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In 2022, the radiation therapist committee of the Association française du personnel paramédical d'electroradiologie médicale (AFPPE, French association of paramedical electroradiology technicians), the Société française de radiothérapie oncologique (SFRO, French society of radiation oncology) and the Syndicat national des radiothérapeutes oncologue (SNRO, national syndicate of radiation oncologists) have been committed to working on the development of advanced practice roles. The objective of this article is to report the activities that should be in the scope of radiation therapists advanced practice and describe the competences required for these activities. This work was carried out by six radiation therapists, six radiation oncologists and one medical physicist representatives of the French national societies for each professional group. First, a basic list of activities was established and then competences were identified for groups of activities. In total, the list includes five core competences, nine competences and nine groups of activities that can be divided into the four pillars of advanced practice. The nine groups of activities can be presented in seven different dimensions including patient care and support, treatment planning, treatment imaging and delivery, management and consultancy, quality and risk management, research and innovation, education and training. The French advanced practice competences framework was developed with a multidisciplinary group to move forward the project of a master degree in advanced practice in radiation therapy in France. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusion Safety and quality improvement are essential to clinical practice in radiation therapy as planning and treatment increase in complexity and sophistication. An incident learning system (ILS) is a safety and quality improvement tool that can aid risk mitigation to improve patient safety and quality of care. The aim of this study was to quantify the impact of implementing a new e‐ILS, Learning In Radiation ONcology (LIRON), on reporting and safety culture within a local health district (LHD).The ILS (LIRON) was implemented in 2020 with the intent of tracking actual incidents, near misses and procedural non‐compliances for analysis of root causes and contributing factors. A survey was conducted after 12 months of LIRON use, and distributed to radiation oncologists, radiation therapists and radiation oncology medical physicists within the LHD. Results were compared with the responses to a pre‐ILS implementation survey, to review changes in staff perceptions of safety culture, barriers to reporting and ILS understanding.Survey response rates were similar at baseline and at the 12‐month follow‐up, 64% and 63%, respectively. Findings showed increased ILS participation (49–71%), increased perception of no barriers to reporting (34–43%) and increased encouragement to report (37–43%). Greater confidence in the department's ability to learn from the ILS was evident (24–46%).Initial findings of LIRON implementation show positive impact but warrant further long‐term review for greater understanding of its impact on staff perceptions, safety culture and improving departmental processes. [ABSTRACT FROM AUTHOR]

The present study aimed to summarize and report data on errors related to treatment planning, which were collected by medical physicists. The following analyses were performed based on the 10-year error report data: (1) listing of high-risk errors that occurred and (2) the relationship between the number of treatments and error rates, (3) usefulness of the Automated Plan Checking System (APCS) with the Eclipse Scripting Application Programming Interface and (4) the relationship between human factors and error rates. Differences in error rates were observed before and after the use of APCS. APCS reduced the error rate by ~1% for high-risk errors and 3% for low-risk errors. The number of treatments was negatively correlated with error rates. Therefore, we examined the relationship between the workload of medical physicists and error occurrence and revealed that a very large workload may contribute to overlooking errors. Meanwhile, an increase in the number of medical physicists may lead to the detection of more errors. The number of errors was correlated with the number of physicians with less clinical experience; the error rates were higher when there were more physicians with less experience. This is likely due to the lack of training among clinically inexperienced physicians. An environment to provide adequate training is important, as inexperience in clinical practice can easily and directly lead to the occurrence of errors. In any environment, the need for additional plan checkers is an essential factor for eliminating errors. [ABSTRACT FROM AUTHOR]

