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Background: The advancements in artificial intelligence and computational power have made deep learning an attractive tool for radiotherapy treatment planning. Deep learning has the potential to significantly simplify the trial‐and‐error process involved in inverse planning required by modern treatment techniques such as volumetric modulated arc therapy (VMAT). In this study, we explore the ability of deep learning to predict organ‐at‐risk (OAR) dose–volume histograms (DVHs) of left‐sided breast cancer patients undergoing VMAT treatment based solely on their anatomical characteristics. The predicted DVHs could be used to derive patient‐specific dose constraints and dose objectives, streamlining the treatment planning process, standardizing the quality of the plans, and personalizing the treatment planning. Purpose: This study aimed to develop a deep learning‐based framework for the prediction of organ‐specific dose–volume histograms (DVH) based on structures delineated for left‐sided breast cancer treatment. Methods: We used a dataset of 249 left‐sided breast cancer patients treated with tangential VMAT fields. We extracted delineated structures and dose distributions for each patient and derived slice‐by‐slice DVHs for planning target volume (PTV) and organs‐at‐risk. The patients were divided into training (70%, n = 174), validation (10%, n = 24), and test (20%, n = 51) sets. Collected data were used to train a deep learning model for the prediction of the DVHs based on the delineated structures. The developed deep learning model comprised a modified DenseNet architecture followed by a recurrent neural network. Results: In the independent test set (n = 51), the point‐wise differences in the slice‐by‐slice DVHs between the clinical and predicted DVHs were small; the mean squared errors were 3.53, 1.58, 2.28, 3.37, and 1.44 [×10−4] for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. With the derived cumulative DVHs, the mean absolute difference ± standard deviation of mean doses between the clinical and the predicted DVH were 0.08 ± 0.04 Gy, 0.24 ± 0.22 Gy, 0.73 ± 0.46 Gy, 0.07 ± 0.06 Gy, and 0.14 ± 0.14 Gy for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. Conclusions: The deep learning‐based approach enabled automatic and reliable prediction of the DVH based on delineated structures. The predicted DVHs could potentially serve as patient‐specific clinical goals used to aid treatment planning and avoid suboptimal plans or to derive optimization objectives and constraints for automated treatment planning. [ABSTRACT FROM AUTHOR]

Background: Complexity metrics are mathematical quantities designed to quantify aspects of radiotherapy treatment plans that may affect both their deliverability and dosimetric accuracy. Despite numerous studies investigating their utility, there remains a notable absence of shared tools for their extraction. Purpose: This study introduces UCoMX (Universal Complexity Metrics Extractor), a software package designed for the extraction of complexity metrics from the DICOM‐RT plan files of radiotherapy treatments. Methods: UCoMX is developed around two extraction engines: VCoMX (VMAT Complexity Metrics Extractor) for VMAT/IMRT plans, and TCoMX (Tomotherapy Complexity Metrics Extractor) tailored for Helical Tomotherapy plans. The software, built using Matlab, is freely available in both Matlab‐based and stand‐alone versions. More than 90 complexity metrics, drawn from relevant literature, are implemented in the package: 43 for VMAT/IMRT and 51 for Helical Tomotherapy. Results: The package is designed to read DICOM‐RT plan files generated by most commercially available Treatment Planning Systems (TPSs), across various treatment units. A reference dataset containing VMAT, IMRT, and Helical Tomotherapy plans is provided to serve as a reference for comparing UCoMX with other in‐house systems available at other centers. Conclusion: UCoMX offers a straightforward solution for extracting complexity metrics from radiotherapy plans. Its versatility is enhanced through different versions, including Matlab‐based and stand‐alone, and its compatibility with a wide range of commercially available TPSs and treatment units. UCoMX presents a free, user‐friendly tool empowering researchers to compute the complexity of treatment plans efficiently. [ABSTRACT FROM AUTHOR]

The document provides an update on clinical practice guidelines for managing patients with sarcoma, rare and complex malignant tumors of bone and soft tissue. It highlights the importance of multidisciplinary expertise for optimal outcomes and discusses the need for equitable access to diagnosis and treatment for rare cancers. The guidelines focus on treatment at specialized sarcoma centers, management of retroperitoneal sarcoma, and pediatric patients with sarcoma. Recommendations emphasize timely referral to specialized centers to reduce local recurrence and surgical complications, improve limb conservation, and overall survival. The document encourages leveraging evidence-based recommendations to advocate for equitable access to specialized sarcoma care for all patients and families. [Extracted from the article]

