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Treatment planning parameters in radiotherapy are key elements that dictate the success of treatment outcome. While some parameters are commonly evaluated irrespective of cancer type, others are site-dependent and strongly patient specific. Given the critical influence of planning parameters on personalized therapy, the aim of this study was to evaluate the correlations between the dosimetric indices (conformity, homogeneity and mismatch indices) related to tumor coverage and the patient-specific parameters which encompass parameters pertaining to organs at risk (widths and lengths of heart and ipsilateral lung included in treatment fields, mean/maximum doses to heart, ipsilateral lung, left anterior descending aorta and contralateral breast) and tumor volume. Forty breast cancer patients were divided into two groups according to tumor location: twenty with left-sided (group A) and twenty with right-sided breast cancer (group B). Conformal (3DCRT), intensity modulated (IMRT) and volumetric arc modulated (VMAT) radiotherapy techniques were used for plan creation. Moderate to strong correlations were found for ipsilateral lung parameters for both groups of patients regardless of the treatment technique. Moderate to strong correlations were found for heart parameters in group A patients, while no correlations were observed in group B. The mismatch index presented moderate to strong correlations with tumor volume for all treatment techniques (r = -0.861 3DCRT, r = -0.556 IMRT, r = -0.533 VMAT) particularly in group A. The evaluated correlations indicate the role of dosimetric indices in personalized treatment planning. [ABSTRACT FROM AUTHOR]

Simple Summary: Nasopharynx cancer treatment often involves radiation therapy, but choosing the best method can be challenging. This study compares two advanced radiation techniques: one using X-rays and another using protons. Both aim to target the tumor while sparing healthy tissues, but their effectiveness can vary based on the cancer's location and stage. Researchers want to determine which technique works best for different patients, particularly those with cancer close to sensitive brain structures. By analyzing treatment plans for 28 patients, we developed guidelines to help doctors choose the most suitable technique. This research could lead to more personalized and effective treatments, potentially improving outcomes and quality of life for patients with nasopharynx cancer. The findings may also help healthcare providers use resources more efficiently, benefiting both patients and the broader medical community. Background: Proton therapy requires caution when treating patients with targets near neural structures. Intuitive and quantitative guidelines are needed to support decision-making concerning the treatment modality. This study compared dosimetric profiles of intensity-modulated proton therapy (IMPT) and intensity-modulated radiation therapy (IMRT) using helical tomotherapy (HT) for adaptive re-planning in cT3-4 nasopharyngeal cancer (NPCa) patients, aiming to establish criteria for selecting appropriate treatment modalities. Methods: HT and IMPT plans were generated for 28 cT3-4 NPCa patients undergoing definitive radiotherapy. Dosimetric comparisons were performed for target coverage and high-priority organs at risk (OARs). The correlation between dosimetric parameters and RT modality selection was analyzed with the target OAR distances. Results: Target coverages were similar, while IMPT achieved better dose spillage. HT was more favorable for brainstem D1, optic chiasm Dmax, optic nerves Dmax, and p-cord D1. IMPT showed advantages for oral cavity Dmean. Actually, 14 IMPT and 14 HT plans were selected as adaptive plans, with IMPT allocated to most cT3 patients (92.9% vs. 42.9%, p = 0.013). The shortest distances from the target to neural structures were negatively correlated with OAR doses. Receiver operating characteristic curve analyses were carried out to discover the optimal cut-off values of the shortest distances between the target and the OARs (temporal lobes and brainstem), which were 0.75 cm (AUC = 0.908, specificity = 1.00) and 0.85 cm (AUC = 0.857, specificity = 0.929), respectively. Conclusions: NPCa patients with cT4 tumor or with the shortest distance between the target and critical neural structures < 0.8 cm were suboptimal candidates for IMPT adaptive re-planning. These criteria may improve resource utilization and clinical outcomes. [ABSTRACT FROM AUTHOR]

The article discusses the use of artificial intelligence in radiotherapy treatment planning and dose optimization, particularly focusing on Head and Neck Cancer (HNC). Artificial intelligence systems can enhance treatment accuracy, efficiency, and outcomes by automating treatment planning, determining appropriate doses, and reducing human errors. While there are challenges and concerns, such as negative perceptions, the integration of artificial intelligence in cancer radiotherapy shows promise for improving treatment outcomes and efficiency. Continued research and development in this field are crucial for unlocking the full potential of AI in cancer radiotherapy. [Extracted from the article]

Purpose: The impact of various hip prosthesis materials on the amount of dose perturbation generated by 9 MeV electron and 18 MV photon beams during pelvic radiation therapy is analyzed in this research. Materials and Methods: The Varian 2100 C/D LINAC head for the electron (9 MeV) and photon (18 MV) modes and a water phantom with a realistic hip prosthesis were modeled using the Monte Carlo (MC) code; MCNPX (Ver. 2.6.0) and were benchmarked for measurement. Four different materials, including Cobalt-Chromium-Molybdenumalloy (CCM), Stainless Steel (SS), Titanium, and Titanium-alloy (Ti-alloy) were evaluated. The changes in electron and photon fluences and dose perturbations due to the presence of the hip prostheses were investigated. Results: An increased dose of 13.29%, 13.77%, 6.16%, and 5.93% for 9 MeV and 30.43%, 33.05%, 10.89%, and 11.27% for 18 MV was calculated for Ti-alloy, Ti, CCM, and SS prosthesis, respectively. At 0.5 mm distance from the prosthesis, the Electron Backscatter Factor (EBF) of 1.31, 1.33, 1.15, and 1.14 for 9 MeV and 1.32, 1.34, 1.11, and 1.12 for 18 MV was calculated for Ti-alloy, Ti, CCM, and SS prosthesis, respectively. Dose perturbation is higher at a near distance from the prosthesis; by reducing the distance from the Ti-alloy prosthesis (1.5 to 0.5 mm), an increased dose of 7.70% and 5.54% resulted in 18 MV and 9 MeV, respectively. The dose decreases of up to 21% behind the hip prosthesis were calculated for 18 MV. Conclusion: It is essential to consider the dose perturbation due to the presence of a hip prosthesis to achieve the optimal treatment planning in radiotherapy. [ABSTRACT FROM AUTHOR]

Background: To explore the beneficial subgroups after radiotherapy in high-grade neuroendocrine cervical cancer (HGNECC) and construct two survival prognosis models to quantify the efficacy of radiotherapy assessment. Methods: In this retrospective study, we included 592 eligible samples from the Surveillance, Epidemiology, and End Results (SEER) database and 56 patients with lymph-node positive HGNECC from Chongqing Medical University. Cox regression analysis was used to identify independent survival prognosis risk factors for HGNECC patients. Propensity score matching (PSM) was employed as it balances the baseline differences among grouping methods. Kaplan--Meier (K-M) curves were used to analyze survival differences among different groups. Two survival prediction nomograms were constructed separately (using the "rms" package in R software) based on whether radiotherapy was administered. The stability and accuracy of these models were assessed using receiver operating characteristic (ROC) curves and calibration curves in both the training and validation datasets. P<0.05 was considered to indicate statistically significant differences. Results: Age, Federation of Gynecology and Obstetrics (FIGO)-stage, and treatment methods (surgery vs. chemotherapy) were independent risk factors that affected survival prognosis (P<0.05). Radiotherapy showed adverse effects on survival in patients with early tumor staging, lymph-node negative status, and absence of distant metastasis (all P<0.05). The lymph-node positive group had a beneficial response to radiotherapy (P<0.05), and patients with metastasis in the radiotherapy group showed a survival protection trend (P=0.069). Conclusion: In HGNECC, patients with lymph-node positive status can benefit from radiotherapy in terms of survival outcomes. We constructed two survival prediction models based on whether radiotherapy was administered, thereby offering a more scientifically guided approach to clinical treatment planning by quantifying the radiotherapy efficacy. [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]