Multi-criteria optimization (MCO) function has been available on commercial radiotherapy (RT) treatment planning systems to improve plan quality; however, no study has compared Eclipse and RayStation MCO functions for prostate RT planning. The purpose of this study was to compare prostate RT MCO plan qualities in terms of discrepancies between Pareto optimal and final deliverable plans, and dosimetric impact of final deliverable plans. In total, 25 computed tomography datasets of prostate cancer patients were used for Eclipse (version 16.1) and RayStation (version 12A) MCO-based plannings with doses received by 98% of planning target volume having 76 Gy prescription (PTV76D98%) and 50% of rectum (rectum D50%) selected as trade-off criteria. Pareto optimal and final deliverable plan discrepancies were determined based on PTV76D98% and rectum D50% percentage differences. Their final deliverable plans were compared in terms of doses received by PTV76 and other structures including rectum, and PTV76 homogeneity index (HI) and conformity index (CI), using a t-test. Both systems showed discrepancies between Pareto optimal and final deliverable plans (Eclipse: −0.89% (PTV76D98%) and −2.49% (Rectum D50%); RayStation: 3.56% (PTV76D98%) and −1.96% (Rectum D50%)). Statistically significantly different average values of PTV76D98%,HI and CI, and mean dose received by rectum (Eclipse: 76.07 Gy, 0.06, 1.05 and 39.36 Gy; RayStation: 70.43 Gy, 0.11, 0.87 and 51.65 Gy) are noted, respectively (p < 0.001). Eclipse MCO-based prostate RT plan quality appears better than that of RayStation. [ABSTRACT FROM AUTHOR]

Head and neck (HN) cancers pose a difficult problem in the planning of intensity-modulated radiation therapy (IMRT) treatment. The primary tumor can be large and asymmetrical, and multiple organs at risk (OARs) with varying dose-sparing goals lie close to the target volume. Currently, there is no systematic way of automating the generation of IMRT plans, and the manual options face planning quality and long planning time challenges. In this article, we present a reinforcement learning (RL) model for the purposes of providing automated treatment planning to reduce clinical workflow time as well as providing a better starting point for human planners to modify and build upon. Several models with progressing complexity are presented, including the relevant plan dosimetry analysis and model interpretations of the resulting strategies learned by the auto-planning agent. Models were trained on a set of 40 patients and validated on a set of 20 patients. The presented models are shown to be consistent with the requirements of an RL model to be underpinned by a Markov decision process (MDP). In-depth interpretability of the models is presented by examination of the decision space using action hyperplanes. The auto-planning agent was able to generate plans with superior reduction in the mean dose of the left and right parotid glands by approximately 7 Gy ± 2.5 Gy (p < 0.01) over a starting, static template plan with only pre-defined general prescription information. RL plans were comparable to a human expert's clinical plans for the primary (44 Gy), boost (26 Gy) , and the summed plans (70 Gy) with p-values of 0.43, 0.72, and 0.67, respectively, for the dosimetric endpoints and uniform target coverage normalization. The RL planning agent was able to produce the plans used in validation in an average of 13.58 min, with a minimum and a maximum planning time of 2.27 and 44.82 min, respectively. [ABSTRACT FROM AUTHOR]

For patients with newly diagnosed glioblastoma, the current standard-of-care includes maximal safe resection, followed by concurrent chemoradiotherapy and adjuvant temozolomide, with tumor treating fields. Traditionally, diagnostic imaging is performed pre- and post-resection, without additional dedicated longitudinal imaging to evaluate tumor volumes or other treatment-related changes. However, the recent introduction of MR-guided radiotherapy using the ViewRay MRIdian A3i system includes a dedicated BrainTx package to facilitate the treatment of intracranial tumors and provides daily MR images. We present the first reported case of a glioblastoma imaged and treated using this workflow. In this case, a 67-year-old woman underwent adjuvant chemoradiotherapy after gross total resection of a left frontal glioblastoma. The radiotherapy treatment plan consisted of a traditional two-phase design (46 Gy followed by a sequential boost to a total dose of 60 Gy at 2 Gy/fraction). The treatment planning process, institutional workflow, treatment imaging, treatment timelines, and target volume changes visualized during treatment are presented. This case example using our institutional A3i system workflow successfully allows for imaging and treatment of primary brain tumors and has the potential for margin reduction, detection of early disease progression, or to detect the need for dose adaptation due to interfraction tumor volume changes. [ABSTRACT FROM AUTHOR]

