Hufnagl, Antonia Isabelle (2020)
Modelling neoplastic cell transformation and tumour induction for charged particles with the local effect model.
Technische Universität Darmstadt
doi: 10.25534/tuprints-00011935
Ph.D. Thesis, Primary publication
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Item Type: | Ph.D. Thesis | ||||
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Type of entry: | Primary publication | ||||
Title: | Modelling neoplastic cell transformation and tumour induction for charged particles with the local effect model | ||||
Language: | English | ||||
Referees: | Scholz, PD Dr. Michael ; Drossel, Prof. Dr. Barbara | ||||
Date: | 2020 | ||||
Place of Publication: | Darmstadt | ||||
Date of oral examination: | 15 June 2020 | ||||
DOI: | 10.25534/tuprints-00011935 | ||||
Abstract: | Radiotherapy of cancer is a rapidly advancing technology and has led to an increased number of longterm survivors. It is, however, often intertwined with undesirable side effects, such as secondary cancer, which can occur several years or decades after the treatment. It is therefore crucial to estimate secondary cancer risk after radiotherapy in order to deliver the best possible treatment to the patient. This is especially important for pediatric patients and young adults that have a long lifetime expectancy. Particle therapy is a new treatment modality that offers superior dose conformity and efficient sparing of normal tissue compared to photon therapy, and has in particular been proposed for the patient group mentioned above. However, due to limited clinical and epidemiological studies of particle therapy, the carcinogenic potential of ion radiation is not yet fully understood. Therefore, radiobiological models are needed for evaluating the systematics of carcinogenesis related effects after particle irradiation. In this work, a novel method for simulating the relative biological effectiveness of particle radiation with regard to neoplastic cell transformation as initial step in tumour development was implemented. This was performed by employing a radiobiological model for estimating biological effects after particle radiation (local effect model). The induction of lethal and mutagenic events were considered as statistically correlated processes that both originate from DNA damage. In order to correctly describe the joint probability of these two processes, the local effect model was applied twice. Additional to modelling neoplastic cell transformation and tumour induction after particle radiation, secondary cancer risk estimates for various scanned proton and carbon ion beam treatment plans were compared. In a first step, treatment plans were analysed for an idealized geometry in order to assess the underlying systematics of cancer induction. In a second step, secondary cancer risks were compared for 20 patient proton and carbon ion treatment plans. The results show good agreement between experimental and simulated neoplastic cell transformation in vitro and tumour induction probabilities in animal models for particle radiation, allowing the application of the implemented method for estimating secondary cancer risks after particle radiotherapy. With this method it was possible to assess secondary cancer risk dependence on several factors such as treatment plan geometry, fractionation scheme and tissue radiosensitivity. A lower secondary cancer risk was estimated for carbon ions compared to protons at the lateral field margins in the entrance channel due to reduced lateral scattering of carbon ions, while an increased risk was found closely behind the tumour due to fragmentation of carbon ions. The observed general systematics enabled to consistently explain secondary cancer risk after proton and carbon ion beam therapy and is in agreement with results from previous studies. For the considered patient treatment plans, reduced median secondary cancer risks were predicted for proton therapy compared to carbon ion beam therapy for the majority of the organs under consideration. The methods established in this work provide a foundation for quantitatively describing carcinogenesis related effects after particle radiation and for optimizing treatment strategies based on individual patient plans with regard to secondary cancer risk. |
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URN: | urn:nbn:de:tuda-tuprints-119352 | ||||
Classification DDC: | 500 Science and mathematics > 500 Science 500 Science and mathematics > 530 Physics |
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Divisions: | DFG-Graduiertenkollegs > Research Training Group 1657 Molecular and cellular responses to ionizing radiation 05 Department of Physics > Institute for condensed matter physics (2021 merged in Institute for Condensed Matter Physics) |
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Date Deposited: | 06 Aug 2020 12:03 | ||||
Last Modified: | 06 Aug 2020 13:02 | ||||
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/11935 | ||||
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