Simple Summary: The InterVision framework employs advanced deep learning techniques to interpolate or create intermediate images between existing ones using deformable vectors, thereby capturing specific patient characteristics, such as unique anatomical features and variations in organ shape, size, and position. These characteristics are vital for personalizing treatment plans in radiotherapy, as they allow for the use of pre-planning information, which is available before the treatment begins, ensuring a tailored and precise approach to each patient's care. The training process involves two steps: first, generating a general model using a comprehensive dataset, and second, fine-tuning this general model with additional data produced by the InterVision framework. By incorporating the dataset generated through the InterVision framework, we were able to create a more personalized model, surpassing the level of customization achieved by previous fine-tuning approaches. The performance of these models is evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) across 18 anatomical structures in 20 test patients. A total of 18 anatomical structures were selected based on prior treatments that involved the most organs, and 20 test patients were chosen according to the availability that has a re-planning CT and manual contours within the total dataset. This framework is especially valuable for accurately predicting complex organs and targets that present significant challenges for traditional deep learning algorithms, particularly due to the intricate contours and the variability in organ shapes across different patients. Adaptive radiotherapy (ART) workflows are increasingly adopted to achieve dose escalation and tissue sparing under dynamic anatomical conditions. However, recontouring and time constraints hinder the implementation of real-time ART workflows. Various auto-segmentation methods, including deformable image registration, atlas-based segmentation, and deep learning-based segmentation (DLS), have been developed to address these challenges. Despite the potential of DLS methods, clinical implementation remains difficult due to the need for large, high-quality datasets to ensure model generalizability. This study introduces an InterVision framework for segmentation. The InterVision framework can interpolate or create intermediate visuals between existing images to generate specific patient characteristics. The InterVision model is trained in two steps: (1) generating a general model using the dataset, and (2) tuning the general model using the dataset generated from the InterVision framework. The InterVision framework generates intermediate images between existing patient image slides using deformable vectors, effectively capturing unique patient characteristics. By creating a more comprehensive dataset that reflects these individual characteristics, the InterVision model demonstrates the ability to produce more accurate contours compared to general models. Models are evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) for 18 structures in 20 test patients. As a result, the Dice score was 0.81 ± 0.05 for the general model, 0.82 ± 0.04 for the general fine-tuning model, and 0.85 ± 0.03 for the InterVision model. The Hausdorff distance was 3.06 ± 1.13 for the general model, 2.81 ± 0.77 for the general fine-tuning model, and 2.52 ± 0.50 for the InterVision model. The InterVision model showed the best performance compared to the general model. The InterVision framework presents a versatile approach adaptable to various tasks where prior information is accessible, such as in ART settings. This capability is particularly valuable for accurately predicting complex organs and targets that pose challenges for traditional deep learning algorithms. [ABSTRACT FROM AUTHOR]

Purpose: The study aimed to investigate the optimal isodose line (IDL) in linear accelerator‐based stereotactic radiotherapy for single brain metastasis, using HyperArc. We compared the dosimetric parameters for target and normal brain tissue among six plans with different IDLs. Methods: This study included 30 patients with single brain metastasis. We retrospectively generated six plans for each tumor with different IDLs (80%, 70%, 60%, 50%, 40%, and 33%) using HyperArc. All treatment plans were normalized to the prescription dose of 35 Gy in five fractions which was covered by 95% of the planning target volume (PTV), defined by adding a 1.0 mm margin to the gross tumor volume (GTV). The dosimetric parameters were compared among the six plans. Results: For GTV > 0.1 cm3, the ratio of brain–GTV volumes receiving 25 Gy to PTV (V25Gy/PTV) was significantly lower at IDL 40%–70% than at IDL 80% and 33% (p < 0.01, retrospectively). For GTV < 0.1 cm3, V25Gy/PTV decreased continuously as IDL decreased. The values of D99% and D80% for GTV increased with decreasing IDL. An IDL of 50% or less was required to achieve D99% of greater than 43 Gy and D80% of greater than 50 Gy. The mean values of D99% and D80% for IDL 50% were 44.3 and 51.9 Gy. Conclusion: The optimal IDL is 40%–50% for GTV > 0.1 cm3. These lower IDLs could increase D99% and D80% of GTV while lowering V25Gy of normal brain tissue, which may help reduce the risk of radiation necrosis and improve local control. [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]

This paper examines the dosimetric uncertainty arising from the use of thermoplastic masks in the treatment of head and neck cancer through radiotherapy. This study was conducted through Monte Carlo simulations using the Monte Carlo N-Particle eXtended (MCNPX code), and the theoretical results are compared with radiochromic films. Using material characterization techniques, the compounds of the thermoplastic mask were identified, confirming that most of the material corresponds to the polymer C10H16O4. The theoretical results show increases ranging from 42% to 57.4% in the surface absorbed dose for 6 and 15 MV photon beams, respectively, compared to the absorbed dose without the mask. The experimental data corroborate these findings, showing dose increases ranging from 18.4% to 52.1% compared to the expected surface absorbed dose without the mask. These results highlight the need to consider the bolus effect induced by thermoplastic masks during the precise and safe planning and application of radiotherapy treatment in order to ensure its therapeutic efficacy and minimize the associated risks to patients. [ABSTRACT FROM AUTHOR]

Background: Quality assurance (QA) of patient‐specific treatment plans for intensity‐modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. Purpose: The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. Method: We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3 mm; 3%, 2 mm; 2%, 2 mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. Results: The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. Conclusion: When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method. [ABSTRACT FROM AUTHOR]

Purpose objective(s): Manual contouring of the prostate region in planning computed tomography (CT) images is a challenging task due to factors such as low contrast in soft tissues, inter- and intra-observer variability, and variations in organ size and shape. Consequently, the use of automated contouring methods can offer significant advantages. In this study, we aimed to investigate automated male pelvic multi-organ contouring in multi-center planning CT images using a hybrid convolutional neural network-vision transformer (CNN-ViT) that combines convolutional and ViT techniques. Materials/methods: We used retrospective data from 104 localized prostate cancer patients, with delineations of the clinical target volume (CTV) and critical organs at risk (OAR) for external beam radiotherapy. We introduced a novel attention-based fusion module that merges detailed features extracted through convolution with the global features obtained through the ViT. Results: The average dice similarity coefficients (DSCs) achieved by VGG16-UNet-ViT for the prostate, bladder, rectum, right femoral head (RFH), and left femoral head (LFH) were 91.75%, 95.32%, 87.00%, 96.30%, and 96.34%, respectively. Experiments conducted on multi-center planning CT images indicate that combining the ViT structure with the CNN network resulted in superior performance for all organs compared to pure CNN and transformer architectures. Furthermore, the proposed method achieves more precise contours compared to state-of-the-art techniques. Conclusion: Results demonstrate that integrating ViT into CNN architectures significantly improves segmentation performance. These results show promise as a reliable and efficient tool to facilitate prostate radiotherapy treatment planning. [ABSTRACT FROM AUTHOR]

Background: Conventional single-energy CT can only provide a raw estimation of electron density (ED) for dose calculation by developing a calibration curve that simply maps the HU values to ED values through their correlations. Spectral CT, also known as dual-energy CT (DECT) or multi-energy CT, can generate a series of quantitative maps, such as ED maps. Using spectral CT for radiotherapy simulations can directly acquire ED information without developing specific calibration curves. The purpose of this study is to assess the feasibility of utilizing electron density (ED) maps generated by a novel dual-layer detector spectral CT simulator for dose calculation in radiotherapy treatment plans. Methods: 30 patients from head&neck, chest, and pelvic treatment sites were selected retrospectively, and all of them underwent spectral CT simulation. Treatment plans based on conventional CT images were transplanted to ED maps with the same structure set, including planning target volume (PTV) and organs at risk (OARs), and the dose distributions were then recalculated. The differences in dose and volume histogram (DVH) parameters of the PTV and OARs between the two types of plans were analyzed and compared. Besides, gamma analysis between these plans was performed by using MEPHYSTO Navigator software. Results: In terms of PTV, the homogeneity index (HI), gradient index (GI), D2%, D98%, and Dmean showed no significant difference between conventional plans and ED plans. For OARs, statistically significant differences were observed in parotids D50%, brainstem in head&neck plans, spinal cord in chest plans and rectum D50% in pelvic plans, whereas the variance remained minor. For the rest, the DVH parameters exhibited no significant difference between conventional plans and ED plans. All of the mean gamma passing rates (GPRs) of gamma analysis were higher than 90%. Conclusion: Compared to conventional treatment plans relying on CT images, plans utilizing ED maps demonstrated similar dosimetric quality. However, the latter approach enables direct utilization in dose calculation without the requirements of establishing and selecting a specific Hounsfield unit (HU) to ED calibration curve, providing an advantage in clinical applications. [ABSTRACT FROM AUTHOR]

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). Conclusion: 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. [ABSTRACT FROM AUTHOR]

