UNASSIGNED: Currently, intensity-modulated radiation therapy (IMRT) is commonly used in radiotherapy clinics. However, designing a treatment plan with multiple beam angles depends on the experience of human planners, and is mostly achieved using a trial-and-error approach. It is preferrable but challenging to solve this issue automatically and mathematically using an optimization approach. The goal of this study is to develop a mixed-integer linear programming (MILP) approach for the beam angle optimization (BAO) of non-coplanar IMRT for liver cancer.
UNASSIGNED: MILP models for the BAO of both coplanar and non-coplanar IMRT treatment plans were developed. The beam angles of the IMRT plans were first selected by the MILP model built using mathematical optimization software. Next, the IMRT plans with the selected beam angles was created in a commercial treatment planning system. Finally, the fluence map and dose distribution of the IMRT plans were generated under pre-defined dose-volume constraints. The IMRT plans of 10 liver cancer patients previously treated at our institute were used to assessed the proposed MILP models. For each patient, both coplanar and non-coplanar IMRT plans with beam angles optimized by the MILP models were compared with the IMRT plan clinically approved by physicians.
UNASSIGNED: The MILP model-guided IMRT plans showed reduced doses for most of the organs at risk (OARs). Compared with the IMRT plans clinically approved by physicians, the doses for the spinal cord (28.5 vs. 36.1, P=0.001<0.05) and liver (27.6 vs. 29.1, P=0.005<0.05) decreased significantly in the IMRT plans with non-coplanar beams selected by the MILP models.
UNASSIGNED: The MILP model is an effective tool for the BAO in coplanar and non-coplanar IMRT treatment planning. It facilitates the automation of IMRT treatment planning for current high-precision radiotherapy.

This study aims to evaluate the feasibility of using a commercially available boron neutron capture therapy (BNCT) dose calculation program (NeuCure® Dose Engine) in terms of calculation accuracy and computation time. Treatment planning was simulated under the following calculation parameters: 1.5-5.0 mm grid sizes and 1-10% statistical uncertainties. The calculated monitor units (MUs) and computation times were evaluated. The MUs calculated on grid sizes larger than 2 mm were overestimated by 2% compared with the result of 1.5 mm grid. We established the two-step method for the routine administration of BNCT: multiple calculations involved in beam optimization should be done at a 5 mm grid and a 10% statistical uncertainty (the shortest computation time: 10.3 ± 2.1 min) in the first-step, and final dose calculations should be performed at a 2 mm grid and a 10% statistical uncertainty (satisfied clinical accuracy: 6.9 ± 0.3 h) in the second-step.

Over the last decades, the use of artificial intelligence, machine learning and deep learning in medical fields has skyrocketed. Well known for their results in segmentation, motion management and posttreatment outcome tasks, investigations of machine learning and deep learning models as fast dose calculation or quality assurance tools have been present since 2000. The main motivation for this increasing research and interest in artificial intelligence, machine learning and deep learning is the enhancement of treatment workflows, specifically dosimetry and quality assurance accuracy and time points, which remain important time-consuming aspects of clinical patient management. Since 2014, the evolution of models and architectures for dose calculation has been related to innovations and interest in the theory of information research with pronounced improvements in architecture design. The use of knowledge-based approaches to patient-specific methods has also considerably improved the accuracy of dose predictions. This paper covers the state of all known deep learning architectures and models applied to external radiotherapy with a description of each architecture, followed by a discussion on the performance and future of deep learning predictive models in external radiotherapy.