Background Purpose Methods Results Conclusions Dose calculation in radiotherapy aims to accurately estimate and assess the dose distribution of a treatment plan. Monte Carlo (MC) dose calculation is considered the gold standard owing to its ability to accurately simulate particle transport in inhomogeneous media. However, uncertainties such as the patient's dynamically deforming anatomy can still lead to differences between the delivered and planned dose distribution.Development and validation of a deformable voxel geometry for MC dose calculations (DefVoxMC) to account for dynamic deformation in the dose calculation process of photon‐ and electron‐based radiotherapy treatment plans for clinically motivated cases.DefVoxMC relies on the subdivision of a regular voxel geometry into dodecahedrons. It allows shifting the dodecahedrons’ corner points according to the deformation in the patient's anatomy using deformation vector fields (DVF). DefVoxMC is integrated into the Swiss Monte Carlo Plan (SMCP) to allow the MC dose calculation of photon‐ and electron‐based treatment plans on the deformable voxel geometry. DefVoxMC is validated in two steps. A compression test and a Fano test are performed in silico. Delta4 (for photon beams) and EBT4 film measurements in a cubic PMMA phantom (for electron beams) are performed on a TrueBeam in Developer Mode for clinically motivated treatment plans. During these measurements, table motion is used to mimic rigid dynamic patient motion. The measured and calculated dose distributions are compared using gamma passing rate (GPR) (3% / 2 mm (global), 10% threshold). DefVoxMC is used to study the impact of patient‐recorded breathing motion on the dose distribution for clinically motivated lung and breast cases, each prescribed 50 Gy to 50% of the target volume. A volumetric modulated arc therapy (VMAT) and an arc mixed‐beam radiotherapy (Arc‐MBRT) plan are created for the lung and breast case, respectively. For the dose calculation, the dynamic deformation of the patient's anatomy is described by DVFs obtained from deformable image registration of the different phases of 4DCTs. The resulting dose distributions are compared to the ones of the static situation using dose–volume histograms and dose differences.DefVoxMC is successfully integrated into the SMCP to enable the MC dose calculation of photon‐ and electron‐based treatments on a dynamically deforming patient anatomy. The compression and the Fano test agree within 1.0% and 0.1% with the expected result, respectively. Delta4 and EBT4 film measurements agree with the calculated dose by a GPR >95%. For the clinically motivated cases, breathing motion resulted in areas with a dose increase of up to 26.9 Gy (lung) and up to 7.6 Gy (breast) compared to the static situation. The largest dose differences are observed in high‐dose‐gradient regions perpendicular to the beam plane, consequently decreasing the planning target volume coverage (V95%) by 4.2% for the lung case and 2.0% for the breast case.A novel method for MC dose calculation for photon‐ and electron‐based treatments on dynamically deforming anatomy is successfully developed and validated. Applying DefVoxMC to clinically motivated cases, we found that breathing motion has non‐negligible impact on the dosimetric plan quality. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions While electron beams of up to 20 MeV are commonly used in radiotherapy, the use of very‐high‐energy electrons (VHEEs) in the range of 100–200 MeV is now becoming a realistic option thanks to the recent advancements in accelerator technology. Indeed, VHEE offers several clinically attractive features and can be delivered using various conformation methods (including scanning, collimation, and focussing) at ultra‐high dose rates. To date, there is a lack of research tools for fast simulation of treatment plans using VHEE beams.This work aims to implement and validate a simple and fast dose calculation algorithm based on the Fermi–Eyges theory of multiple Coulomb scattering for VHEE radiation therapy, with energies up to 200 MeV. A treatment planning system (TPS) toolkit with VHEE modality would indeed allow for further preclinical investigations, including treatment plan optimization and evaluation, and thus contribute to the gradual introduction of VHEE radiotherapy in clinical practice.A VHEE pencil beam scanning double Gaussian model was introduced into the open‐source TPS matRad environment along with new functions and options dedicated to VHEE dose calculations. Various geometries and field configurations were then calculated in matRad (up to 200 MeV and 15 × 15 cm2, with complex bone or lung heterogeneities) and the results were compared to Monte Carlo simulations in the TOPAS/Geant4 toolkit. Two types of beam model (divergent or focused) were also tested. Examples of clinical treatment plans were computed, and the results were compared between the two codes.VHEE modality was fully implemented in matRad with GUI capabilities while preserving all original TPS features. New relevant options such as the importation of specific spot‐lists or adjustment of the lateral dose calculation cutoff to optimize the calculation speed were validated. Single spot and square field dose distributions were validated in water alone as well as in clinically relevant inhomogeneities. Dose maps from the VHEE model in matRad were in good agreement with TOPAS (2D gamma index [2%/1 mm] with passing rates superior to 90%, <6% mean dose differences), except for large interface heterogeneities.This work describes the implementation of a simple but efficient VHEE simulation model in matRad. A few configurations were studied in order to validate the model against accurate Monte Carlo simulations, demonstrating its usefulness for carrying out preliminary studies involving VHEE radiotherapy. [ABSTRACT FROM AUTHOR]