Objective: The aim of the study is to compare the accuracy of dose calculation for different dose calculation algorithms with different prescription points (air, tissue, air--tissue interface in carcinoma lung patients and bone, tissue, and bone--tissue interface in carcinoma buccal Mucosa tumors). Materials and Methods: Forty-one patients with carcinoma lung and buccal mucosa were retrospectively selected for this study. A three-dimensional conformal radiotherapy reference plan was created using the prescription point in the tissue with Monte Carlo (MC) algorithms for both the groups of patients. The reference plan was modified by changing the prescription point and algorithms in the tissue, air, air--tissue interface for lung patients and tissue, bone, and bone--tissue interface for buccal mucosa patients. The dose received by the target volume and other organs at risk (OAR) structures was compared. To find out the statistical difference between different prescription points and algorithms, the statistical tests were performed with repeated measures ANOVA. Results: The target volume receiving 95% dose coverage in lung patients decreased to -3.08%, -5.75%, and -1.87% in the dose prescription point at the air--tissue interface with the dose calculation algorithms like MC, collapsed cone (CC), and pencil beam (PB), respectively, compared to that of the MC tissue. Spinal cord dose was increased in the CC and PB algorithms in all prescription points in patients with lung and buccal mucosa. OAR dose calculated by PB in all prescription points showed a significant deviation compared to MC tissue prescription point. Conclusion: This study will help demonstrate the accuracy of dose calculation for the different dose prescription points with the different treatment algorithms in radiotherapy treatment planning. [ABSTRACT FROM AUTHOR]

An editorial is presented that focuses on the role of technological innovation in Medical Radiation Sciences (MRS), emphasizing its integration into clinical practice to enhance patient care through advancements like artificial intelligence, and patient-centric approaches. It underscores the need for rigorous governance, inclusive co-design with stakeholders, and specialized education to effectively implement these innovations while ensuring patient safety and improving healthcare outcomes.

Background: Glioblastoma (GBM) is the most common and aggressive primary brain cancer. The treatment of GBM consists of a combination of surgery and subsequent oncological therapy, i.e., radiotherapy, chemotherapy, or their combination. If postoperative oncological therapy involves irradiation, magnetic resonance imaging (MRI) is used for radiotherapy treatment planning. Unfortunately, in some cases, a very early worsening (progression) or return (recurrence) of the disease is observed several weeks after the surgery and is called rapid early progression (REP). Radiotherapy planning is currently based on MRI for target volumes definitions in many radiotherapy facilities. However, patients with REP may benefit from targeting radiotherapy with other imaging modalities. The purpose of the presented clinical trial is to evaluate the utility of 11C-methionine in optimizing radiotherapy for glioblastoma patients with REP. Methods: This study is a nonrandomized, open-label, parallel-setting, prospective, monocentric clinical trial. The main aim of this study was to refine the diagnosis in patients with GBM with REP and to optimize subsequent radiotherapy planning. Glioblastoma patients who develop REP within approximately 6 weeks after surgery will undergo 11C-methionine positron emission tomography (PET/CT) examinations. Target volumes for radiotherapy are defined using both standard planning T1-weighted contrast-enhanced MRI and PET/CT. The primary outcome is progression-free survival defined using RANO criteria and compared to a historical cohort with REP treated without PET/CT optimization of radiotherapy. Discussion: PET is one of the most modern methods of molecular imaging. 11C-Methionine is an example of a radiolabelled (carbon 11) amino acid commonly used in the diagnosis of brain tumors and in the evaluation of response to treatment. Optimized radiotherapy may also have the potential to cover those regions with a high risk of subsequent progression, which would not be identified using standard-of-care MRI for radiotherapy planning. This is one of the first study focused on radiotherapy optimization for subgroup of patinets with REP. Trial Registration: NCT05608395, registered on 8.11.2022 in clinicaltrials.gov; EudraCT Number: 2020–000640-64, registered on 26.5.2020 in clinicaltrialsregister.eu. Protocol ID: MOU-2020–01, version 3.2, date 18.09.2020. [ABSTRACT FROM AUTHOR]

Automation and artificial intelligence (AI) is already possible for many radiation therapy planning and treatment processes with the aim of improving workflows and increasing efficiency in radiation oncology departments. Currently, AI technology is advancing at an exponential rate, as are its applications in radiation oncology. This commentary highlights the way AI has begun to impact radiation therapy treatment planning and looks ahead to potential future developments in this space. Historically, radiation therapist's (RT's) role has evolved alongside the adoption of new technology. In Australia, RTs have key clinical roles in both planning and treatment delivery and have been integral in the implementation of automated solutions for both areas. They will need to continue to be informed, to adapt and to transform with AI technologies implemented into clinical practice in radiation oncology departments. RTs will play an important role in how AI‐based automation is implemented into practice in Australia, ensuring its application can truly enable personalised and higher‐quality treatment for patients. To inform and optimise utilisation of AI, research should not only focus on clinical outcomes but also AI's impact on professional roles, responsibilities and service delivery. Increased efficiencies in the radiation therapy workflow and workforce need to maintain safe improvements in practice and should not come at the cost of creativity, innovation, oversight and safety. [ABSTRACT FROM AUTHOR]

Backgroud and objectives: The implementation of patient-specific quality assurance (PSQA) has become a crucial aspect of the radiation therapy process. Machine learning models have demonstrated their potential as virtual QA tools, accurately predicting the gamma passing rate (GPR) of volumetric modulated arc therapy (VMAT)plans, thereby ensuring safe and efficient treatment for patients. However, there is limited multi-center research dedicated to predicting the GPR. In this study, a dosiomics-based machine learning approach was employed to construct a prediction model for classifying GPR in multiple radiotherapy institutions. Additionally, the model's performance was compared by evaluating the impact of two distinct feature selection methods. Methods: A retrospective data collection was conducted on 572 VMAT patients across three radiotherapy institutions. Utilizing a three-dimensional dose verification technique grounded in real-time measurements, y analysis was conducted according to the criteria of 3%/2 mm and 2%/2 mm, employing a dose threshold of 10% along with absolute dose and global normalization mode. Dosiomics features were extracted from the dose files, and distinct subsets of features were selected as inputs for the model using the random forest (RF) and RF combined with SHapley Additive exPlanations (SHAP) methods. The data underwent training using the extreme gradient boosting (XGBoost) algorithm, and the model's classification performance was assessed through F1-score and area under the curve (AUC) values. Results: The model exhibited optimal performance under the 3%/2 mm criteria, utilizing a subset of 20 features and attaining an AUC value of 0.88 and an F1score of 0.89. Similarly, under the 2%/2 mm criteria, the model demonstrated superior performance with a subset of 10 features, resulting in an AUC value of 0.91 and an F1-score of 0.89. The feature selection methods of RF and RF + SHAP have achieved good model performance by selecting as few features as possible. Conclusion: Based on the multi-center PSQA results, it is possible to utilize dosiomics features extracted from dose files to construct a machine learning predictive model. This model demonstrates excellent discriminative abilities, thus promoting the progress of gamma passing rate prognostic models in clinical application and implementation. Furthermore, it holds potential in providing patients with secure and efficient personalized QA management, while also reducing the workload of medical physicists. [ABSTRACT FROM AUTHOR]

Purpose: Artificial intelligence (AI) based commercial software can be used to automatically delineate organs at risk (OAR), with potential for efficiency savings in the radiotherapy treatment planning pathway, and reduction of inter‐ and intra‐observer variability. There has been little research investigating gross failure rates and failure modes of such systems. Method: 50 head and neck (H&N) patient data sets with "gold standard" contours were compared to AI‐generated contours to produce expected mean and standard deviation values for the Dice Similarity Coefficient (DSC), for four common H&N OARs (brainstem, mandible, left and right parotid). An AI‐based commercial system was applied to 500 H&N patients. AI‐generated contours were compared to manual contours, outlined by an expert human, and a gross failure was set at three standard deviations below the expected mean DSC. Failures were inspected to assess reason for failure of the AI‐based system with failures relating to suboptimal manual contouring censored. True failures were classified into 4 sub‐types (setup position, anatomy, image artefacts and unknown). Results: There were 24 true failures of the AI‐based commercial software, a gross failure rate of 1.2%. Fifteen failures were due to patient anatomy, four were due to dental image artefacts, three were due to patient position and two were unknown. True failure rates by OAR were 0.4% (brainstem), 2.2% (mandible), 1.4% (left parotid) and 0.8% (right parotid). Conclusion: True failures of the AI‐based system were predominantly associated with a non‐standard element within the CT scan. It is likely that these non‐standard elements were the reason for the gross failure, and suggests that patient datasets used to train the AI model did not contain sufficient heterogeneity of data. Regardless of the reasons for failure, the true failure rate for the AI‐based system in the H&N region for the OARs investigated was low (∼1%). [ABSTRACT FROM AUTHOR]