OBJECTIVE: STereotactic Arrhythmia Radioablation (STAR) showed promising results in patients with refractory ventricular tachycardia (VT). However, clinical data is scarce and heterogeneous. The STOPSTORM.eu consortium was established to investigate and harmonize STAR in Europe. The primary goal of this benchmark study was to investigate current treatment planning practice within the STOPSTORM project as a baseline for future harmonization.
METHODS: Planning target volumes (PTV) overlapping extra-cardiac organs-at-risk and/or cardiac substructures were generated for three STAR cases. Participating centers were asked to create single fraction treatment plans with 25 Gy dose prescription based on in-house clinical practice. All treatment plans were reviewed by an expert panel and quantitative crowd knowledge-based analysis was performed with independent software using descriptive statistics for ICRU report 91 relevant parameters and crowd dose-volume-histograms. Thereafter, treatment planning consensus statements were established using a dual-stage voting process.
RESULTS: Twenty centers submitted 67 treatment plans for this study. In most plans (75%) Intensity Modulated Arc Therapy (IMAT) with 6 MV flattening-filter-free beams was used. Dose prescription was mainly based on PTV D95% (49%) or D96-100% (19%). Many participants preferred to spare close extra-cardiac organs-at-risk (75%) and cardiac substructures (50%) by PTV coverage reduction. PTV D0.035cm3 ranged 25.5-34.6 Gy, demonstrating a large variety of dose inhomogeneity. Estimated treatment times without motion compensation or setup ranged 2-80 minutes. For the consensus statements, strong agreement was reached for beam technique planning, dose calculation, prescription methods and trade-offs between target and extra-cardiac critical structures. No agreement was reached on cardiac substructure dose limitations and on desired dose inhomogeneity in the target.
CONCLUSIONS: This STOPSTORM multi-center treatment planning benchmark study showed strong agreement on several aspects of STAR treatment planning, but also revealed disagreement on others. To standardize and harmonize STAR in the future, consensus statements were established, however clinical data is urgently needed for actionable guidelines for treatment planning.

Objective.This study simulated the potential of gold nanoparticles (GNPs) to improve the effectiveness of radiation therapy in pancreatic cancer cases. The purpose of this study was to assess the impact of GNPs on tumor control probability (TCP) and normal tissue complication probability (NTCP) in pancreatic cancer cases undergoing radiation therapy. The work aimed to compare treatment plans generated with a novel 2.5 MV beam using GNPs to conventional 6 MV plans and evaluate the dose-volume histogram (DVH), TCP, and NTCP.Approach.Treatment planning for five pancreatic computed tomography (CT) images was performed using the open-source MATLAB-based treatment planning program matRad. MATLAB codes were developed to calculate the relative biological effectiveness (RBE) of GNPs and apply the corresponding dose and RBE values to each voxel. TCP and NTCP were calculated based on the applied RBE values.Main results.Adding GNPs to the 2.5 MV treatment plan resulted in a significant increase in TCP, from around 59% to 93.5%, indicating that the inclusion of GNPs improved the effectiveness of the radiation treatment. The range in NTCP without GNPs was relatively larger compared to that with GNPs.Significance.The results indicated that the addition of GNPs to a 2.5 MV plan can increase TCP while maintaining a relatively low NTCP value (<1%). The use of GNPs may also reduce NTCP values by decreasing the dose to normal tissues while maintaining the same prescribed dose to the tumor. Hence, the addition of GNPs can improve the balance between TCP and NTCP.

BACKGROUND: 3D printing holds great potential of improving examination, diagnosis and treatment planning as well as interprofessional communication in the field of gynecological oncology. In the current manuscript we evaluated five individualized, patient-specific models of cervical cancer FIGO Stage I-III, created with 3D printing, concerning their value for translational oncology.
METHODS: Magnetic resonance imaging (MRI) of the pelvis was performed on a 3.0 Tesla MRI, including a T2-weighted isotropic 3D sequence. The MRI images were segmented and transferred to virtual 3D models via a custom-built 3D-model generation pipeline and printed by material extrusion. The 3D models were evaluated by all medical specialties involved in patient care of cervical cancer, namely surgeons, radiologists, pathologists and radiation oncologists. Information was obtained from evaluated profession-specific questionnaires which were filled out after inspecting all five models. The questionnaires included multiple-select questions, questions based on Likert scales (1 = \"strongly disagree \" or \"not at all useful \" up to 5 = \"strongly agree \" or \"extremely useful \") and dichotomous questions (\"Yes\" or \"No\").
RESULTS: Surgeons rated the models as useful during surgery (4.0 out of 5) and for patient communication (4.7 out of 5). Furthermore, they believed that the models had the potential to revise the patients\' treatment plan (3.7 out of 5). Pathologists evaluated with mean ratings of 3.0 out of 5 for the usefulness of the models in diagnostic reporting and macroscopic evaluation. Radiologist acknowledged the possibility of providing additional information compared to imaging alone (3.7 out of 5). Radiation oncologists strongly supported the concept by rating the models highly for understanding patient-specific pathological characteristics (4.3 out of 5), assisting interprofessional communication (mean 4.3 out of 5) and communication with patients (4.7 out of 5). They also found the models useful for improving radiotherapy treatment planning (4.3 out of 5).
CONCLUSIONS: The study revealed that the 3D printed models were generally well-received by all medical disciplines, with radiation oncologists showing particularly strong support. Addressing the concerns and tailoring the use of 3D models to the specific needs of each medical speciality will be essential for realizing their full potential in clinical practice.