Background: High‐quality 3D‐anatomy of the day is needed for treatment plan adaptation in radiotherapy. For online x‐ray‐based CBCT workflows, one approach is to create a synthetic CT or to utilize a fan‐beam CT with corresponding registrations. The former potentially introduces uncertainties in the dose calculation if deformable image registration is used. The latter can introduce burden and complexity to the process, the facility, and the patient. Purpose: Using the CBCT of the day, acquired on the treatment device, for direct dose calculation and plan adaptation can overcome these limitations. This study aims to assess the accuracy of the calculated dose on the CBCT scans acquired on a Halcyon linear accelerator equipped with HyperSight. Methods: HyperSight's new CBCT reconstruction algorithm includes improvements in scatter correction, HU calibration of the imager, and beam shape adaptation. Furthermore, HyperSight introduced a new x‐ray detector. To show the effect of the implemented improvements, gamma comparisons of 2%/2 mm, 2%/1 mm, and 1%/1 mm were made between the dose distribution in phantoms calculated on the CBCT reconstructions and the simulation CT scans, considering this the standard of care. The resulting gamma passing rates were compared to those obtained with the Halcyon 3.0 reconstruction and hardware without HyperSight's technologies. Various anatomical phantoms for dosimetric evaluations on brain, head and neck, lung, breast, and prostate cases have been used in this study. Results: The overall results demonstrated that HyperSight outperformed the Halcyon 3.0 version. Based on the gamma analysis, the calculated dose using HyperSight was closer to the CT scan‐based doses than the calculated dose using iCBCT Halcyon 3.0 for most cases. Over all plans and gamma criteria, Halcyon 3.0 achieved an average passing rate of 92.9%, whereas HyperSight achieved 98.1%. Conclusion: Using HyperSight CBCT images for direct dose calculation, for example, in (online) plan adaptation, seems feasible for the investigated cases. [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]

Normal tissue tolerance dose limits to the heart have been established to reduce the risk of radiation‐induced cardiac disease (RICD). Dose constraints have been developed based on either the mean dose delivered to the whole heart (MHD) or the dose delivered to a specific volume, for example, volume of heart receiving equal to or greater than 30 Gy (V30). There is increasing evidence that the impact of thoracic radiation on cardiac morbidity and mortality has been underestimated. Consequently, there is a need to reduce the dose delivered to the heart in radical radiotherapy treatment planning. The pathophysiology of RICD may relate to dose to specific cardiac substructures (CS) rather than the traditionally observed MHD for common toxicities. The MHD or V30 Gy threshold dose rarely represents the true dose delivered to individual CS. Studies have shown the dose to specific areas may be more strongly correlated with overall survival (OS). With advances in modern radiotherapy techniques, it is vital that we develop robust, evidence‐based dose limits for CS, to fully understand and reduce the risk of RICD, particularly in high‐risk populations with cardiac risk factors. The following review will summarise the existing evidence of dose limits to CS, explain how dose limits may vary according to different disease sites or radiation techniques and propose how radiotherapy plans can be optimised to reduce the dose to these CS in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Stereotactic body radiation therapy (SBRT) is known to modulate the immune system and contribute to the generation of anti‐tumor T cells and stimulate T cell infiltration into tumors. Radiation‐induced immune suppression (RIIS) is a side effect of radiation therapy that can decrease immunological function by killing naive T cells as well as SBRT‐induced newly created effector T cells, suppressing the immune response to tumors and increasing susceptibility to infections. Purpose: RIIS varies substantially among patients and it is currently unclear what drives this variability. Models that can accurately predict RIIS in near real time based on treatment plan characteristics would allow treatment planners to maintain current protocol specific dosimetric criteria while minimizing immune suppression. In this paper, we present an algorithm to predict RIIS based on a model of circulating blood using early stage lung cancer patients treated with SBRT. Methods: This Python‐based algorithm uses DICOM data for radiation therapy treatment plans, dose maps, patient CT data sets, and organ delineations to stochastically simulate blood flow and predict the doses absorbed by circulating lymphocytes. These absorbed doses are used to predict the fraction of lymphocytes killed by a given treatment plan. Finally, the time dependence of absolute lymphocyte count (ALC) following SBRT is modeled using longitudinal blood data up to a year after treatment. This model was developed and evaluated on a cohort of 64 patients with 10‐fold cross validation. Results: Our algorithm predicted post‐treatment ALC with an average error of 0.24±0.21×109$0.24 \pm 0.21 \times {10}^9$ cells/L with 89% of the patients having a prediction error below 0.5 × 109 cells/L. The accuracy was consistent across a wide range of clinical and treatment variables. Our model is able to predict post‐treatment ALC < 0.8 (grade 2 lymphopenia), with a sensitivity of 81% and a specificity of 98%. This model has a ∼38‐s end‐to‐end prediction time of post treatment ALC. Conclusion: Our model performed well in predicting RIIS in patients treated using lung SBRT. With near‐real time model prediction time, it has the capability to be interfaced with treatment planning systems to prospectively reduce immune cell toxicity while maintaining national SBRT conformity and plan quality criteria. [ABSTRACT FROM AUTHOR]