Radiotherapy treatment plans have become highly conformal, posing additional constraints on the accuracy of treatment delivery. Here, we explore the use of radiation-sensitive ultrasound contrast agents (superheated phase-change nanodroplets) as dosimetric radiation sensors. In a series of experiments, we irradiated perfluorobutane nanodroplets dispersed in gel phantoms at various temperatures and assessed the radiation-induced nanodroplet vaporization events using offline or online ultrasound imaging. At 25 °C and 37 °C, the nanodroplet response was only present at higher photon energies (≥10 MV) and limited to <2 vaporization events per cm2 per Gy. A strong response (~2000 vaporizations per cm2 per Gy) was observed at 65 °C, suggesting radiation-induced nucleation of the droplet core at a sufficiently high degree of superheat. These results emphasize the need for alternative nanodroplet formulations, with a more volatile perfluorocarbon core, to enable in vivo photon dosimetry. The current nanodroplet formulation carries potential as an innovative gel dosimeter if an appropriate gel matrix can be found to ensure reproducibility. Eventually, the proposed technology might unlock unprecedented temporal and spatial resolution in image-based dosimetry, thanks to the combination of high-frame-rate ultrasound imaging and the detection of individual vaporization events, thereby addressing some of the burning challenges of new radiotherapy innovations. [ABSTRACT FROM AUTHOR]

Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept "As Low As Reasonably Achievable". Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. Methods: A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDIvol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Results: Significant variations were found in the DLP, CTDIvol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution's DRL was significantly low. The optimized dose protocol showed a reduction in the CTDIvol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Conclusions: Optimized protocol DRLs were proposed for comparison purposes. [ABSTRACT FROM AUTHOR]

This study aimed to propose an automated prediction approach of the consolidation tumor ratios (CTRs) of part-solid tumors of patients treated with radiotherapy on treatment planning computed tomography images using deep learning segmentation (DLS) models. For training the DLS model for cancer regions, a total of 115 patients with non-small cell lung cancer (NSCLC) who underwent stereotactic body radiation therapy were selected as the training dataset, including solid, part-solid, and ground-glass opacity tumors. For testing the automated prediction approach of CTRs based on segmented tumor regions, 38 patients with part-solid tumors were selected as an internal test dataset A (IN) from a same institute as the training dataset, and 49 patients as an external test dataset (EX) from a public database. The CTRs for part-solid tumors were predicted as ratios of the maximum diameters of solid components to those of whole tumors. Pearson correlations between reference and predicted CTRs for the two test datasets were 0.953 (IN) and 0.926 (EX) for one of the DLS models (p < 0.01). Intraclass correlation coefficients between reference and predicted CTRs for the two test datasets were 0.943 (IN) and 0.904 (EX) for the same DLS models. The findings suggest that the automated prediction approach could be robust in calculating the CTRs of part-solid tumors. [ABSTRACT FROM AUTHOR]

This article discusses two innovations in medical imaging techniques. The first innovation, called Golden-angle Radial Sparse Parallel (GRASP) VIBE, was introduced to overcome difficulties with breath holding during liver and pancreas MRI scans. The technique allows for the retrospective reconstruction of multiple contrast-enhanced phases, improving image quality and reducing the need for non-contrast scans. The second innovation involves using 6 flattening filter free (6FFF) for deep inspiration breath hold (DIBH) breast treatments. This technique resulted in significant time savings and maintained a similar level of plan quality. Both innovations have the potential to improve efficiency and resource utilization in clinical practice. [Extracted from the article]

Background: The course, the origin of the branch, and the division of the aorta may vary and the aortic bifurcation level and branching can be determined by arteriogram, magnetic resonance imaging, computed tomography (CT) scan, and cadaveric dissection. Aims and Objectives: This study aimed to find the variation in aortic bifurcation levels in cervical malignancy patients who underwent radiotherapy. Materials and Methods: Between January 2018 and December 2022 previously untreated, histologically proven squamous cell carcinoma of the cervix patients who received radiotherapy in our department were selected for this retrospective analysis. A planning contrast-enhanced CT scan with a 3 mm slice thickness was done in a CT simulator to delineate the target volume and aorta, common iliac vessels in all patients. Results: 407 cervical carcinoma patients who received radiotherapy in Linear Accelerator (LINAC) were included in this analysis. The aortic bifurcation is most commonly situated at the level of the L3-L4 intervertebral disc in 290 (71.25%) cases with a range between the upper L3 body and the lower L5 body. The common iliac bifurcation was situated at the level of L5-S1 in 338 cases (83.04%) and S1 vertebral body in 63 cases (15.47%). The average length of the left common iliac artery was 4.58 cm and the right common iliac artery was 4.44 cm. The diameter of the aorta at the level of just before the bifurcation is 1.39 cm. The average diameter of the left common iliac artery and right common iliac artery were 1.21 cm and 1.13 cm, respectively. The right and left take-off angles (αR, αL) are 25.58° and 23.78°, respectively. Conclusion: The knowledge regarding the anatomic variation of branching and bifurcation of the aorta of utmost importance for surgical procedures, interventional radiology procedures, and proper radiotherapy treatment planning. Acknowledging these anatomic variations may also reduce complications. [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]

In radiotherapy treatment planning, the absorbed doses are subject to executional and preparational errors, which propagate to plan quality metrics. Accurately quantifying these uncertainties is imperative for improved treatment outcomes. One approach, analytical probabilistic modeling (APM), presents a highly computationally efficient method. This study evaluates the empirical distribution of dose–volume histogram points (a typical plan metric) derived from Monte Carlo sampling to quantify the accuracy of modeling uncertainties under different distribution assumptions, including Gaussian, log-normal, four-parameter beta, gamma, and Gumbel distributions. Since APM necessitates the bivariate cumulative distribution functions, this investigation also delves into approximations using a Gaussian or an Ali–Mikhail–Haq Copula. The evaluations are performed in a one-dimensional simulated geometry and on patient data for a lung case. Our findings suggest that employing a beta distribution offers improved modeling accuracy compared to a normal distribution. Moreover, the multivariate Gaussian model outperforms the Copula models in patient data. This investigation highlights the significance of appropriate statistical distribution selection in advancing the accuracy of uncertainty modeling in radiotherapy treatment planning, extending an understanding of the analytical probabilistic modeling capacities in this crucial medical domain. [ABSTRACT FROM AUTHOR]

Purpose: Radiotherapy patients often face undue anxiety due to misconceptions about radiation and their inability to visualize their upcoming treatments. Access to their personal treatment plans is one way in which pre-treatment anxiety may be reduced. But radiotherapy data are quite complex, requiring specialized software for display and necessitating personalized explanations for patients to understand them. Therefore, our goal was to design and implement a novel radiotherapy menu in a patient portal to improve patient access to and understanding of their radiotherapy treatment plans. Methods: A prototype radiotherapy menu was developed in our institution's patient portal following a participatory stakeholder co-design methodology.Customizable page templates were designed to render key radiotherapy data in the portal's patient-facing mobile phone app. DICOM-RT data were used to provide patients with relevant treatment parameters and generate pre-treatment 3D visualizations of planned treatment beams, while the mCODE data standard was used to provide post-treatment summaries of the delivered treatments. A focus group was conducted to gather initial patient feedback on the menu. Results: Pre-treatment: the radiotherapy menu provides patients with a personalized treatment plan overview, including a personalized explanation of their treatment, along with an interactive 3D rendering of their body, and treatment beams for visualization. Post-treatment: a summary of the delivered radiotherapy is provided, allowing patients to retain a concise personal record of their treatment that can easily be shared with future healthcare providers. Focus group feedback was overwhelmingly positive.Patients highlighted how the intuitive presentation of their complex radiotherapy data would better prepare them for their radiation treatments. Conclusions: We successfully designed and implemented a prototype radiotherapy menu in our institution's patient portal that improves patient access to and understanding of their radiotherapy data.We used the mCODE data standard to generate post-treatment summaries in a way that is easily shareable and interoperable. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) in radiation therapy planning is rapidly changing the profession by enabling fast automation. This evolution has both opportunities and concerns for the future of the multidisciplinary team. In Australia, radiation therapists (RTs) are discussing the impact of AI and the potential developments in their role. The introduction of AI has the potential to fully automate treatment planning, allowing for personalized treatment for each patient. However, there are considerations regarding ethics, standards, and the impact on the roles and responsibilities of RTs and the multidisciplinary team. Education and training will be essential to ensure competence with these new developments, and patient involvement and communication strategies are important during the transition to AI. While AI has the potential to increase efficiency and standardization, questions have been raised about how it may hinder human creativity and innovation. Overall, AI-based approaches have the potential to improve personalized care and treatment outcomes. [Extracted from the article]

