Sensitivity study of the microdosimetric kinetic model parameters for carbon ion radiotherapy

Abstract

Acknowledgements This thesis could not have been written without help and support from the people I have worked with the last four years. I am very grateful and privileged for the opportunity this thesis has given me to work with all of you. The thesis would neither have been possible without funding. I am grateful for the financial support from the University of Bergen and the Trond Mohn Foundation. However, I would first and foremost like to thank my main supervisor, Kristian Smeland Ytre-Hauge, for all the help and encouragement throughout these years. Thank you for giving me this opportunity and for guiding me through these four years. Your support in all parts of this PhD project has been invaluable. I really appreciate all the time you spend on making your group in medical physics into such a good place to be. I want to give warm thanks to my co-supervisors Andrea Mairani and Camilla Stokkevåg. Thank you, Andrea, for inviting me to spend five weeks at CNAO in Pavia. Our discussions and your help with FLUKA have been very valuable. I really appreciate that you always have been available, also after my stay in Pavia. To Camilla: Thank you for your guidance during these years. I really appreciate your clinical insight and all the work you have put into helping me, especially by giving positive and constructive feedback on my work. My stay at CNAO gave me a more practical insight to the field of particle therapy, and I got to know many people who helped me with my work. Thanks to Giuseppe Magro, for welcoming me to CNAO and making sure that I had a good time both at and after work. Your help, both at CNAO and later, has really been appreciated. I would also like to thank Kyungdon Choi for help with programming during and after my stay at CNAO. I would like to acknowledge all my collaborators in Oslo. I am especially grateful to Eirik Malinen, who has been my main contact there and who has been involved in all my work with the Oslo group. I would also like to thank Nina Edin and Anne Marit Rykkelid for giving me insight into cell irradiation experiments and for help on the Oslo Cyclotron Laboratory beamline, and Espen Rusten for help with the hypoxia project, especially with analyzing PET images. Through the people in Oslo, I was also introduced to several PET experts in Turku, Finland, and I would like to thank you all for sharing your knowledge.

Ionizing Radiation and Complex DNA Damage: From Prediction to Detection Challenges and Biological Significance

“…The most characteristic example of these models is the linear quadratic cell survival model (LQ), which is widely used in conjunction with experiments [33,34]. Other models, such as the local effect model (LEM) [35] and the microdosimetric kinetic model (MKM) [36], take into account the LET and other quality characteristics of the irradiating particles and the tracks of the secondary particles in the calculation of the cell survival curves. Besides these models, the BIANCA model, in addition to cell survival, also predicts chromosome aberrations, which can be regarded as a strong indicator of normal tissue damage [3,37].…”

In vitro modified microdosimetric kinetic model–based predictions for B14-150 cells survival in 450 MeV/u carbon ion beam with aluminum ridge filter for biologically optimized spread-out Bragg peak

“…For low-LET fragments the B14-150 cells have a and b values comparable to those of other Chinese hamster cells presented in the PIDE database [23] The fitting procedure performed with python scipy package implementing Trust Region Reflective algorithm [24] and simultaneous iterating over R N and R D until we reach the five digits after decimal point agreement with the scipy results. Thus, uncertainties of R N and R D are negligible in comparison to the a ion and b ion uncertainties, but as it demonstrated by [25] they may serve only as a normalization factor of the resulted SOBP curve.…”

Hypoxia adapted relative biological effectiveness models for proton therapy: a simulation study

“…This, together with enough primaries on the MC simulations to calculate physical quantities (voxel mean statistical uncertainty <2% and voxel maximum statistical uncertainty <3%), allows us to compare the outcome from the OER models as the impact of the assumptions made by the authors on the results instead of as the impact of the different uncertainties from the data on the results. A sensitivity study of the impact of uncertainties on dose estimates, as presented by Dahle et al [32], would be of high interest also for ROWD estimations.…”