are important conceptual and quantitative tools, particularly useful whenever cancer risks must be estimated under exposure situations for which no data yet exist, e.g., for novel and prospective radiotherapy protocols. Direct extrapolation from existing data is often not possible due to complex differences between the data sets, but quantitative models can accommodate such extrapolation. Many carcinogenesis models can be characterized as short-term, in that they focus on those processes occurring during and shortly after irradiation. The main advantage of this class of models is that they provide a detailed initial dose response for short-term endpoints which are used as surrogates for carcinogenesis. The main disadvantage is that the possibly substantial modulations of the magnitude and shape of this initial dose response during the lengthy period between irradiation and manifestation of typical solid tumors are not considered. In contrast with the short-term models, another class of biologically-motivated models can be characterized as long-term, in the sense that they track carcinogenesis mechanisms throughout the entire human life span. The main advantages of long-term models are: (1) modulation of the radiation dose response during the long latency period between exposure and diagnosis of cancer is included; and (2) extensive data on spontaneous cancers can be used to help determine the adjustable parameters needed to estimate cancer risks. The main disadvantage is that the early radiation response is typically treated in a less-mechanistic manner than in the short-term models. Here we review some short- and long-term model examples and the carcinogenesis mechanisms which they incorporate. We also discuss an example of unification of both model classes, focusing on application of such formalisms for quantifying radiotherapy-induced second cancer risks.


Cancer Incidence Genomic Instability Clonal Expansion Excess Relative Risk Atomic Bomb Survivor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • David J. Brenner
    • 1
  • Igor Shuryak
    • 1
  • Rainer K. Sachs
    • 2
  1. 1.Center for Radiological ResearchColumbia University Medical CenterNew YorkUSA
  2. 2.Departments of Mathematics and PhysicsUniversity of CaliforniaBerkeleyUSA

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