Purpose: Brain metastasis is a common, life-threatening neurological problem for patients with cancer. Single-isocenter volumetric modulated arc therapy (VMAT) has been popularly used due to its highly conformal dose and short treatment time. Accurate prediction of its dose distribution can provide a general standard for evaluating the quality of treatment plan. In this study, a deep learning model is applied to the dose prediction of a single-isocenter VMAT treatment plan for radiotherapy of multiple brain metastases. Method: A U-net with residual networks (U-ResNet) is employed for the task of dose prediction. The deep learning model is first trained from a database consisting of hundreds of historical treatment plans. The 3D dose distribution is then predicted with the input of the CT image and contours of regions of interest (ROIs). A total of 150 single-isocenter VMAT plans for multiple brain metastases are used for training and testing. The model performance is evaluated based on mean absolute error (MAE) and mean absolute differences of multiple dosimetric indexes (DIs), including (Dmax and Dmean) for OARs, (D98, D95, D50, and D2) for PTVs, homogeneity index, and conformity index. The similarity between the predicted and clinically approved plan dose distribution is also evaluated. Result: For 20 tested patients, the largest and smallest MAEs are 3.3% ± 3.6% and 1.3% ± 1.5%, respectively. The mean MAE for the 20 tested patients is 2.2% ± 0.7%. The mean absolute differences of D98, D95, D50, and D2 for PTV60, PTV52, PTV50, and PTV40 are less than 2.5%, 3.0%, 2.0%, and 3.0%, respectively. The prediction accuracy of OARs for Dmax and Dmean is within 3.2% and 1.2%, respectively. The average DSC ranges from 0.86 to 1 for all tested patients. Conclusion: U-ResNet is viable to produce accurate dose distribution that is comparable to those of the clinically approved treatment plans. The predicted results can be used to improve current treatment planning design, plan quality, efficiency, etc. [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]

Purpose: This research aimed to develop a prediction model to assess bladder wall dosimetry during radiotherapy for patients with pelvic tumors, thereby facilitating the refinement and evaluation of radiotherapy treatment plans to mitigate bladder toxicity. Methods: Radiotherapy treatment plans of 49 rectal cancer patients and 45 gynecologic cancer patients were collected, and multiple linear regression analyses were used to generate prediction models for bladder wall dose parameters (V10−45Gy(cm3)${V_{10 - 45Gy\ }}({{\mathrm{c}}{{\mathrm{m}}^3}})$, Dmean(Gy)${D_{mean}}({{\mathrm{Gy}}})$). These models were based on the multiscale spatial relationship between the planning target volume (PTV) and the bladder or bladder wall. The proportion of bladder or bladder wall volume overlapped by the different distance expansions of the PTV was used as an indicator of the multiscale spatial relationship. The accuracy of these models was verified in a cohort of 12 new patients, with further refinement of radiotherapy treatment plans using the predicted values as optimization parameters. Model accuracy was assessed using root mean square error (RMSE) and mean percentage error (MPE). Results: Models derived from individual disease data outperformed those derived from combined datasets. Predicted bladder wall dose parameters were accurate, with the majority of initial calculated values for new patients falling within the 95% confidence interval of the model predictions. There was a robust correlation between the predicted and actual dose metrics, with a correlation coefficient of 0.943. Using the predicted values to optimize treatment plans significantly reduced bladder wall dose (p<$\ < \ $0.001), with bladder wall Dmean(Gy)${D_{{\mathrm{mean}}}}({Gy})$ and V10−45Gy(cm3)${V_{10 - 45Gy\ }}({{\mathrm{c}}{{\mathrm{m}}^3}})$ decreasing by 2.27±0.80 Gy (5.8%±1.8%) and 2.96±2.05 cm3 (7.9%±5.4%), respectively. Conclusion: The formulated prediction model provides a valuable tool for predicting and minimizing bladder wall dose and for optimizing and evaluating radiotherapy treatment plans for pelvic tumor patients. This approach holds promise for reducing bladder toxicity and potentially improving patient outcomes. [ABSTRACT FROM AUTHOR]

Identification of isocitrate dehydrogenase (IDH)-mutant glioma patients at high risk of early progression is critical for radiotherapy treatment planning. Currently tools to stratify risk of early progression are lacking. We sought to identify a combination of molecular markers that could be used to identify patients who may have a greater need for adjuvant radiation therapy machine learning technology. 507 WHO Grade 2 and 3 glioma cases from The Cancer Genome Atlas, and 1309 cases from AACR GENIE v13.0 datasets were studied for genetic disparities between IDH1-wildtype and IDH1-mutant cohorts, and between different age groups. Genetic features such as mutations and copy number variations (CNVs) correlated with IDH1 mutation status were selected as potential inputs to train artificial neural networks (ANNs) to predict IDH1 mutation status. Grade 2 and 3 glioma cases from the Memorial Sloan Kettering dataset (n = 404) and Grade 3 glioma cases with subtotal resection (STR) from Northwestern University (NU) (n = 21) were used to further evaluate the best performing ANN model as independent datasets. IDH1 mutation is associated with decreased CNVs of EGFR (21% vs. 3%), CDKN2A (20% vs. 6%), PTEN (14% vs. 1.7%), and increased percentage of mutations for TP53 (15% vs. 63%), and ATRX (10% vs. 54%), which were all statistically significant (p < 0.001). Age > 40 was unable to identify high-risk IDH1-mutant with early progression. A glioma early progression risk prediction (GlioPredictor) score generated from the best performing ANN model (6/6/6/6/2/1) with 6 inputs, including CNVs of EGFR, PTEN and CDKN2A, mutation status of TP53 and ATRX, patient's age can predict IDH1 mutation status with over 90% accuracy. The GlioPredictor score identified a subgroup of high-risk IDH1-mutant in TCGA and NU datasets with early disease progression (p = 0.0019, 0.0238, respectively). The GlioPredictor that integrates age at diagnosis, CNVs of EGFR, CDKN2A, PTEN and mutation status of TP53, and ATRX can identify a small cohort of IDH-mutant with high risk of early progression. The current version of GlioPredictor mainly incorporated clinically often tested genetic biomarkers. Considering complexity of clinical and genetic features that correlate with glioma progression, future derivatives of GlioPredictor incorporating more inputs can be a potential supplement for adjuvant radiotherapy patient selection of IDH-mutant glioma patients. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the most common cancer in women. Radiotherapy is an important part of breast cancer treatment after surgery. Breast cancer radiotherapy is usually delivered in 3–5 weeks. This is a long duration for women with breast cancer to stay away from the family and work. We wanted to reduce this duration so that the wages loss and the logistics can be minimised for these patients. Hypofractionation, i.e. high dose per fraction, is delivered in a smaller number of days. In this study, we will compare a 1-week schedule of hypofractionated adjuvant whole breast/chest wall and/or regional nodal radiotherapy against 2 weeks for locoregional disease control, toxicities, quality of life (QoL), survival and second cancers after primary surgery in patients with breast cancer. Methods: Eligible patients with breast cancer after mastectomy or breast conserving surgery (BCS) will be treated with a radiotherapy dose of 26 Gy in 5 fractions over 1 week in the study arm and 34 Gy in 10 fractions over 2 weeks in the control arm. The primary endpoint of this noninferiority study will be locoregional tumour control. Secondary endpoints will be early and late radiation toxicities, quality of life, contralateral primary tumours, regional and distant metastases, survival and second cancers. A total of 1018 patients will be randomised (1:1) to receive 1 week or 2 weeks of radiotherapy. An event-driven analysis will be performed after at least 94 patients have documented locoregional recurrences. Acute radiation toxicity will be assessed and scaled according to the RTOG grading system. Late radiation toxicity will be assessed with the Radiation Therapy Oncology Group and the European Organisation for Research and Treatment of Cancer late radiation morbidity scale. Cosmetic assessment will be done using Harvard/NSABP/RTOG breast cosmesis grading scale at baseline and 3 and 5 years. QoL will be assessed with EORTC QLQ-30 and EORTC QLQ-BR 23 at baseline and 3 and 5 years. Discussion: Hypofractionation reduces treatment time to half while maintaining breast cosmesis and gives control rates equal to conventional fractionation. This is possible because breast tissue can tolerate high dose per fraction. In this study, we presume that 1-week radiotherapy will be non-inferior to 2 week radiotherapy, i.e. disease control will be similar with both the schedules without additional side effects, and QoL of these patients will be maintained. If we are able to achieve these outcomes, then patients will be able to complete their radiotherapy in less duration. There is not much data on regional nodal irradiation with hypofraction in breast cancer. We have used hypofraction for regional nodal irradiation in the past and not encountered any safety issue. If we are able to prove that late-term effects are comparable in the two schedules, it will make the radiation oncologist confident about hypofractionation in breast cancer. As breast cancer is a leading cancer in females and radiation therapy is an integral part of its local management, hypofractionation will help radiation centres worldwide to meet the growing need for radiation treatment in breast cancer, particularly in developing countries where resources are limited. It will also reduce the financial burden on the patient and family. Since we will treat these patients with both simple and complex radiotherapy techniques, it will also be possible for the low-income countries to follow this trial without needing a high-end or expensive radiotherapy equipment as the planning and treatment process will be very simple. Trial registration: The trial is registered with ClinicalTrials.gov ID NCT04472845 and CTRI with REF/2020/09/037050. [ABSTRACT FROM AUTHOR]