Radiotherapy treatment planning (RTP) requires both magnetic resonance (MR) and computed tomography (CT) modalities. However, conducting separate MR and CT scans for patients leads to misalignment, increased radiation exposure, and higher costs. To address these challenges and mitigate the limitations of supervised synthesis methods, we propose a novel unsupervised perceptual attention image synthesis model based on cycleGAN (PA‐cycleGAN). The innovation of PA‐cycleGAN lies in its model structure, which incorporates dynamic feature encoding and deep feature extraction to improve the understanding of image structure and contextual information. To ensure the visual authenticity of the synthetic images, we design a hybrid loss function that incorporates perceptual constraints using high‐level features extracted by deep neural networks. Our PA‐cycleGAN achieves notable results, with an average peak signal‐to‐noise ratio (PSNR) of 28.06, structural similarity (SSIM) of 0.95, and mean absolute error (MAE) of 46.90 on a pelvic dataset. Additionally, we validate the generalization of our method by conducting experiments on an additional head dataset. These experiments demonstrate that PA‐cycleGAN consistently outperforms other state‐of‐the‐art methods in both quantitative metrics and image synthesis quality. [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]

Purpose: Radiotherapy treatment planning (RTP) using MR has been used increasingly for the abdominal site. Multiple contrast weightings and motion‐resolved imaging are desired for accurate delineation of the target and various organs‐at‐risk and patient‐tailored planning. Current MR protocols achieve these through multiple scans with distinct contrast and variable respiratory motion management strategies and acquisition parameters, leading to a complex and inaccurate planning process. This study presents a standalone MR Multitasking (MT)–based technique to produce volumetric, motion‐resolved, multicontrast images for abdominal radiotherapy treatment planning. Methods: The MT technique resolves motion and provides a wide range of contrast weightings by repeating a magnetization‐prepared (saturation recovery and T2 preparations) spoiled gradient‐echo readout series and adopting the MT image reconstruction framework. The performance of the technique was assessed through digital phantom simulations and in vivo studies of both healthy volunteers and patients with liver tumors. Results: In the digital phantom study, the MT technique presented structural details and motion in excellent agreement with the digital ground truth. The in vivo studies showed that the motion range was highly correlated (R2 = 0.82) between MT and 2D cine imaging. MT allowed for a flexible contrast‐weighting selection for better visualization. Initial clinical testing with interobserver analysis demonstrated acceptable target delineation quality (Dice coefficient = 0.85 ± 0.05, Hausdorff distance = 3.3 ± 0.72 mm). Conclusion: The developed MT‐based, abdomen‐dedicated technique is capable of providing motion‐resolved, multicontrast volumetric images in a single scan, which may facilitate abdominal radiotherapy treatment planning. [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]

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: Preliminary data have shown a close association of the generalized ionization cluster size dose (in short, cluster dose) with cell survival, independent of particle type, and energy, when cluster dose is derived from an ionization detail parameter preferred for its association with cell survival. Such results suggest cluster dose has the potential to replace RBE‐weighted dose in proton and ion beam radiotherapy treatment plan optimization, should a uniform cluster dose lead to comparable biological effects. However, further preclinical investigations are warranted to confirm this premise. Purpose: To present an analytical approach to create uniform cluster dose spread‐out Bragg peaks (SOBP) for evaluation of the potential of cluster dose to result in uniform biological effect. Methods: We modified the coefficients of the Bortfeld and Schlegel weight formula, an analytical method typically used for the creation of radiation dose SOBP in particle therapy, to produce uniform cluster dose SOBP of different widths (1–5 cm) at relevant clinical proton and carbon beam energies. Optimum parameters were found by minimization of the ratio between the maximum and minimum cluster dose in the SOBP region using the Nelder–Mead method. Results: The coefficients of the Bortfeld and Schlegel weight formula leading to uniform cluster dose SOBPs were determined for each combination of beam energy and SOBP width studied. The uniformity of the resulting cluster dose SOBPs, calculated as the relative difference between the maximum and minimum cluster dose within the SOBP, was within 0.3%–3.5% for the evaluated proton beams and 1.3%–3.4% for the evaluated carbon beams. Conclusions: The modifications to the analytical approach to create radiation dose SOBPs resulted in uniform cluster dose proton and carbon SOBPs over a wide range of beam energies and SOBP widths. Such SOBPs should prove valuable in preclinical investigations for the selection of nanodosimetric quantities to be used in proton and ion therapy treatment planning. [ABSTRACT FROM AUTHOR]

