Radiation Oncology Physics: A Handbook for Teachers and Students

The linear-quadratic model is one of the key tools in radiation biology and physics. It provides a simple relationship between cell survival and delivered dose: [Formula: see text], and has been used extensively to analyse and predict responses to ionising radiation both in vitro and in vivo. Despite its ubiquity, there remain questions about its interpretation and wider applicability-Is it a convenient empirical fit or representative of some deeper mechanistic behaviour? Does a model of single-cell survival in vitro really correspond to clinical tissue responses? Is it applicable at very high and very low doses? Here, we review these issues, discussing current usage of the LQ model, its historical context, what we now know about its mechanistic underpinnings, and the potential challenges and confounding factors that arise when trying to apply it across a range of systems.

https://iopscience.iop.org/article/10.1088/1361-6560/aaf26a/pdf

The linear quadratic model: usage, interpretation and challenges

J. DAnwl B8NrL.nd, Ph.D., isIlssU14nt professor Andhed, Physic Section, Dep.:ntm ent ofR.4dUaion Oncology, Wa e Forest Unit/miry School of Medicine in Winston-Salem, N.G. He ischairman ofthe AAPM Electronic Media Coordinating Committee, chairman ofthe new AAPM Subcommitteeon MoieCHlaTImaging in Clinical Radiation Oncology, and aformer member ofthe AAPM RildiAtion Therllpy Committee. The AAPM is headqNllTtered in College PIlTk, Md. WHO WE ARE The America n Assoc iation of Physicists in Medicine (AAPM) h J S ap proximately 4.62S mcmbers who practice or arc associn ed with medical physics.

American Association of Physicists in Medicine

The book is based on a laboratory course in nuclear physics given to advanced students. It treats the experimental techniques and instrumentationmost often used in nuclear and particle physics experiments as well as in various other experimental sciences. It provides most useful results and formulae, technical know-how and informative details on -interactionof radiation in matter; - radion protection and radioactive sources; - statistics for the interpretation and analysis of data; - principles and operation of the main types of detectors (ionization, scintillation and semiconductor detectors); - nuclear electronics instrumentation (NIM, CAMAC); - various systems and techniques for experiments. Thanks to the author's long teaching experience the material is presented in a verypractical, hands-on way making the book a useful text and lab companion for students and experienced scientists.

Detectors for Particle Radiation and Introduction to Experimental Particle Physics and Techniques for Nuclear and Particle Physics Experiments: A How‐To Approach

Biological responses to ionizing radiation (IR) have been studied for many years, generally showing the dependence of these responses on the quality of radiation, i.e., the radiation particle type and energy, types of DNA damage, dose and dose rate, type of cells, etc. There is accumulating evidence on the pivotal role of complex (clustered) DNA damage towards the determination of the final biological or even clinical outcome after exposure to IR. In this review, we provide literature evidence about the significant role of damage clustering and advancements that have been made through the years in its detection and prediction using Monte Carlo (MC) simulations. We conclude that in the future, emphasis should be given to a better understanding of the mechanistic links between the induction of complex DNA damage, its processing, and systemic effects at the organism level, like genomic instability and immune responses.

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

The FLUKA Monte Carlo code coupled with an OER model for biologically weighted dose calculations in proton therapy of hypoxic tumors.

Cranio-spinal irradiation (CSI) using protons has dosimetric advantages compared to photons and is expected to reduce risk of adverse effects. The proton relative biological effectiveness (RBE) varies with linear energy transfer (LET), tissue type and dose, but a variable RBE has not replaced the constant RBE of 1.1 in clinical treatment planning. We examined inter-patient variations in RBE for ten proton CSI patients. Variable RBE models were used to obtain RBE and RBE-weighted doses. RBE was quantified in terms of dose weighted organ-mean RBE ($${\overline{{\rm{RBE}}}}_{{\rm{d}}}$$ = mean RBE-weighted dose/mean physical dose) and effective RBE of the near maximum dose (D2%), i.e. RBED2% = $${D}_{2 \% ,RBE}/{D}_{2 \% ,phys}$$, where subscripts RBE and phys indicate that the D2% is calculated based on an RBE model and the physical dose, respectively. Compared to the median $${\overline{{\rm{RBE}}}}_{{\rm{d}}}$$ of the patient population, differences up to 15% were observed for the individual $${\overline{{\rm{RBE}}}}_{{\rm{d}}}$$ values found for the thyroid, while more modest variations were seen for the heart (6%), lungs (2%) and brainstem (<1%). Large inter-patient variation in RBE could be correlated to large spread in LET and dose for these organs at risk (OARs). For OARs with small inter-patient variations, the results show that applying a population based RBE in treatment planning may be a step forward compared to using RBE of 1.1. OARs with large inter-patient RBE variations should ideally be selected for patient-specific biological or RBE robustness analysis if the physical doses are close to known dose thresholds.

