Background and Significance The application of heavy ion beams in cancer therapy must account for the increasing relative biological effectiveness (RBE) with increasing penetration depth when determining dose prescriptions and organ at risk (OAR) constraints in treatment planning. Because RBE depends in a complex manner on factors such as the ion type, energy, cell and tissue radiosensitivity, physical dose, biological endpoint, and position within and outside treatment fields, biophysical models reflecting these dependencies are required for the personalization and optimization of treatment plans. Aim To review and compare three mechanism‐inspired models which predict the complexities of particle RBE for various ion types, energies, linear energy transfer (LET) values and tissue radiation sensitivities. Methods The review of models and mechanisms focuses on the Local Effect Model (LEM), the Microdosimetric‐Kinetic (MK) model, and the Repair‐Misrepair‐Fixation (RMF) model in combination with the Monte Carlo Damage Simulation (MCDS). These models relate the induction of potentially lethal double strand breaks (DSBs) to the subsequent interactions and biological processing of DSB into more lethal forms of damage. A key element to explain the increased biological effectiveness of high LET ions compared to MV x rays is the characterization of the number and local complexity (clustering) of the initial DSB produced within a cell. For high LET ions, the spatial density of DSB induction along an ion's trajectory is much greater than along the path of a low LET electron, such as the secondary electrons produced by the megavoltage (MV) x rays used in conventional radiation therapy. The main aspects of the three models are introduced and the conceptual similarities and differences are critiqued and highlighted. Model predictions are compared in terms of the RBE for DSB induction and for reproductive cell survival. Results and Conclusions Comparisons of the RBE for DSB induction and for cell survival are presented for proton (1H), helium (4He), and carbon (12C) ions for the therapeutically most relevant range of ion beam energies. The reviewed models embody mechanisms of action acting over the spatial scales underlying the biological processing of potentially lethal DSB into more lethal forms of damage. Differences among the number and types of input parameters, relevant biological targets, and the computational approaches among the LEM, MK and RMF models are summarized and critiqued. Potential experiments to test some of the seemingly contradictory aspects of the models are discussed.

Purpose Anatomical changes and patient setup uncertainties during intensity modulated proton therapy (IMPT) of head and neck (HN) cancers demand frequent evaluation of delivered dose. This work investigated a cone-beam computed tomography (CBCT) and deformable image registration based therapy workflow to demonstrate the feasibility of proton dose calculation on synthetic computed tomography (sCT) for adaptive IMPT treatment of HN cancer. Materials and Methods Twenty-one patients with HN cancer were enrolled in this study, a retrospective institutional review board protocol. They had previously been treated with volumetric modulated arc therapy and had daily iterative CBCT. For each patient, robust optimization (RO) IMPT plans were generated using ±3 mm patient setup and ±3% proton range uncertainties. The sCTs were created and the weekly delivered dose was recalculated using an adaptive dose accumulation workflow in which the planning computed tomography (CT) was deformably registered to CBCTs and Hounsfield units transferred from the planning CT. Accumulated doses from ±3 mm/±3% RO-IMPT plans were evaluated using clinical dose-volume constraints for targets (clinical target volume, or CTV) and organs at risk. Results Evaluation of weekly recalculated dose on sCTs showed that most of the patient plans maintained target dose coverage. The primary CTV remained covered by the V95 > 95% (95% of the volume receiving more than 95% of the prescription dose) worst-case scenario for 84.5% of the weekly fractions. The oral cavity accumulated mean dose remained lower than the worst-case scenario for all patients. Parotid accumulated mean dose remained within the uncertainty bands for 18 of the 21 patients, and all were kept lower than RO-IMPT worst-case scenario for 88.7% and 84.5% for left and right parotids, respectively. Conclusion This study demonstrated that RO-IMPT plans account for most setup and anatomical uncertainties, except for large weight-loss changes that need to be tracked throughout the treatment course. We showed that sCTs could be a powerful decision tool for adaptation of these cases in order to reduce workload when using repeat CTs.

Mechanism of Action External beam, whether with photons or particles, remains as the most common type of radiation therapy. The main drawback is that radiation deposits dose in healthy tissue before reaching its target. Boron neutron capture therapy (BNCT) is based on the nuclear capture and fission reactions that occur when 10B is irradiated with low-energy (0.0025 eV) thermal neutrons. The resulting 10B(n,α)7Li capture reaction produces high linear energy transfer (LET) α particles, helium nuclei (4He), and recoiling lithium-7 (7Li) atoms. The short range (5-9 μm) of the α particles limits the destructive effects within the boron-containing cells. In theory, BNCT can selectively destroy malignant cells while sparing adjacent normal tissue at the cellular levels by delivering a single fraction of radiation with high LET particles. History BNCT has been around for many decades. Early studies were promising for patients with malignant brain tumors, recurrent tumors of the head and neck, and cutaneous melanomas; however, there were certain limitations to its widespread adoption and use. Current Limitations and Prospects Recently, BNCT re-emerged owing to several developments: (1) small footprint accelerator-based neutron sources; (2) high specificity third-generation boron carriers based on monoclonal antibodies, nanoparticles, among others; and (3) treatment planning software and patient positioning devices that optimize treatment delivery and consistency.