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In 2019, the male adjudicated youth (AY) population in correctional facilities was 33% White and 67% minority. Yet, the distribution among male AY charged with sexual offenses (AYSOs) was 55% White and 45% minority, highlighting the lack of disproportionate minority contact within the AYSO population. Little research on AYSOs has focused explicitly on exploring racial differences within this population. Using secondary data from 720 AY 11-18 years of age, the goal of this exploratory study was to identify differences in length of detention, presence of clinical syndromes, attachment patterns, and childhood trauma experiences among a sample of AYSOs and AYs by race category. Although few racial differences were identified among AYSOs, study results overwhelmingly highlighted differences between AYSOs and AYs.

UNASSIGNED: Magnetic resonance imaging (MRI) is increasingly used in treatment preparation of ocular proton therapy, but its spatial accuracy might be limited by geometric distortions due to susceptibility artefacts. A correct geometry of the MR images is paramount since it defines where the dose will be delivered. In this study, we assessed the geometrical accuracy of ocular MRI.
UNASSIGNED: A dedicated ocular 3 T MRI protocol, with localized shimming and increased gradients, was compared to computed tomography (CT) and X-ray images in a phantom and in 15 uveal melanoma patients. The MRI protocol contained three-dimensional T2-weighted and T1-weighted sequences with an isotropic reconstruction resolution of 0.3-0.4 mm. Tantalum clips were identified by three observers and clip-clip distances were compared between T2-weighted and T1-weighted MRI, CT and X-ray images for the phantom and between MRI and X-ray images for the patients.
UNASSIGNED: Interobserver variability was below 0.35 mm for the phantom and 0.30(T1)/0.61(T2) mm in patients. Mean absolute differences between MRI and reference were below 0.27 ± 0.16 mm and 0.32 ± 0.23 mm for the phantom and in patients, respectively. In patients, clip-clip distances were slightly larger on MRI than on X-ray images (mean difference T1: 0.11 ± 0.38 mm, T2: 0.10 ± 0.44 mm). Differences did not increase at larger distances and did not correlate to interobserver variability.
UNASSIGNED: A dedicated ocular MRI protocol can produce images of the eye with a geometrical accuracy below half the MRI acquisition voxel (<0.4 mm). Therefore, these images can be used for ocular proton therapy planning, both in the current model-based workflow and in proposed three-dimensional MR-based workflows.

This paper examines the integration of artificial intelligence (AI) in radiotherapy for cancer treatment. The importance of radiotherapy in cancer management and its time-intensive planning process make AI adoption appealing especially with the escalating demand for radiotherapy. This review highlights the efficacy of AI across medical domains, where it surpasses human capabilities in areas such as cardiology and dermatology. Focusing on radiotherapy, the paper details AI\'s applications in target segmentation, dose optimization, and outcome prediction. It discusses adaptive radiotherapy\'s benefits and AI\'s potential to enhance patient outcomes with much improved treatment accuracy. The paper explores ethical concerns, including data privacy and bias, stressing the need for robust guidelines. Educating healthcare professionals and patients about AI\'s role is crucial as it acknowledges potential job-role changes and concerns about patients\' trust in the use of AI. Overall, the integration of AI in radiotherapy holds transformative potential in streamlining processes, improving outcomes, and reducing costs. AI\'s potential to reduce healthcare costs underscores its significance with impactful change globally. However, successful implementation hinges on addressing ethical and logistical challenges and fostering collaboration among healthcare professionals and patient population data sets for its optimal utilization. Rigorous education, collaborative efforts, and global data sharing will be the compass guiding its\' success in radiotherapy and healthcare.