Inter-patient variations in relative biological effectiveness for cranio-spinal irradiation with protons

Protons and heavier ions have an increased relative biological effectiveness (RBE) compared to photons. While variable RBE models are applied clinically in carbon ion therapy, the RBE in proton therapy is accounted for clinically by applying a constant RBE of 1.1. However, an increasing amount of experimental and clinical data show that also the proton RBE varies spatially within the patient. In addition, the existing carbon ion RBE models give substantially different RBE-weighted dose (often referred to as biological dose) distributions for the same irradiation scenarios. Improving the current RBE calculations is therefore crucial for the treatment received by patients. In this thesis, variables affecting the RBE and biological dose models were studied using the FLUKA Monte Carlo code. In the first part of the thesis, a low-energy proton beam cell irradiation experiment at the Oslo Cyclotron Laboratory (OCL) was implemented in FLUKA (Paper I). Applying the FLUKA implementation, the dose and linear energy transfer (LET) (both the dose-averaged LET (LETd) and LET spectra) were estimated in potential cell irradiation positions. The LETd values increased along the beam path, up to approximately 40 keV/μm in the distal dose-fall off. The LET spectra became narrower with depth in water. Comparisons with a simulated 80 MeV proton beam showed that the OCL beam had significantly higher LETd values and much narrower LET spectra for the same LETd values. The FLUKA implementation of the OCL beam demonstrated the importance of having proper proton beam characteristics to achieve accurate RBE versus LET data. In the second part of the thesis, the RBE model applied clinically in carbon ion therapy in Japan (the microdosimetric kinetic model, MKM) was implemented in FLUKA (Paper II). For the implementation, tables connecting the saturationcorrected dose-mean specific energy (  ) to particle type and particle kinetic energy were generated. The FLUKA implementation was then used to study the sensitivity of the MKM to variations in the model parameters (Paper III). The created  tables agreed well with the tables applied clinically in Japan. The

Studies of the Relative Biological Effectiveness and Biological Dose in Proton and Carbon Ion Therapy

This master project is the first vendor-independent performance evaluation of the new nanoScan PET/CT system at the University of Bergen. A comprehensive performance evaluation of a novel scanner is very important, particularly when quantitative assessments of images are required. The nanoScan PET/CT system is a fully integrated small-animal PET/CT system. An abbreviated performance evaluation of the CT subsystem was done, which included a Hounsfield quality check, a comparison of reconstruction filters and an evaluation of the different scanning methods. The PET subsystem was performance evaluated according to the NEMA NU 4-2008 standard. This standard includes tests of spatial resolution, counting rate capabilities, sensitivity and image quality. The CT evaluation proved adequate for its intended use. There were only minor differences in the noise measurement of the different reconstruction filters. The scanning method “helical, 1 pitch” would for most applications be recommended, as this scanning method had lowest dose, good images, and just few minutes longer scan time than the scanning method with lowest scan time. The measurements from the PET evaluation were in good agreement with values reported by vendor and in literature. The evaluation of the scanner shows that it has one of the best spatial resolutions available, approximately 1 mm at center of field of view (FOV). The sensitivity at center of FOV was 8.8%, just a bit lower than the highest reported absolute sensitivity at center of FOV, i.e. 10%. The counting rate capabilities proved adequate for all applications undertaken to date, and the NEMA image quality phantom studies demonstrated good values of uniformity and recovery coefficients. The procedures and methods in this thesis will make it easier to monitor the scanner performance with periodic testing to check if the scanner is robust, reliable and reproducible.

Performance Evaluation of a Small-Animal PET/CT System

Introduction Variable relative biological effectiveness (RBE) models allow for differences in linear energy transfer (LET), physical dose and tissue type to be accounted for when quantifying and optimizing the biological damage of protons. These models are complex and fraught with uncertainties, and therefore simpler RBE-optimization strategies have also been suggested. Our aim was to compare several biological optimization strategies for proton therapy by evaluating their performance in different clinical cases. Materials and Methods Two different optimization strategies were compared: full variable RBE optimization and differential RBE optimization which involve applying fixed RBE for the planning target volume (PTV) and variable RBE in organs at risk (OARs). The optimization strategies were coupled to two variable RBE models and one LET-weighted dose model (LWD) with performance demonstrated on three different clinical cases: brain, head and neck and prostate tumors. Results Cases with low ( α / β ) x in the tumor showed a large impact of the full RBE optimization strategies, with up to 10% reduction in RBE-weighted dose to the PTV and OAR compared to the reference plan, while smaller variations were obtained with differential optimization ( ( α / β ) x , the differential RBE optimization strategy showed a greater reduction in RBE-weighted dose to the OARs compared to the reference plan and the full RBE optimization strategy. Conclusion Differences between the optimization strategies varied across the studied cases, influenced by both biological and physical parameters. While full RBE optimization demonstrated greater OAR sparing, awareness of underdosage to the target must be carefully considered.

Tordis J. Dahle

Papers

The FLUKA Monte Carlo code coupled with an OER model for biologically weighted dose calculations in proton therapy of hypoxic tumors.

Inter-patient variations in relative biological effectiveness for cranio-spinal irradiation with protons

Studies of the Relative Biological Effectiveness and Biological Dose in Proton and Carbon Ion Therapy

Performance Evaluation of a Small-Animal PET/CT System

The Organ Sparing Potential of Different Biological Optimization Strategies in Proton Therapy.