Background: Prostate-Specific Membrane Antigen (PSMA) PET/CT and multiparametric MRI (mpMRI) are well-established modalities for identifying intra-prostatic lesions (IPLs) in localised prostate cancer. This study aimed to investigate the use of PSMA PET/CT and mpMRI for biologically targeted radiation therapy treatment planning by: (1) analysing the relationship between imaging parameters at a voxel-wise level and (2) assessing the performance of radiomic-based machine learning models to predict tumour location and grade. Methods: PSMA PET/CT and mpMRI data from 19 prostate cancer patients were co-registered with whole-mount histopathology using an established registration framework. Apparent Diffusion Coefficient (ADC) maps were computed from DWI and semi-quantitative and quantitative parameters from DCE MRI. Voxel-wise correlation analysis was conducted between mpMRI parameters and PET Standardised Uptake Value (SUV) for all tumour voxels. Classification models were built using radiomic and clinical features to predict IPLs at a voxel level and then classified further into high-grade or low-grade voxels. Results: Perfusion parameters from DCE MRI were more highly correlated with PET SUV than ADC or T2w. IPLs were best detected with a Random Forest Classifier using radiomic features from PET and mpMRI rather than either modality alone (sensitivity, specificity and area under the curve of 0.842, 0.804 and 0.890, respectively). The tumour grading model had an overall accuracy ranging from 0.671 to 0.992. Conclusions: Machine learning classifiers using radiomic features from PSMA PET and mpMRI show promise for predicting IPLs and differentiating between high-grade and low-grade disease, which could be used to inform biologically targeted radiation therapy planning. [ABSTRACT FROM AUTHOR]

Realizing the potential of user‐developed automation software interacting with a treatment planning system (TPS) requires rigorous testing to ensure patient safety and data integrity. We developed an automated test platform to allow comparison of the treatment planning database before and after the execution of a write‐enabled script interacting with a commercial TPS (Eclipse, Varian Medical Systems, Palo Alto, CA) using the vendor‐provided Eclipse Scripting Application Programming Interface (ESAPI). The C#‐application known as Write‐Enable Script Testing Engine (WESTE) serializes the treatment planning objects (Patient, Structure Set, PlanSetup) accessible through ESAPI, and then compares the serialization acquired before and after the execution of the script being tested, documenting identified differences to highlight the changes made to the treatment planning data. The first two uses of WESTE demonstrated that the testing platform could acquire and analyze the data quickly (<4 s per test case) and facilitate the clinical implementation of write‐enabled scripts. [ABSTRACT FROM AUTHOR]

Purpose: Radiotherapy treatment planning is a complex process with multiple, dependent steps involving an interdisciplinary patient care team. Effective communication and real‐time tracking of resources and care path activities are key for clinical efficiency and patient safety. Materials and Methods: We designed and implemented a secure, interactive web‐based dashboard for patient care path, clinical workflow, and resource utilization management. The dashboard enables visualization of resource utilization and tracks progress in a patient's care path from the time of acquisition of the planning CT to the time of treatment in real‐time. It integrates with the departmental electronic medical records (EMR) system without the creation and maintenance of a separate database or duplication of work by clinical staff. Performance measures of workflow were calculated. Results: The dashboard implements a standardized clinical workflow and dynamically consolidates real‐time information queried from multiple tables in the EMR database over the following views: (1) CT Sims summarizes patient appointment information on the CT simulator and patient load; (2) Linac Sims summarizes patient appointment times, setup history, and notes, and patient load; (3) Task Status lists the clinical tasks associated with a treatment plan, their due date, status and ownership, and patient appointment details; (4) Documents provides the status of all documents in the patients' charts; and (5) Diagnoses and Interventions summarizes prescription information, imaging instructions and whether the plan was approved for treatment. Real‐time assessment and quantification of progress and delays in a patient's treatment start were achieved. Conclusions: This study indicates it is feasible to develop and implement a dashboard, tailored to the needs of an interdisciplinary team, which derives and integrates information from the EMR database for real‐time analysis and display of resource utilization and clinical workflow in radiation oncology. The framework developed facilitates informed, data‐driven decisions on clinical workflow management as we seek to optimize clinical efficiency and patient safety. [ABSTRACT FROM AUTHOR]

