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Surgery, chemotherapy and radiotherapy are the most commonly employed treatment options in the fight against cancer. Out of these, radiotherapy presents a non-invasive strategy that makes use of ionizing radiation for selective tumor cell sterilization, being administered to about 50$\%$ of all cancer patients in Europe. The concept of precision medicine consists in moving away from a one-fits-all approach and towards personalized treatment schemes that account for the expected individual patient response to a given therapy. As such, radiotherapy is a highly personalized form of treatment, where a dedicated treatment plan is designed based on individual patient data in the form of biomedical images and clinical characteristics. In order to determine the optimal treatment approach, radiobiological modeling is an essential tool in this context, translating patient properties and treatment parameters into estimations of the probability for tumor control and radiation-induced side effects. Not only does this form of modeling allow for a treatment design suited to the patient-specific type of cancer and consequent risk for specific side effects, but it can also be employed to assess the potential benefit of new and alternative treatment strategies. The use of charged particles for external beam therapy is a modality rapidly gaining in popularity thanks to their peculiar physical interactions with matter, characterized by a sharp dose peak at the end of their track. This allows for the sparing of healthy tissue distal to the target, reducing the probability of side effects compared to conventional photon therapy at equal tumor control. Especially in the field of proton therapy, developments in beamline technology and biomedical imaging have enabled a wide range of highly specialized treatment options that emphasize the possibility to tailor treatments to the patient-specific situation. In order to estimate the effectiveness of these different treatment strategies, the development and continuous validation of radiobiological models for outcome estimation, based on the history of previously treated patients, is a prerequisite. The first part of this thesis is therefore dedicated to the collection and analysis of treatment outcome data from a large number of patients treated with proton therapy. Follow-up data about the incidence of late side effects, such as radiation-induced optic neuropathy and temporal lobe radionecrosis, has been collected to model the patient-specific risk for these side effects. It was found that a combination of risk factors, such as an older age, arterial hypertension or higher dose are the driving factors behind the incidence of the studied healthy tissue toxicities. These risk factors as well as normal tissue complication probability estimations can be transferred into the treatment planning process and, by placing patients into specific risk groups, adjustments be made to personalize each patient treatment to their maximum benefit. Further treatment personalization possibilities are becoming available by the usage of newly developed biomarkers that, based on modern medical imaging, allow functional mapping of the tumor microenvironment. Tumor response to the same therapy can vary greatly across the patient population and the investigation of the metabolic or oxygenation status can add essential information to the planning procedure with the option to tailor the treatment accordingly. Hypoxia refers to a state in the tumor microenvironment, where the cells are exposed to insufficient amounts of oxygen, which has been shown to correlate with worse treatment outcomes. For example, proton therapy with a capacity for precise dose painting facilitated by pencil-beam scanning, could be a suitable treatment modality for cancers that are subject to particularly high radioresistance in the form of hypoxia. The second part of this thesis is dedicated to the evaluation of the clinical potential for hypoxia-guided proton therapy in advanced-stage lung cancer treatments, making use of dedicated radiobiological models concerning radiosensitivity and treatment outcome estimations. A strategy with proton therapy is proposed that makes use of novel functional oxygenation imaging with positron emission tomography, to determine the patient-specific level of hypoxia which are taken into account in the design of dose-escalated treatments. Not only is it possible to, by calibrating the dose escalation, mitigate the hypoxia-induced loss in tumor control probability, but also reduce normal tissue complication probabilities for lungs, heart and esophagus significantly compared to the state-of-the-art approach with conventional photon therapy. Because of the well-known sensitivity of proton therapy to respiratory organ motion, time-resolved anatomical computed tomography imaging is taken into account to simulate these proton treatments under realistic motion conditions. While motion-induced dose degradation reduces the chances to control the disease, benefits of this hypoxia-targeted dose escalation strategy remain. Considering that dose escalation with photon therapy is challenging due to excess dose to healthy tissue, this makes proton therapy a valid option for the treatment of advanced-stage lung cancer affected by hypoxia which should be explored in clinical trials once the availability of reliable functional oxygenation imaging is given. Overall, these results showcase how patient data and emerging biomedical imaging technology in combination with radiobiological modeling can contribute to finding the best radiotherapy treatment for each patient. While requiring further validation, clinical trials and improved accessibility to modern imaging technology, the presented models and treatment strategies lay a foundation for a personalized proton treatment design through which the quality of care in radiation oncology can be improved. |