Background: MR‐only radiotherapy treatment planning is an attractive alternative to conventional workflow, reducing scan time and ionizing radiation. It is crucial to derive the electron density map or synthetic CT (sCT) from MR data to perform dose calculations to enable MR‐only treatment planning. Automatic segmentation of relevant organs in MR images can accelerate the process by preventing the time‐consuming manual contouring step. However, the segmentation label is available only for CT data in many cases. Purpose: We propose CycleSeg, a unified framework that generates sCT and corresponding segmentation from MR images without access to MR segmentation labels Methods: CycleSeg utilizes the CycleGAN formulation to perform unpaired synthesis of sCT and image alignment. To enable MR (sCT) segmentation, CycleSeg incorporates unsupervised domain adaptation by using a pseudo‐labeling approach with feature alignment in semantic segmentation space. In contrast to previous approaches that perform segmentation on MR data, CycleSeg could perform segmentation on both MR and sCT. Experiments were performed with data from prostate cancer patients, with 78/7/10 subjects in the training/validation/test sets, respectively. Results: CycleSeg showed the best sCT generation results, with the lowest mean absolute error of 102.2 and the lowest Fréchet inception distance of 13.0. CycleSeg also performed best on MR segmentation, with the highest average dice score of 81.0 and 81.1 for MR and sCT segmentation, respectively. Ablation experiments confirmed the contribution of the proposed components of CycleSeg. Conclusion: CycleSeg effectively synthesized CT and performed segmentation on MR images of prostate cancer patients. Thus, CycleSeg has the potential to expedite MR‐only radiotherapy treatment planning, reducing the prescribed scans and manual segmentation effort, and increasing throughput. [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]

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]

Background: Accurate and automated segmentation of thoracic organs‐at‐risk (OARs) is critical for radiotherapy treatment planning of thoracic cancers. However, this has remained a challenging task for four major reasons: (1) thoracic OARs have diverse morphologies; (2) thoracic OARs have low contrast with the background; (3) boundaries of thoracic OARs are blurry; (4) class imbalance issue caused by small organs. Purpose: To overcome the above challenges and achieve accurate and automated segmentation of thoracic OARs on thoracic CT. Methods: A novel cascaded framework based on mixed attention and multiscale information for thoracic OARs segmentation, called Cascaded‐TOARNet. This cascaded framework comprises two stages: localization and segmentation. During the localization stage, TOARNet locates each organ to crop the regions of interest (ROIs). During the segmentation stage, TOARNet accurately segments the ROIs, and the segmentation results are merged into a complete result. Results: We evaluated our proposed method and other common segmentation methods on two public datasets: the AAPM Thoracic Auto‐Segmentation Challenge dataset and the Segmentation of Thoracic Organs at Risk (SegTHOR) dataset. Our method demonstrated superior performance, achieving a mean Dice score of 92.6% on the SegTHOR dataset and 90.8% on the AAPM dataset. Conclusions: This segmentation method holds great promise as an essential tool for enhancing the efficiency of thoracic radiotherapy planning. [ABSTRACT FROM AUTHOR]

Background and purpose: Magnetic resonance imaging (MRI)‐based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error‐prone image registration, ultimately reducing patient radiation dose and setup uncertainty. In this work, we propose a MRI‐to‐CT transformer‐based improved denoising diffusion probabilistic model (MC‐IDDPM) to translate MRI into high‐quality sCT to facilitate radiation treatment planning. Methods: MC‐IDDPM implements diffusion processes with a shifted‐window transformer network to generate sCT from MRI. The proposed model consists of two processes: a forward process, which involves adding Gaussian noise to real CT scans to create noisy images, and a reverse process, in which a shifted‐window transformer V‐net (Swin‐Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise‐free CT scans. With an optimally trained Swin‐Vnet, the reverse diffusion process was used to generate noise‐free sCT scans matching MRI anatomy. We evaluated the proposed method by generating sCT from MRI on an institutional brain dataset and an institutional prostate dataset. Quantitative evaluations were conducted using several metrics, including Mean Absolute Error (MAE), Peak Signal‐to‐Noise Ratio (PSNR), Multi‐scale Structure Similarity Index (SSIM), and Normalized Cross Correlation (NCC). Dosimetry analyses were also performed, including comparisons of mean dose and target dose coverages for 95% and 99%. Results: MC‐IDDPM generated brain sCTs with state‐of‐the‐art quantitative results with MAE 48.825 ± 21.491 HU, PSNR 26.491 ± 2.814 dB, SSIM 0.947 ± 0.032, and NCC 0.976 ± 0.019. For the prostate dataset: MAE 55.124 ± 9.414 HU, PSNR 28.708 ± 2.112 dB, SSIM 0.878 ± 0.040, and NCC 0.940 ± 0.039. MC‐IDDPM demonstrates a statistically significant improvement (with p < 0.05) in most metrics when compared to competing networks, for both brain and prostate synthetic CT. Dosimetry analyses indicated that the target dose coverage differences by using CT and sCT were within ± 0.34%. Conclusions: We have developed and validated a novel approach for generating CT images from routine MRIs using a transformer‐based improved DDPM. This model effectively captures the complex relationship between CT and MRI images, allowing for robust and high‐quality synthetic CT images to be generated in a matter of minutes. This approach has the potential to greatly simplify the treatment planning process for radiation therapy by eliminating the need for additional CT scans, reducing the amount of time patients spend in treatment planning, and enhancing the accuracy of treatment delivery. [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: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning‐based auto‐segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning‐based auto‐segmentation approaches face two challenges in clinical practice: generalizability and human‐AI interaction. A generalizable and promptable auto‐segmentation model, which segments OARs of multiple disease sites simultaneously and supports on‐the‐fly human‐AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning. Purpose: Meta's segment anything model (SAM) was proposed as a generalizable and promptable model for next‐generation natural image segmentation. We further evaluated the performance of SAM in radiotherapy segmentation. Methods: Computed tomography (CT) images of clinical cases from four disease sites at our institute were collected: prostate, lung, gastrointestinal, and head & neck. For each case, we selected the OARs important in radiotherapy treatment planning. We then compared both the Dice coefficients and Jaccard indices derived from three distinct methods: manual delineation (ground truth), automatic segmentation using SAM's 'segment anything' mode, and automatic segmentation using SAM's 'box prompt' mode that implements manual interaction via live prompts during segmentation. Results: Our results indicate that SAM's segment anything mode can achieve clinically acceptable segmentation results in most OARs with Dice scores higher than 0.7. SAM's box prompt mode further improves Dice scores by 0.1∼0.5. Similar results were observed for Jaccard indices. The results show that SAM performs better for prostate and lung, but worse for gastrointestinal and head & neck. When considering the size of organs and the distinctiveness of their boundaries, SAM shows better performance for large organs with distinct boundaries, such as lung and liver, and worse for smaller organs with less distinct boundaries, like parotid and cochlea. Conclusions: Our results demonstrate SAM's robust generalizability with consistent accuracy in automatic segmentation for radiotherapy. Furthermore, the advanced box‐prompt method enables the users to augment auto‐segmentation interactively and dynamically, leading to patient‐specific auto‐segmentation in radiation therapy. SAM's generalizability across different disease sites and different modalities makes it feasible to develop a generic auto‐segmentation model in radiotherapy. [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]