Introduction: Brain tumors pose a significant challenge in the field of oncology, and radiation therapy plays a crucial role in their treatment. Accurate modeling of radiation dose distribution is essential for optimizing treatment plans and improving patient outcomes. In this study, we utilized the Monte Carlo N - Particle (MCNP) code to investigate the effect of radiation dose on the treatment of brain tumors. Methods: We developed a comprehensive MCNP model to simulate the interaction of radiation with brain tissue. The model incorporated anatomical details, such as tissue heterogeneity and tumor characteristics, to accurately represent the complex tumor microenvironment. We employed various radiation sources, including photon and electron beams, to deliver therapeutic doses to the tumor region. The treatment plans were optimized based on prescribed dose, tumor size, and critical organ constraints. Results: The MCNP simulations provided detailed information on the radiation dose distribution within the brain tumor and surrounding healthy tissues. We analyzed the impact of different radiation doses on tumor control probability (TCP) and normal tissue complication probability (NTCP). Our results demonstrated a dose - dependent relationship between radiation dose and tumor control, with higher doses resulting in increased TCP. However, escalating the radiation dose beyond a certain threshold led to a higher risk of normal tissue complications. Conclusions: The findings of this study highlight the importance of accurate radiation dose planning in brain tumor treatment. The utilization of MCNP code allowed for precise modeling of radiation interactions and provided insights into the dose - response relationship. Optimizing treatment plans to maximize tumor control while minimizing normal tissue toxicity is crucial for improving patient outcomes. Further investigations should focus on refining treatment protocols and exploring advanced radiation delivery techniques to enhance therapeutic efficacy. [ABSTRACT FROM AUTHOR]

