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      RBE and related modeling in carbon-ion therapy

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      Physics in Medicine & Biology
      IOP Publishing

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          Abstract

          Carbon ion therapy is a promising evolving modality in radiotherapy to treat tumors that are radioresistant against photon treatments. As carbon ions are more effective in normal and tumor tissue, the relative biological effectiveness (RBE) has to be calculated by bio-mathematical models and has to be considered in the dose prescription. This review (i) introduces the concept of the RBE and its most important determinants, (ii) describes the physical and biological causes of the increased RBE for carbon ions, (iii) summarizes available RBE measurements in vitro and in vivo, and (iv) describes the concepts of the clinically applied RBE models (mixed beam model, local effect model, and microdosimetric-kinetic model), and (v) the way they are introduced into clinical application as well as (vi) their status of experimental and clinical validation, and finally (vii) summarizes the current status of the use of the RBE concept in carbon ion therapy and points out clinically relevant conclusions as well as open questions. The RBE concept has proven to be a valuable concept for dose prescription in carbon ion radiotherapy, however, different centers use different RBE models and therefore care has to be taken when transferring results from one center to another. Experimental studies significantly improve the understanding of the dependencies and limitations of RBE models in clinical application. For the future, further studies investigating quantitatively the differential effects between normal tissues and tumors are needed accompanied by clinical studies on effectiveness and toxicity.

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          Most cited references186

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          The linear-quadratic formula and progress in fractionated radiotherapy.

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            Exploring the role of cancer stem cells in radioresistance.

            Radiobiological research over the past decades has provided evidence that cancer stem cell content and the intrinsic radiosensitivity of cancer stem cells varies between tumours, thereby affecting their radiocurability. Translation of this knowledge into predictive tests for the clinic has so far been hampered by the lack of methods to discriminate between stem cells and non-stem cells. New technologies allow isolation of cells expressing specific surface markers that are differentially expressed in tumour cell subpopulations that are enriched for cancer stem cells. Combining these techniques with functional radiobiological assays holds the potential to elucidate the role of cancer stem cells in radioresistance in individual tumours, and to use this knowledge for the development of predictive markers for optimization of radiotherapy.
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              The impact of a negligent G2/M checkpoint on genomic instability and cancer induction.

              DNA damage responses (DDR) encompass DNA repair and signal transduction pathways that effect cell cycle checkpoint arrest and/or apoptosis. How DDR pathways respond to low levels of DNA damage, including low doses of ionizing radiation, is crucial for assessing environmental cancer risk. It has been assumed that damage-induced cell cycle checkpoints respond to a single double strand break (DSB) but the G2/M checkpoint, which prevents entry into mitosis, has recently been shown to have a defined threshold of 10-20 DSBs. Here, we consider the impact of a negligent G2/M checkpoint on genomic stability and cancer risk.
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                Author and article information

                Journal
                Physics in Medicine & Biology
                Phys. Med. Biol.
                IOP Publishing
                1361-6560
                January 01 2018
                December 19 2017
                : 63
                : 1
                : 01TR02
                Article
                10.1088/1361-6560/aa9102
                28976361
                0e8b5cc2-3fa6-42cb-8e0e-7a70a3df96dc
                © 2017

                http://iopscience.iop.org/info/page/text-and-data-mining

                http://creativecommons.org/licenses/by/3.0/

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