Radiation therapy is one of the most widely used therapies for malignancies. The therapeutic use of heavy ions, such as carbon, has gained significant interest due to advantageous physical and radiobiologic properties compared to photon based therapy. By taking advantage of these unique properties, carbon ion radiotherapy may allow dose escalation to tumors while reducing radiation dose to adjacent normal tissues. There are currently 13 centers treating with carbon ion radiotherapy, with many of these centers publishing promising safety and efficacy data from the first cohorts of patients treated. To date, carbon ion radiotherapy has been studied for almost every type of malignancy, including intracranial malignancies, head and neck malignancies, primary and metastatic lung cancers, tumors of the gastrointestinal tract, prostate and genitourinary cancers, sarcomas, cutaneous malignancies, breast cancer, gynecologic malignancies, and pediatric cancers. Additionally, carbon ion radiotherapy has been studied extensively in the setting of recurrent disease. We aim to provide a comprehensive review of the studies of each of these disease sites, with a focus on the current trials using carbon ion radiotherapy.

Our GPU-based MC is the first of its kind to include a detailed nuclear model to handle nonelastic interactions of protons with any nucleus. Dosimetric calculations are in very good agreement with (GEANT)4.9.6p2/TOPAS. Our MC is being integrated into a framework to perform fast routine clinical QA of pencil-beam based treatment plans, and is being used as the dose calculation engine in a clinically applicable MC-based IMPT treatment planning system. The detailed nuclear modeling will allow us to perform very fast linear energy transfer and neutron dose estimates on the GPU.

The treatment of cancer with proton radiation therapy was first suggested in 1946 followed by the first treatments in the 1950s. As of 2020, almost 200 000 patients have been treated with proton beams worldwide and the number of operating proton therapy (PT) facilities will soon reach one hundred. PT has long moved from research institutions into hospital-based facilities that are increasingly being utilized with workflows similar to conventional radiation therapy. While PT has become mainstream and has established itself as a treatment option for many cancers, it is still an area of active research for various reasons: the advanced dose shaping capabilities of PT cause susceptibility to uncertainties, the high degrees of freedom in dose delivery offer room for further improvements, the limited experience and understanding of optimizing pencil beam scanning, and the biological effect difference compared to photon radiation. In addition to these challenges and opportunities currently being investigated, there is an economic aspect because PT treatments are, on average, still more expensive compared to conventional photon based treatment options. This roadmap highlights the current state and future direction in PT categorized into four different themes, 'improving efficiency', 'improving planning and delivery', 'improving imaging', and 'improving patient selection'.

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Purpose Craniopharyngioma is a pediatric brain tumor whose volume is prone to change during radiation therapy. We compared photon- and proton-based irradiation methods to determine the effect of tumor volume change on target coverage and normal tissue irradiation in these patients. Methods and Materials For this retrospective study, we acquired imaging and treatment-planning data from 14 children with craniopharyngioma (mean age, 5.1 years) irradiated with photons (54 Gy) and monitored by weekly magnetic resonance imaging (MRI) examinations during radiation therapy. Photon intensity-modulated radiation therapy (IMRT), double-scatter proton (DSP) therapy, and intensity-modulated proton therapy (IMPT) plans were created for each patient based on his or her pre-irradiation MRI. Target volumes were contoured on each weekly MRI scan for adaptive modeling. The measured differences in conformity index (CI) and normal tissue doses, including functional sub-volumes of the brain, were compared across the planning methods, as was target coverage based on changes in target volumes during treatment. Results CI and normal tissue dose values of IMPT plans were significantly better than those of the IMRT and DSP plans (p < 0.01). Although IMRT plans had a higher CI and lower optic nerve doses (p < 0.01) than did DSP plans, DSP plans had lower cochlear, optic chiasm, brain, and scanned body doses (p < 0.01). The mean planning target volume (PTV) at baseline was 54.8 cm3, and the mean increase in PTV was 11.3% over the course of treatment. The dose to 95% of the PTV was correlated with a change in the PTV; the R2 values for all models, 0.73 (IMRT), 0.38 (DSP), and 0.62 (IMPT), were significant (p < 0.01). Conclusions Compared with photon IMRT, proton therapy has the potential to significantly reduce whole-brain and -body irradiation in pediatric patients with craniopharyngioma. IMPT is the most conformal method and spares the most normal tissue; however, it is highly sensitive to target volume changes, whereas the DSP method is not.

A MC-based treatment planning system was developed. The treatment planning can be performed in a clinically viable time frame on a hardware system costing around 45,000 dollars. The fast calculation and optimization make the system easily expandable to robust and multicriteria optimization.

The SOI microdosimeter with its well-defined 3D SV has applicability in characterizing proton radiation fields and can measure relevant physical parameters to model the RBE with submillimeter spatial resolution. It has been shown that for a physical dose of 1.82 Gy at the BP, the derived RBE based on the MKM model increased from 1.14 to 1.6 in the BP and its distal part. Good agreement was observed between the experimental and simulation results, confirming the potential application of SOI microdosimeter with 3D SV for quality assurance in proton therapy.

By exploiting GPU acceleration, MC-based, biologically optimized plans were created for small-tumor target patients. This optimizer will be used in an upcoming feasibility trial on LETd painting for radioresistant tumors.