Background: Proton radiotherapy treatment plans are currently restricted by the range uncertainties originating from the stopping power ratio (SPR) prediction based on single‐energy computed tomography (SECT). Various studies have shown that multi‐energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age‐related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection‐based MECT systems for SPR prediction. Purpose: To present and evaluate a novel proton SPR prediction method based on MD images from dual‐energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy. Methods: A theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non‐tissue materials, by minimizing the root‐mean‐square relative attenuation error in the energy interval from 40 to 140 keV. A model (here called MD‐SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data‐fitting. The goodness of fit of the MD‐SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head‐ and an abdomen‐sized phantom setup, were acquired with a clinical projection‐based fast‐kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non‐tissue materials in the phantoms, as well as used for input to the MD‐SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image‐based consecutive scan DECT for the same phantom setups. Results: The predicted SPRs of the tissue surrogates were similar for MD‐SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non‐adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between −0.7% and −1.8%. The SPRs of the non‐tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD‐SPR images, compared to 6.8% for DirectSPR. Conclusions: The MD‐SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non‐tissue materials. [ABSTRACT FROM AUTHOR]

Background: The use of synthetic computed tomography (CT) for radiotherapy treatment planning has received considerable attention because of the absence of ionizing radiation and close spatial correspondence to source magnetic resonance (MR) images, which have excellent tissue contrast. However, in an MR‐only environment, little effort has been made to examine the quality of synthetic CT images without using the original CT images. Purpose: To estimate synthetic CT quality without referring to original CT images, this study established the relationship between synthetic CT uncertainty and Bayesian uncertainty, and proposed a new Bayesian deep network for generating synthetic CT images and estimating synthetic CT uncertainty for MR‐only radiotherapy treatment planning. Methods and Materials: A novel deep Bayesian network was formulated using probabilistic network weights. Two mathematical expressions were proposed to quantify the Bayesian uncertainty of the network and synthetic CT uncertainty, which was closely related to the mean absolute error (MAE) in Hounsfield Unit (HU) of synthetic CT. These uncertainties were examined to demonstrate the accuracy of representing the synthetic CT uncertainty using a Bayesian counterpart. We developed a hybrid Bayesian architecture and a new data normalization scheme, enabling the Bayesian network to generate both accurate synthetic CT and reliable uncertainty information when probabilistic weights were applied. The proposed method was evaluated in 59 patients (13/12/32/2 for training/validation/testing/uncertainty visualization) diagnosed with prostate cancer, who underwent same‐day pelvic CT‐ and MR‐acquisitions. To assess the relationship between Bayesian and synthetic CT uncertainties, linear and non‐linear correlation coefficients were calculated on per‐voxel, per‐tissue, and per‐patient bases. For accessing the accuracy of the CT number and dosimetric accuracy, the proposed method was compared with a commercially available atlas‐based method (MRCAT) and a U‐Net conditional‐generative adversarial network (UcGAN). Results: The proposed model exhibited 44.33 MAE, outperforming UcGAN 52.51 and MRCAT 54.87. The gamma rate (2%/2 mm dose difference/distance to agreement) of the proposed model was 98.68%, comparable to that of UcGAN (98.60%) and MRCAT (98.56%). The per‐patient and per‐tissue linear correlation coefficients between the Bayesian and synthetic CT uncertainties ranged from 0.53 to 0.83, implying a moderate to strong linear correlation. Per‐voxel correlation coefficients varied from −0.13 to 0.67 depending on the regions‐of‐interest evaluated, indicating tissue‐dependent correlation. The R2 value for estimating MAE solely using Bayesian uncertainty was 0.98, suggesting that the uncertainty of the proposed model was an ideal candidate for predicting synthetic CT error, without referring to the original CT. Conclusion: This study established a relationship between the Bayesian model uncertainty and synthetic CT uncertainty. A novel Bayesian deep network was proposed to generate a synthetic CT and estimate its uncertainty. Various metrics were used to thoroughly examine the relationship between the uncertainties of the proposed Bayesian model and the generated synthetic CT. Compared with existing approaches, the proposed model showed comparable CT number and dosimetric accuracies. The experiments showed that the proposed Bayesian model was capable of producing accurate synthetic CT, and was an effective indicator of the uncertainty and error associated with synthetic CT in MR‐only workflows. [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]