Purpose: Patients with cancer have a high risk of depression. However, a few studies have assessed differences in the incidence of depression among patients with prostate cancer (PC) based on whether they received radiotherapy (RTx) or surgical treatment. Materials and Methods: We analyzed data from the National Health Insurance Sharing Service database regarding the entire Korean adult population with PC (n=210,924) between 2007 and 2017. The adjusted hazard ratios (HRs) of depression associated with treatment were estimated using propensity score-matched Cox proportional hazards models and Kaplan–Meier survival analyses. Results: Our final cohort comprised 9,456 patients with PC; of which, 8,050 men underwent surgery. During a mean followup duration of 7.1 years, 503 (5.3%) patients were newly diagnosed with depression. A significant difference in the incidence of depression was noted between the RTx and surgery groups (RTx vs. surgery: 5.55% vs. 5.28%; p=0.011) in the unmatched cohort. In the matched cohort, older age (≥70 years, HR: 1.596, p<0.001) and poor Charlson comorbidity index scores (HR: 1.232, p=0.039) were correlated with the risk of depression. In addition, the adjusted HR for depression in the surgery group was 0.843 (p=0.221) compared with that in the RTx group. Kaplan–Meier analyses revealed that no significant difference in the cumulative probability of persistent depression was detected between the RTx and surgery groups in matched cohort (p=0.3386). Conclusions: In this nationwide population-based study, no significant differences in the risk of depression were observed between the surgical and RTx groups. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the status of remote-radiotherapy treatment planning (RRTP) in Japan through a nationwide questionnaire survey. The survey was conducted between 29 June and 4 August 2022, at 834 facilities in Japan that were equipped with linear accelerators. The survey utilized a Google form that comprised 96 questions on facility information, information about the respondent, utilization of RRTP between facilities, usage for telework and the inclination to implement RRTPs in the respondent's facility. The survey analyzed the utilization of the RRTP system in four distinct implementation types: (i) utilization as a supportive facility, (ii) utilization as a treatment facility, (iii) utilization as a teleworker outside of the facility and (iv) utilization as a teleworker within the facility. The survey response rate was 58.4% (487 facilities responded). Among the facilities that responded, 10% (51 facilities) were implementing RRTP. 13 served as supportive facilities, 23 as treatment facilities, 17 as teleworkers outside of the facility and 5 as teleworkers within the facility. In terms of system usage between supportive and treatment facilities, 70–80% of the participants utilized the system for emergencies or as overtime work for external workers. A substantial number of facilities (38.8%) reported that they were unfamiliar with RRTP implementation. The survey showed that RRTP utilization in Japan is still limited, with a significant number of facilities unfamiliar with the technology. The study highlights the need for greater understanding and education about RRTP and financial funds of economical compensation. [ABSTRACT FROM AUTHOR]

Purpose: This study proposed an end-to-end unsupervised medical fusion generative adversarial network, MedFusionGAN, to fuse computed tomography (CT) and high-resolution isotropic 3D T1-Gd Magnetic resonance imaging (MRI) image sequences to generate an image with CT bone structure and MRI soft tissue contrast to improve target delineation and to reduce the radiotherapy planning time. Methods: We used a publicly available multicenter medical dataset (GLIS-RT, 230 patients) from the Cancer Imaging Archive. To improve the models generalization, we consider different imaging protocols and patients with various brain tumor types, including metastases. The proposed MedFusionGAN consisted of one generator network and one discriminator network trained in an adversarial scenario. Content, style, and L1 losses were used for training the generator to preserve the texture and structure information of the MRI and CT images. Results: The MedFusionGAN successfully generates fused images with MRI soft-tissue and CT bone contrast. The results of the MedFusionGAN were quantitatively and qualitatively compared with seven traditional and eight deep learning (DL) state-of-the-art methods. Qualitatively, our method fused the source images with the highest spatial resolution without adding the image artifacts. We reported nine quantitative metrics to quantify the preservation of structural similarity, contrast, distortion level, and image edges in fused images. Our method outperformed both traditional and DL methods on six out of nine metrics. And it got the second performance rank for three and two quantitative metrics when compared with traditional and DL methods, respectively. To compare soft-tissue contrast, intensity profile along tumor and tumor contours of the fusion methods were evaluated. MedFusionGAN provides a more consistent, better intensity profile, and a better segmentation performance. Conclusions: The proposed end-to-end unsupervised method successfully fused MRI and CT images. The fused image could improve targets and OARs delineation, which is an important aspect of radiotherapy treatment planning. [ABSTRACT FROM AUTHOR]

Background: In pediatric radiotherapy treatment planning of abdominal tumors, dose constraints to the pancreatic tail/spleen are applied to reduce late toxicity. In this study, an analysis of inter- and intrafraction motion of the pancreatic tail/spleen is performed to estimate the potential benefits of online MRI-guided radiotherapy (MRgRT). Materials and methods: Ten randomly selected neuroblastoma patients (median age: 3.4 years), irradiated with intensity-modulated arc therapy at our department (prescription dose: 21.6/1.8 Gy), were retrospectively evaluated for inter- and intrafraction motion of the pancreatic tail/spleen. Three follow-up MRIs (T2- and T1-weighted ± gadolinium) were rigidly registered to a planning CT (pCT), on the vertebrae around the target volume. The pancreatic tail/spleen were delineated on all MRIs and pCT. Interfraction motion was defined as a center of gravity change between pCT and T2-weighted images in left-right (LR), anterior-posterior (AP) and cranial-caudal (CC) direction. For intrafraction motion analysis, organ position on T1-weighted ± gadolinium was compared to T2-weighted. The clinical radiation plan was used to estimate the dose received by the pancreatic tail/spleen for each position. Results: The median (IQR) interfraction motion was minimal in LR/AP, and largest in CC direction; pancreatic tail 2.5 mm (8.9), and spleen 0.9 mm (3.9). Intrafraction motion was smaller, but showed a similar motion pattern (pancreatic tail, CC: 0.4 mm (1.6); spleen, CC: 0.9 mm (2.8)). The differences of Dmean associated with inter- and intrafraction motions ranged from − 3.5 to 5.8 Gy for the pancreatic tail and − 1.2 to 3.0 Gy for the spleen. In 6 out of 10 patients, movements of the pancreatic tail and spleen were highlighted as potentially clinically significant because of ≥ 1 Gy dose constraint violation. Conclusion: Inter- and intrafraction organ motion results into unexpected constrain violations in 60% of a randomly selected neuroblastoma cohort, supporting further prospective exploration of MRgRT. [ABSTRACT FROM AUTHOR]

Objective: We apply the superiorization methodology to the constrained intensity-modulated radiation therapy (IMRT) treatment planning problem. Superiorization combines a feasibility-seeking projection algorithm with objective function reduction: The underlying projection algorithm is perturbed with gradient descent steps to steer the algorithm towards a solution with a lower objective function value compared to one obtained solely through feasibility-seeking. Approach: Within the open-source inverse planning toolkit matRad, we implement a prototypical algorithmic framework for superiorization using the well-established Agmon, Motzkin, and Schoenberg (AMS) feasibility-seeking projection algorithm and common nonlinear dose optimization objective functions. Based on this prototype, we apply superiorization to intensity-modulated radiation therapy treatment planning and compare it with (i) bare feasibility-seeking (i.e., without any objective function) and (ii) nonlinear constrained optimization using first-order derivatives. For these comparisons, we use the TG119 water phantom, the head-and-neck and the prostate patient of the CORT dataset. Main results: Bare feasibility-seeking with AMS confirms previous studies, showing it can find solutions that are nearly equivalent to those found by the established piece-wise least-squares optimization approach. The superiorization prototype solved the linearly constrained planning problem with similar dosimetric performance to that of a general-purpose nonlinear constrained optimizer while showing smooth convergence in both constraint proximity and objective function reduction. Significance: Superiorization is a useful alternative to constrained optimization in radiotherapy inverse treatment planning. Future extensions with other approaches to feasibility-seeking, e.g., with dose-volume constraints and more sophisticated perturbations, may unlock its full potential for high performant inverse treatment planning. [ABSTRACT FROM AUTHOR]