Purpose: Deep learning–based knowledge‐based planning (KBP) methods have been introduced for radiotherapy dose distribution prediction to reduce the planning time and maintain consistent high‐quality plans. This paper presents a novel KBP model using an attention‐gating mechanism and a three‐dimensional (3D) U‐Net for intensity‐modulated radiation therapy (IMRT) 3D dose distribution prediction in head‐and‐neck cancer. Methods: A total of 340 head‐and‐neck cancer plans, representing the OpenKBP—2020 AAPM Grand Challenge data set, were used in this study. All patients were treated with the IMRT technique and a dose prescription of 70 Gy. The data set was randomly divided into 64%/16%/20% as training/validation/testing cohorts. An attention‐gated 3D U‐Net architecture model was developed to predict full 3D dose distribution. The developed model was trained using the mean‐squared error loss function, Adam optimization algorithm, a learning rate of 0.001, 120 epochs, and batch size of 4. In addition, a baseline U‐Net model was also similarly trained for comparison. The model performance was evaluated on the testing data set by comparing the generated dose distributions against the ground‐truth dose distributions using dose statistics and clinical dosimetric indices. Its performance was also compared to the baseline model and the reported results of other deep learning‐based dose prediction models. Results: The proposed attention‐gated 3D U‐Net model showed high capability in accurately predicting 3D dose distributions that closely replicated the ground‐truth dose distributions of 68 plans in the test set. The average value of the mean absolute dose error was 2.972 ± 1.220 Gy (vs. 2.920 ± 1.476 Gy for a baseline U‐Net) in the brainstem, 4.243 ± 1.791 Gy (vs. 4.530 ± 2.295 Gy for a baseline U‐Net) in the left parotid, 4.622 ± 1.975 Gy (vs. 4.223 ± 1.816 Gy for a baseline U‐Net) in the right parotid, 3.346 ± 1.198 Gy (vs. 2.958 ± 0.888 Gy for a baseline U‐Net) in the spinal cord, 6.582 ± 3.748 Gy (vs. 5.114 ± 2.098 Gy for a baseline U‐Net) in the esophagus, 4.756 ± 1.560 Gy (vs. 4.992 ± 2.030 Gy for a baseline U‐Net) in the mandible, 4.501 ± 1.784 Gy (vs. 4.925 ± 2.347 Gy for a baseline U‐Net) in the larynx, 2.494 ± 0.953 Gy (vs. 2.648 ± 1.247 Gy for a baseline U‐Net) in the PTV_70, and 2.432 ± 2.272 Gy (vs. 2.811 ± 2.896 Gy for a baseline U‐Net) in the body contour. The average difference in predicting the D99 value for the targets (PTV_70, PTV_63, and PTV_56) was 2.50 ± 1.77 Gy. For the organs at risk, the average difference in predicting the Dmax${D_{max}}$ (brainstem, spinal cord, and mandible) and Dmean${D_{mean}}$ (left parotid, right parotid, esophagus, and larynx) values was 1.43 ± 1.01 and 2.44 ± 1.73 Gy, respectively. The average value of the homogeneity index was 7.99 ± 1.45 for the predicted plans versus 5.74 ± 2.95 for the ground‐truth plans, whereas the average value of the conformity index was 0.63 ± 0.17 for the predicted plans versus 0.89 ± 0.19 for the ground‐truth plans. The proposed model needs less than 5 s to predict a full 3D dose distribution of 64 × 64 × 64 voxels for a new patient that is sufficient for real‐time applications. Conclusions: The attention‐gated 3D U‐Net model demonstrated a capability in predicting accurate 3D dose distributions for head‐and‐neck IMRT plans with consistent quality. The prediction performance of the proposed model was overall superior to a baseline standard U‐Net model, and it was also competitive to the performance of the best state‐of‐the‐art dose prediction method reported in the literature. The proposed model could be used to obtain dose distributions for decision‐making before planning, quality assurance of planning, and guiding‐automated planning for improved plan consistency, quality, and planning efficiency. [ABSTRACT FROM AUTHOR]

Purpose: To reduce the likelihood of errors in organ delineations used for radiotherapy treatment planning, a knowledge‐based quality control (KBQC) system, which discriminates between valid and anomalous delineations is developed. Method and Materials: The KBQC is comprised of a group‐wise inference system and anomaly detection modules trained using historical priors from 296 locally advanced lung and prostate cancer patient computational tomographies (CTs). The inference system discriminates different organs based on shape, relational, and intensity features. For a given delineated image set, the inference system solves a combinatorial optimization problem that results in an organ group whose relational features follow those of the training set considering the posterior probabilities obtained from support vector machine (SVM), discriminant subspace ensemble (DSE), and artificial neural network (ANN) classifiers. These classifiers are trained on nonrelational features with a 10‐fold cross‐validation scheme. The anomaly detection module is a bank of ANN autoencoders, each corresponding with an organ, trained on nonrelational features. A heuristic rule detects anomalous organs that exceed predefined organ‐specific tolerances for the feature reconstruction error and the classifier's posterior probabilities. Independent data sets with anomalous delineations were used to test the overall performance of the KBQC system. The anomalous delineations were manually manipulated, computer‐generated, or propagated based on a transformation obtained by imperfect registrations. Both peer‐review–based scoring system and shape similarity coefficient (DSC) were used to label regions of interest (ROIs) as normal or anomalous in two independent test cohorts. Results: The accuracy of the classifiers was ≥$\ge$99.8%, and the minimum per‐class F1‐scores were 0.99, 0.99, and 0.98 for SVM, DSE, and ANN, respectively. The group‐wise inference system reduced the miss‐classification likelihood for the test data set with anomalous delineations compared to each individual classifier and a fused classifier that used the average posterior probability of all classifiers. For 15 independent locally advanced lung patients, the system detected >$>$79% of the anomalous ROIs. For 1320 auto‐segmented abdominopelvic organs, the anomaly detection system identified anomalous delineations, which also had low Dice similarity coefficient values with respect to manually delineated organs in the training data set. Conclusion: The KBQC system detected anomalous delineations with superior accuracy compared to classification methods that judge only based on posterior probabilities. [ABSTRACT FROM AUTHOR]