Background: Automatic solutions for generating radiotherapy treatment plans using deep learning (DL) have been investigated by mimicking the voxel's dose. However, plan optimization using voxel‐dose features has not been extensively studied. Purpose: This study aims to investigate the efficiency of a direct optimization strategy with finite elements (FEs) after DL dose prediction for automatic intensity‐modulated radiation therapy (IMRT) treatment planning. Methods: A double‐UNet DL model was adapted for 220 cervical cancer patients (200 for training and 20 for testing), who underwent IMRT between 2016 and 2020 at our clinic. The model inputs were computed tomography (CT) slices, organs at risk (OARs), and planning target volumes (PTVs), and the outputs were dose distributions of uniformly generated high‐dose region‐controlled plans. The FEs were discretized into equal intervals of the dose prediction value within the [OARs avoid PTV(O‐P)] and [body avoids OARs & PTV(B‐OP)] regions in the test cohort and used to define the objectives for IMRT plan optimization. The plans were optimized using a two‐step process. In the beginning, the plans of two extra cases with and without low‐dose region control were compared to pursue robust and optimal dose adjustment degree pattern of FEs. In the first step, the mean dose of O‐P FEs were constrained to differing degrees according to the pattern. The further the FEs from the PTV, the tighter the constraints. In the second step, the mean dose of O‐P FEs from first step were constrained again but weakly and the dose of the B‐OP FEs from dose prediction and PTV were tightly regulated. The dosimetric parameters of the OARs and PTV were evaluated and compared using an interstep approach. In another 10 cases, the plans optimized via the aforementioned steps (method 1) were compared with those directly generated by the double‐UNet dose prediction model trained by low and high region‐controlled plans (method 2). Results: The mean differences in dose metrics between the UNet‐predicted dose and the clinical plans were: 0.47 Gy for bladder D50%; 0.62 Gy for rectum D50%; 0% for small intestine V30Gy; 1% for small intestine V40Gy; 4% for left femoral head V30Gy; and 6% for right femoral head V30Gy. The reductions in mean dose (p < 0.001) after FE‐based optimization were: 4.0, 1.9, 2.8, 5.9, and 5.7 Gy for the bladder, rectum, small intestine, left femoral head, and right femoral head, respectively, with flat PTV homogeneity and conformity. Method 1 plans produced lower mean doses than those of method 2 for the bladder (0.7 Gy), rectum (1.0 Gy), and small intestine (0.6 Gy), while maintaining PTV homogeneity and conformity. Conclusion: FE‐based direct optimization produced lower OAR doses and adequate PTV doses after DL prediction. This solution offers rapid and automatic plan optimization without manual adjustment, particularly in low‐dose regions. [ABSTRACT FROM AUTHOR]