Simple Summary: The authors conducted this research to improve the prediction of how patients with esophageal cancer respond to a treatment called neoadjuvant chemoradiotherapy, which can enhance survival rates. However, doctors struggle to accurately predict how well a patient will respond to this treatment using existing imaging methods. To address this, the researchers developed a computer-based method called DCRNet, that not only analyzes medical images but also considers the distribution of radiation therapy doses on the radiotherapy treatment plans to make more accurate predictions. They tested this method on 80 patients with esophageal cancer and found that the HRNetV2p model with DCR performed the best, significantly improving prediction accuracy compared to other models. This breakthrough has the potential to help doctors better anticipate patient responses to treatment, which could lead to more personalized and effective care, and improving the treatment planning of radiotherapy. Esophageal cancer is a deadly disease, and neoadjuvant chemoradiotherapy can improve patient survival, particularly for patients achieving a pathological complete response (ypCR). However, existing imaging methods struggle to accurately predict ypCR. This study explores computer-aided detection methods, considering both imaging data and radiotherapy dose variations to enhance prediction accuracy. It involved patients with node-positive esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy and surgery, with data collected from 2014 to 2017, randomly split into five subsets for 5-fold cross-validation. The algorithm DCRNet, an advanced version of OCRNet, integrates RT dose distribution into dose contextual representations (DCR), combining dose and pixel representation with ten soft regions. Among the 80 enrolled patients (mean age 55.68 years, primarily male, with stage III disease and middle-part lesions), the ypCR rate was 28.75%, showing no significant demographic or disease differences between the ypCR and non-ypCR groups. Among the three summarization methods, the maximum value across the CTV method produced the best results with an AUC of 0.928. The HRNetV2p model with DCR performed the best among the four backbone models tested, with an AUC of 0.928 (95% CI, 0.884–0.972) based on 5-fold cross-validation, showing significant improvement compared to other models. This underscores DCR-equipped models' superior AUC outcomes. The study highlights the potential of dose-guided deep learning in ypCR prediction, necessitating larger, multicenter studies to validate the results. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the clinical feasibility of the Siemens Healthineers AI‐Rad Companion Organs RT VA30A (Organs‐RT) auto‐contouring algorithm for organs at risk (OARs) of the pelvis, thorax, and head and neck (H&N). Methods: Computed tomography (CT) datasets from 30 patients (10 pelvis, 10 thorax, and 10 H&N) were collected. Four sets of OARs were generated on each scan, one set by Organs‐RT and the others by three experienced users independently. A physician (expert) then evaluated each contour by assigning a score from the following scale: 1‐Must Redo, 2‐Major Edits, 3‐Minor Edits, 4‐Clinically usable. Using the highest‐scored OAR from the human users as a reference, the contours generated by Organs‐RT were evaluated via Dice Similarity Coefficient (DSC), Hausdorff Distance (HDD), Mean Distance to Agreement (mDTA), Volume comparison, and visual inspection. Additionally, each human user recorded the time to delineate each structure set and time‐saving efficiency was measured. Results: The average DSC obtained for the pelvic OARs ranged between (0.81 ± 0.06)Rectum and (0.94 ± 0.03)Bladder. (0.75 ± 0.09)Esophagus to (0.96±0.02)Rt.Lung${({0.96 \pm 0.02})}_{{\mathrm{Rt}}.{\mathrm{\ Lung}}}$ for the thoracic OARs and (0.66 ± 0.07)Lips to (0.83 ± 0.04)Brainstem for the H&N. The average HDD in cm for the pelvis cohort ranged between (0.95 ± 0.35)Bladder to (3.62 ± 2.50)Rectum, (0.42 ± 0.06)SpinalCord to (2.09 ± 2.00)Esophagus for the thoracic set and (0.53±0.22)Cerv_SpinalCord${({0.53 \pm 0.22})}_{{\mathrm{Cerv}}\_{\mathrm{SpinalCord}}}$ to (1.50 ± 0.50)Mandible for the H&N region. The time‐saving efficiency was 67% for H&N, 83% for pelvis, and 84% for thorax. 72.5%, 82%, and 50% of the pelvis, thorax, and H&N OARs were scored as clinically usable by the expert, respectively. Conclusions: The highest agreement registered between OARs generated by Organs‐RT and their respective references was for the bladder, heart, lungs, and femoral heads, with an overall DSC≥0.92. The poorest agreement was for the rectum, esophagus, and lips, with an overall DSC⩽0.81. Nonetheless, Organs‐RT serves as a reliable auto‐contouring tool by minimizing overall contouring time and increasing time‐saving efficiency in radiotherapy treatment planning. [ABSTRACT FROM AUTHOR]

Background: Currently, radiation therapy treatment planning system intends biological optimization that relies heavily upon plan metrics from tumor control probability (TCP) and normal tissue complication probability (NTCP) modeling. Implementation and expansion of TCP and NTCP models with alternative data is an important step towards reliable radiobiological treatment planning. In this retrospective single institution study, the treatment charts of 139 lung cancer patients treated with chemo-radiotherapy were reviewed and correlated dosimetric predictors with the incidence of esophagitis and established NTCP model of esophagitis grade 1 and 2 for lung cancer patients. Methods: Esophagus is an organ at risk (OAR) in lung cancer radiotherapy (RT). Esophagitis is a common toxicity induced by RT. In this study, dose volume parameters Vx (Vx: percentage esophageal volume receiving ≥ x Gy) and mean esophagus dose (MED) as quantitative dose-volume metrics, the esophagitis grade 1 and 2 as endpoints, were reviewed and derived from the treatment planning system and the electronic medical record system. Statistical analysis of binary logistic regression and probit were performed to have correlated the probability of grade 1 and 2 esophagitis to MED and Vx. IBM SPSS software version 24 at 5% significant level (α = 0.05) was used in the statistical analysis. Results: The probabilities of incidence of grade 1 and 2 esophagitis proportionally increased with increasing the values of Vx and MED. V20, V30, V40, V50 and MED are statistically significant good dosimetric predictors of esophagitis grade 1. 50% incidence probability (TD50) of MED for grade 1 and 2 esophagitis were determined. Lyman Kutcher Burman model parameters, such as, n, m and TD50, were fitted and compared with other published findings. Furthermore, the sigmoid shaped dose responding curve between probability of esophagitis grade 1 and MED were generated respecting to races, gender, age and smoking status. Conclusions: V20, V30, V40 and V50 were added onto Quantitative Analysis of Normal Tissue Effects in the clinic, or QUANTEC group's dose constrains of V35, V50, V70 and MED. Our findings may be useful as both validation of 3-Dimensional planning era models and also additional clinical guidelines in treatment planning and plan evaluation using radiobiology optimization. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to assess the accuracy of Dolphin VTO prediction in patients requiring extraction treatment by comparing the VTO-predicted values and post-treatment values. Method: Digital pre and post-treatment lateral cephalograms of 52 patients having Class I bimaxillary protrusion were taken and transferred in Dolphin Imaging software for analysis and VTO prediction. A composite analysis was made containing 17 dental and soft tissue parameters (Holdaway, Mc Namara, Rakosi, Steiners, and Tweed analysis). Results: A comparison between post-treatment and VTO-predicted values showed that software was able to predict soft tissue changes accurately in regions such as nose, inferior sulcus depth, and chin region (p >.05). In dental changes, software was able to predict the inclination of upper incisors to Sn and palatal plane correctly (p >.05). Parameters for other areas such as upper and lower lip, position of upper and lower incisor show significant difference in their VTO and post-treatment values (p <.05) showing inaccuracy of the software. Conclusion: The VTO prediction of dental and soft tissue changes was significantly different from the post-treatment values for parameters such as superior sulcus depth, lower lip to h line, subnasal to h line, upper lip thickness, lip strain, nasolabial angle, interincisal angle, Ui-NA, Li-NB, and IMPA. For nasal prominence, inferior sulcus depth, chin thickness, UI-SN, and UI-PP, there was no significant difference seen. Since, for most of the parameters, a significant difference was found between the post-treatment and the predicted values, so it was inferred that the software-based VTO prediction could not be fully relied for any treatment planning. [ABSTRACT FROM AUTHOR]

If the physicists are not familiar with the optimizer of the system, how can physicists be able to oversee or direct the oART process and troubleshoot in real time? Radiotherapy (RT) is at crossroads, as the technological improvements are pivotal in improving its treatment efficacy. While physicists have diverse responsibilities ranging from quality assurance of treatment equipment to safe delivery of patient plans, dosimetrists are dedicated to treatment planning and therefore have time allocated solely to the intricate task of developing optimal treatment plans. [Extracted from the article]