Gliomas are the most frequent primary brain tumour. The proximity of organs at risk, the infiltrating nature, and the radioresistance of gliomas have to be taken into account in the choice of prescribed dose and technique of radiotherapy. The management of glioma patients is based on clinical factors (age, KPS) and tumour characteristics (histology, molecular biology, tumour location), and strongly depends on available and associated treatments, such as surgery, radiation therapy, and chemotherapy. The knowledge of molecular biomarkers is currently essential, they are increasingly evolving as additional factors that facilitate diagnostics and therapeutic decision-making. We present the update of the recommendations of the French society for radiation oncology on the indications and the technical procedures for performing radiation therapy in patients with gliomas. [ABSTRACT FROM AUTHOR]

Background: The evaluation of automatic segmentation algorithms is commonly performed using geometric metrics. An analysis based on dosimetric parameters might be more relevant in clinical practice but is often lacking in the literature. The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in radiation therapy (RT) for prostate cancer patients.Methods: A database of 69 computed tomography images with prostate, bladder, and rectum delineations was used for single-label 3D U-Net training with dice similarity coefficient (DSC)-based loss. Volumetric modulated arc therapy (VMAT) plans have been generated for both manual and automatic segmentations with the same optimization settings. These were chosen to give consistent plans when applying perturbations to the manual segmentations. Contours were evaluated in terms of DSC, average and 95% Hausdorff distance (HD). Dose distributions were evaluated with the manual segmentation as reference using dose volume histogram (DVH) parameters and a 3%/3 mm gamma-criterion with 10% dose cut-off. A Pearson correlation coefficient between DSC and dosimetric metrics, i.e. gamma index and DVH parameters, has been calculated.Results: 3D U-Net-based segmentation achieved a DSC of 0.87 (0.03) for prostate, 0.97 (0.01) for bladder and 0.89 (0.04) for rectum. The mean and 95% HD were below 1.6 (0.4) and below 5 (4) mm, respectively. The DVH parameters, V[Formula: see text] for the bladder and V[Formula: see text] for the rectum, showed agreement between dose distributions within [Formula: see text] and [Formula: see text], respectively. The D[Formula: see text] and V[Formula: see text], for prostate and its 3 mm expansion (surrogate clinical target volume) showed agreement with the reference dose distribution within 2% and 3 Gy with the exception of one case. The average gamma pass-rate was 85%. The comparison between geometric and dosimetric metrics showed no strong statistically significant correlation.Conclusions: The 3D U-Net developed for this work achieved state-of-the-art geometrical performance. Analysis based on clinically relevant DVH parameters of VMAT plans demonstrated neither excessive dose increase to OARs nor substantial under/over-dosage of the target in all but one case. Yet the gamma analysis indicated several cases with low pass rates. The study highlighted the importance of adding dosimetric analysis to the standard geometric evaluation. [ABSTRACT FROM AUTHOR]

Purpose: Radiation therapy treatment planning is a time‐consuming and iterative manual process. Consequently, plan quality varies greatly between and within institutions. Artificial intelligence shows great promise in improving plan quality and reducing planning times. This technical note describes our participation in the American Association of Physicists in Medicine Open Knowledge‐Based Planning Challenge (OpenKBP), a competition to accurately predict radiation therapy dose distributions. Methods: A three‐dimensional (3D) densely connected U‐Net with dilated convolutions was developed to predict 3D dose distributions given contoured CT images of head and neck patients as input. While traditional augmentation techniques such as rotations and translations were explored, it was found that training on random patches alone resulted in the greatest model performance. A custom‐weighted mean squared error loss function was employed. Finally, an ensemble of best‐performing networks was used to generate the final challenge predictions. Results: Our team (SuperPod) placed second in the dose stream of the OpenKBP challenge. The average mean absolute difference between the predicted and clinical dose distributions of the testing dataset was 2.56 Gy. On average, the predicted normalized target DVH metrics were within 3% of the clinical plans, and the predicted organ at risk DVH metrics were within 2 Gy of the clinical plans. Conclusions: The developed 3D dense dilated U‐Net architecture can accurately predict 3D radiotherapy dose distributions and can be used as part of a fully automated radiation therapy planning pipeline. [ABSTRACT FROM AUTHOR]

Featured Application: Radiation therapy treatment planning. Radiation therapy (RT) is a constantly evolving therapeutic technique; improvements are continuously being introduced for both methodological and practical aspects. Among the features that have undergone a huge evolution in recent decades, dose calculation algorithms are still rapidly changing. This process is propelled by the awareness that the agreement between the delivered and calculated doses is of paramount relevance in RT, since it could largely affect clinical outcomes. The aim of this work is to provide an overall picture of the main dose calculation algorithms currently used in RT, summarizing their underlying physical models and mathematical bases, and highlighting their strengths and weaknesses, referring to the most recent studies on algorithm comparisons. This handy guide is meant to provide a clear and concise overview of the topic, which will prove useful in helping clinical medical physicists to perform their responsibilities more effectively and efficiently, increasing patient benefits and improving the overall quality of the management of radiation treatment. [ABSTRACT FROM AUTHOR]

• The value of Cherenkov imaging at the field junction was completed to assess the accuracy of identifying potential incidents with this new imaging technology. • The analysis technique invented here, provides a method for automated detection and quantification of match regions. • The region of field match could be isolated using automated techniques. • The average distance offset from Pt 1 showed a 25.0% increase in dose at the match line, and for Pt 2, a 23.6% decrease in dose. In mono-isocentric radiation therapy treatment plans designed to treat the whole breast and supraclavicular lymph nodes, the fields meet at isocenter, forming the match line. Insufficient coverage at the match line can lead to recurrence, and overlap over weeks of treatment can lead to increased risk of healthy tissue toxicity. Cherenkov imaging was used to assess the accuracy of delivery at the match line and identify potential incidents during patient treatments. A controlled calibration was constructed from the deconvolved Cherenkov images from the delivery of a modified patient treatment plan to an anthropomorphic phantom with introduced separation and overlap. The trend from this calibration was then used to evaluate the field match line for accuracy and inter-fraction consistency for two patients. The intersection point between matching field profiles was directly correlated to the distance (gap/overlap) between the fields (anthropomorphic phantom R 2 = 0.994 "breath hold" and R 2 = 0.990 "free breathing"). The profile intersection points from two patients' imaging sessions yielded an average of +1.40 mm offset (overlap) and −1.32 mm offset (gap), thereby introducing roughly a 25.0% over-dose and a −23.6% under-dose (R 2 = 0.994). This study shows that field match regions can be detected and quantified by taking deconvolved Cherenkov images and using their product image to create steep intensity gradients, causing match lines to stand out. These regions can then be quantitatively translated into a dose consequence. This approach offers a high sensitivity detection method which can quantify match line variability and errors in vivo. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to evaluate the overall accuracy of the beam commissioning criteria of Monaco treatment planning system (TPS) based on the American Association of Physicists in Medicine (AAPM) Task Group Report 119 (TG-119). Methods: The work was performed using 6 MV energy LINAC with a variable dose rate of 600 MU/min. The AAPM TG-119 intensity-modulated radiation therapy (IMRT) commissioning tests are composed of two preliminary tests and four clinical test cases. The clinical tests consisted of mock prostate, mock head and neck, C-shaped target, and multitarget. Matrixx phantom was used for evaluating the IMRT plans. The dose prescription for each fraction was 200 cGy in mock prostate, mock head and neck, C-shaped target, and multitarget. Dose distributions were analyzed using gamma criteria of 3% and 2% dose difference (DD) and 3 and 2 distance to agreement. Results: In all test cases, the gamma criteria for 2%/2mm and 3%/3mm were found to be 94% and 98%, respectively. Results showed that the average gamma criteria result was in the range of 98% to 96% (3%/3, 2%/2) overall test cases, for getting better results, we did post modeling evaluation and tune some geometrical value of TPS with this method our results improve with range of 99% to 98% (3%/3, 2%/2). Conclusions: Findings were favorable and in some tests were comparable with the other studies. The dose point values were within the mean values of the range reported by TG-119. Overall, the Monaco TPS is needed to apply IMRT technique in radiation therapy centers. [ABSTRACT FROM AUTHOR]