Background: Advancements in deep‐learning based synthetic computed tomography (sCT) image conversion methods have enabled the development of magnetic resonance imaging (MRI)‐only based radiotherapy treatment planning (RTP) of the brain. Purpose: This study evaluates the clinical feasibility of a commercial, deep‐learning based MRI‐only RTP method with respect to dose calculation and patient positioning verification performance in RTP of the brain. Methods: Clinical validation of dose calculation accuracy was performed by a retrospective evaluation for 25 glioma and 25 brain metastasis patients. Dosimetric and image quality of the studied MRI‐only RTP method was evaluated by a direct comparison of the sCT‐based and computed tomography (CT)‐based external beam radiation therapy (EBRT) images and treatment plans. Patient positioning verification accuracy of sCT images was evaluated retrospectively for 10 glioma and 10 brain metastasis patients based on clinical cone‐beam computed tomography (CBCT) imaging. Results: An average mean dose difference of Dmean = 0.1% for planning target volume (PTV) and 0.6% for normal tissue (NT) structures were obtained for glioma patients. Respective results for brain metastasis patients were Dmean = 0.5% for PTVs and Dmean=1.0% for NTs. Global three‐dimensional (3D) gamma pass rates using 2%/2 mm dose difference and distance‐to‐agreement (DTA) criterion were 98.0% for the glioma subgroup, and 95.2% for the brain metastasis subgroup using 1%/1 mm criterion. Mean distance differences of <1.0 mm were observed in all Cartesian directions between CT‐based and sCT‐based CBCT patient positioning in both subgroups. Conclusions: In terms of dose calculation and patient positioning accuracy, the studied MRI‐only method demonstrated its clinical feasibility for RTP of the brain. The results encourage the use of the studied method as part of a routine clinical workflow. [ABSTRACT FROM AUTHOR]

Background and Purpose: Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving high sCT accuracies. However, existing sCT synthesis methods are often center-specific, posing a challenge to their generalizability. To overcome this limitation, recent studies have proposed approaches, such as multicenter training . Material and methods: The purpose of this work was to propose a multicenter sCT synthesis by DL, using a 2D cycle-GAN on 128 prostate cancer patients, from four different centers. Four cases were compared: monocenter cases, monocenter training and test on another center, multicenter trainings and a test on a center not included in the training and multicenter trainings with an included center in the test. Trainings were performed using 20 patients. sCT accuracy evaluation was performed using Mean Absolute Error, Mean Error and Peak-Signal-to-Noise-Ratio. Dose accuracy was assessed with gamma index and Dose Volume Histogram comparison. Results: Qualitative, quantitative and dose results show that the accuracy of sCTs for monocenter trainings and multicenter trainings using a seen center in the test did not differ significantly. However, when the test involved an unseen center, the sCT quality was inferior. Conclusions: The aim of this work was to propose generalizable multicenter training for MR-to-CT synthesis. It was shown that only a few data from one center included in the training cohort allows sCT accuracy equivalent to a monocenter study.

The present study was implemented to compare the dosimetric parameters of the target dose coverage and critical structures in the treatment planning of four radiotherapy techniques [namely, three-dimensional conformal radiation therapy (3D-CRT), intensity-modulated radiation therapy (IMRT), hybrid IMRT (h-IMRT) and volumetric-modulated arc therapy (VMAT)] for stage III non-small cell lung cancer (NSCLC) qualified plans for medical physicists, therapists and physicians. A total of 40 patients confirmed to have stage IIIA or IIIB NSCLC were enrolled, and four plans were designed for each patient. The prescription dose to the planning target volume (PTV) was assigned as 60 Gy in 30 fractions. The conformity index (CI), heterogeneity index (HI) and parameters of organs at risk (OARs) were calculated. For the PTV, the CI for VMAT was found to be the highest of all the four techniques (P<0.05), whereas the HI for the h-IMRT technique was found to be the lowest (P<0.05). Concerning the OARs, for the percentage of lung volume receiving a dose >5 Gy (lung V5), the highest value was obtained with VMAT (P<0.05), whereas for lung V30 and heart V30, the VMAT and IMRT techniques were found to be better compared with 3D-CRT and h-IMRT (P<0.05). For esophagus V50, the maximal dose (Dmax) and mean dose for the IMRT technique displayed the best results (P<0.05), and in the case of the spinal cord, the Dmax with VMAT showed a significant advantage over the other techniques (P<0.05). The treatment monitor units (MUs) in IMRT were found to be the largest (P<0.05), whereas the treatment time with VMAT was the shortest (P<0.05). For smaller PTVs, VMAT was the technique that provided the optimal dose distribution and sparing of the heart. Compared with 3D-CRT alone, adding 20% IMRT to the 3D-CRT base plan was shown to improve the plan quality, and IMRT and VMAT, as techniques, had better dose coverage and sparing of OARs. Furthermore, for patients in whom the lung V5 could be kept low enough, VMAT potentially offered a good alternative to the technique to IMRT, thereby offering additional possibilities for sparing of other OARs, and decreasing the MUs and treatment time. [ABSTRACT FROM AUTHOR]

Xuanfeng Ding, PhD I fully agree with my opponent that I "proton arc therapy does not deviate from the fundamental principles of the radiation therapy," i and the beauty of proton arc therapy is that our radiation oncology community could quickly adopt it for clinical use. Keywords: FLASH; Proton arc therapy EN FLASH Proton arc therapy 1 7 7 08/08/23 20230801 NES 230801 INTRODUCTION In the clinical practice of radiation oncology, we rely on precise dose calculation and deposition,[[1], [3]] high dose conformity[[4], [6]] to the target while lowering the dose to the adjacent Organs-At-Risk (OARs) as much as possible. The good part is that both developments will push our radiation treatment technology to its limits toward a new era of fast, efficient, precise, and effective personalized therapy. [Extracted from the article]

The use of multi‐modality imaging technologies such as CT, MRI, and PET imaging is state of the art for radiation therapy treatment planning. Except for a limited number of low magnetic field MR scanners the majority of such imaging technologies can only image the patient in a recumbent position. Delivering radiation therapy treatments with the patient in an upright orientation has many benefits and several companies are now developing upright patient positioners combined with upright diagnostic helical CT scanners to facilitate upright radiation therapy treatments. Due to the directional changes in the gravitational forces on the patient's body, most structures and organs will change position and shape between the recumbent and upright positions. Detailed knowledge about such structures and organs are therefore often only available in the recumbent position. The problem statement is therefore well defined, that is, how do we know where such structures and organs, that is, the target or region at risk volumes, are in the upright position if those cannot be identified and or delineated accurately enough using the upright diagnostic quality CT images only? This paper outlines two methods based on synthetic CT or MR images to overcome this problem. [ABSTRACT FROM AUTHOR]

Stereotactic radiotherapy (SRT) methods have become common for the treatment of small tumors in various parts of the body. Small field dosimetry has a unique set of challenges when it comes to the pre‐treatment validation of a radiotherapy plan that involves film dosimetry or high‐resolution detectors. Comparison of commercial quality assurance (QA) devices to the film dosimetry method for pre‐treatment evaluation of stereotactic radiosurgery (SRS), fractionated SRT, and stereotactic body radiation therapy treatment plans have been evaluated in this study. Forty stereotactic QA plans were measured using EBT‐XD film, IBA Matrixx Resolution, SNC ArcCHECK, Varian aS1200 EPID, SNC SRS MapCHECK, and IBA myQA SRS. The results of the commercial devices are compared to the EBT‐XD film dosimetry results for each gamma criteria. Treatment plan characteristics such as modulation factor and target volume were investigated for correlation with the passing rates. It was found that all detectors have greater than 95% passing rates at 3%/3 mm. Passing rates decrease rapidly for ArcCHECK and the Matrixx as criteria became more strict. In contrast, EBT‐XD film, SNC SRS MapCHECK, and IBA myQA SRS passing rates do not decline as rapidly when compared to Matrix Resolution, ArcCHECK, and the EPID. EBT‐XD film, SNC SRS MapCHECK, and IBA myQA SRS maintain greater than 90% passing rate at 2%/1 mm and greater than 80% at 1%/1 mm. Additionally, the ability of these devices to detect changes in dose distribution due to MLC positioning errors was investigated. Ten VMAT SBRT/SRS treatment plans were created with 6 MV FFF or 10 MV FFF beam energies using Eclipse 15.6. A MATLAB script was used to create two MLC positioning error scenarios from the original treatment plan. It was found that errors in MLC positioning were most reliably detected at 2%/1 mm for high‐resolution detectors and that lower‐resolution detectors did not consistently detect MLC positioning errors. [ABSTRACT FROM AUTHOR]