Background: Radiotherapy for head and neck cancer may cause various oral sequelae, such as radiation-induced mucositis. To protect healthy tissue from irradiation, intraoral devices can be used. Current tissue retraction devices (TRDs) have to be either individually manufactured at considerable cost and time expenditure or they are limited in their variability. In this context, a 3D-printed, tooth-borne TRD might further facilitate clinical use. Methods: A novel approach for the manufacturing of TRDs is described and its clinical application is analysed retrospectively. The devices were virtually designed for fabrication by 3D-printing technology, enabling—in only a single printing design—caudal or bi-lateral tongue displacement, as well as stabilization of a tongue-out position. For a total of 10 patients undergoing radiotherapy of head and neck tumors, the devices were individually adapted after pre-fabrication. Technical and clinical feasibility was assessed along with patient adherence. Tissue spacing was calculated by volumetric analysis of tongue retraction. In one exemplary case, radiotherapy treatment plans before and after tissue displacement were generated and compared. The reproducibility of maxillomandibular relation at device re-positioning was quantified by repeated intraoral optical scanning in a voluntary participant. Results: 3D-printing was useful for the simplification of TRD manufacture, resulting in a total patient treatment time of less than 30 min. The devices were tolerated well by all tested patients over the entire radiation treatment period. No technical complications occurred with the devices. The TRDs caused an effective spacing of the healthy adjacent tissue, e.g., the tongue. Position changes of maxillomandibular relation were limited to a mean value of 98.1 µm ± 29.4 µm root mean square deviation between initial reference and follow-up positions. Conclusions: The presented method allows a resource-efficient fabrication of individualized, tooth-bourne TRDs. A high reproducibility of maxillomandibular relation was found and the first clinical experiences underline the high potential of such devices for radiotherapy in the head and neck area. [ABSTRACT FROM AUTHOR]

Radiation therapy (RT) is one of the main modalities of cancer treatment worldwide with computed tomography (CT), as the most commonly used imaging method for treatment planning system (TPS). Image reconstruction errors may greatly affect all the radiation therapy planning process, such as target delineation, dose calculation and delivery, particularly with particle therapy. Metallic implants, such as hip and spinal implants, and dental filling significantly deteriorate image quality. These hardware structures are often very complex in geometry leading to geometric complex artefacts in the clinical target volume (CTV) area, rendering the delineation of CTV challenging. In our review, we focus on the methods to overcome artefact consequences on CTV delineation: 1– medical approaches anticipating issues associated with imaging artefacts during preoperative multidisciplinary discussions while following standard recommendations; 2– common metal artefact reduction (MAR) methods such as manually override artefact regions, ballistics avoiding beam paths through implanted materials, megavoltage-CT (MVCT); 3– prospects with radiolucent implants, MAR algorithms and various methods of dual energy computed tomography (DECT). Despite substantial and broad evidence for their benefits, there is still no universal solution for cases involving implanted metallic devices. There is still a high need for research efforts to adapt technologies to our issue: "how do I accurately delineate the ideal CTV in a metal artefact area?" [ABSTRACT FROM AUTHOR]