Background: Automatic detection and segmentation of intraprostatic lesions (ILs) on preoperative multiparametric‐magnetic resonance images (mp‐MRI) can improve clinical workflow efficiency and enhance the diagnostic accuracy of prostate cancer and is an essential step in dominant intraprostatic lesion boost. Purpose: The goal is to improve the detection and segmentation accuracy of 3D ILs in MRI by a proposed a deep learning (DL)‐based algorithm with histopathological ground truth. Methods: This retrospective study included 262 patients with in vivo prostate biparametric MRI (bp‐MRI) scans and were divided into three cohorts based on their data analysis and annotation. Histopathological ground truth was established by using histopathology images as delineation reference standard on cohort 1, which consisted of 64 patients and was randomly split into 20 training, 12 validation, and 32 testing patients. Cohort 2 consisted of 158 patients with bp‐MRI based lesion delineation, and was randomly split into 104 training, 15 validation, and 39 testing patients. Cohort 3 consisted of 40 unannotated patients, used in semi‐supervised learning. We proposed a non‐local Mask R‐CNN and boosted its performance by applying different training techniques. The performance of non‐local Mask R‐CNN was compared with baseline Mask R‐CNN, 3D U‐Net and an experienced radiologist's delineation and was evaluated by detection rate, dice similarity coefficient (DSC), sensitivity, and Hausdorff Distance (HD). Results: The independent testing set consists of 32 patients with histopathological ground truth. With the training technique maximizing detection rate, the non‐local Mask R‐CNN achieved 80.5% and 94.7% detection rate; 0.548 and 0.604 DSC; 5.72 and 6.36 95 HD (mm); 0.613 and 0.580 sensitivity for ILs of all Gleason Grade groups (GGGs) and clinically significant ILs (GGG > 2), which outperformed baseline Mask R‐CNN and 3D U‐Net. For clinically significant ILs, the model segmentation accuracy was significantly higher than that of the experienced radiologist involved in the study, who achieved 0.512 DSC (p = 0.04), 8.21 (p = 0.041) 95 HD (mm), and 0.398 (p = 0.001) sensitivity. Conclusion: The proposed DL model achieved state‐of‐art performance and has the potential to help improve radiotherapy treatment planning and noninvasive prostate cancer diagnosis. [ABSTRACT FROM AUTHOR]

This systematic review study aims to provide comprehensive data on different radiobiological models, parameters, and endpoints used for calculating the normal tissue complication probability (NTCP) based on clinical data from head and neck cancer patients treated with conformal radiotherapy. A systematic literature search was carried out according to the PRISMA guideline for the identification of relevant publications in six electronic databases of Embase, PubMed, Scopus, and Google Scholar to July 2022 using specific keywords in the paper's title and abstract. The initial search resulted in 1368 articles for all organs for the review article about the NTCP parameters. One hundred and seventy‐eight articles were accepted for all organs with complete parameters for the mentioned models and finally, 20 head and neck cancer articles were accepted for review. Analysis of the studies shows that the Lyman‐Kutcher‐Burman (LKB) model properly links the NTCP curve parameters to the postradiotherapy endpoints. In the LKB model for esophagus, the minimum, and maximum corresponding parameters were reported as TD50 = 2.61 Gy with grade ≥3 radiation‐induced esophagitis endpoints as the minimum TD50 and TD50 = 68 Gy as the maximum ones. nmin = 0.06, nmax = 1.04, mmin = 0.1, and mmax = 0.65, respectively. Unfortunately, there was not a wide range of published articles on other organs at risk like ear or cauda equina except Burman et al. (Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys Ther. 1991;21:123–135). Findings suggest that the validation of different radiobiological models and their corresponding parameters need to be investigated in vivo and in vitro for developing a more accurate NTCP model to be used for radiotherapy treatment planning optimization. [ABSTRACT FROM AUTHOR]

Automatic segmentation is the most effective and fast method to plan radiotherapy treatment in thoracic computed tomography (CT), as manual segmentation takes time and produces variability in the results. This article proposes a hybrid HCIU method to segment the thoracic organs at risk (OAR) using prior localization techniques. The dataset of 40 Patients containing 7420 slices of thoracic CT has been used to perform the experiment on the Google Colab Pro+ framework. HCIU constitutes a hybrid deep learning model for features extraction, a K‐mean algorithm to form the clusters based on the extracted features, and the ensemble UNET InceptionV3 model trained on the data of each cluster for the segmentation of OAR. With HCIU, we achieved an average dice score of 92.05%, 97.39%, 95.75%, and 96.45% for the esophagus, heart, trachea, and aorta. The average sensitivity and specificity values achieved were 98.38% and 97.43% on the test dataset. Also, statistical tests have been applied to each organ's pixel values for analyzing the model's segmented results. A hybrid method has been proposed to improve the localization in thoracic OAR segmentation in CT. The proposed method achieved precise accuracy in segmenting the four organs compared with the other state‐of‐the‐art methods. [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: Adaptive radiotherapy (ART) can incorporate anatomical variations in a reoptimized treatment plan for fractionated radiotherapy. An automatic solution to objectively determine whether ART should be performed immediately after the daily image acquisition is highly desirable. Purpose: We investigate a quantitative criterion for whether ART should be performed in prostate cancer radiotherapy by synthesizing pseudo‐CT (sCT) images and evaluating dosimetric impact on treatment planning using deep learning approaches. Method and materials: Planning CT (pCT) and daily cone‐beam CT (CBCT) data sets of 74 patients are used to train (60 patients) and evaluate (14 patients) a cycle adversarial generative network (CycleGAN) that performs the task of synthesizing high‐quality sCT from daily CBCT. Automatic delineation (AD) of the bladder is performed on the sCT using the U‐net. The combination of sCT and AD allows us to perform dose calculations based on the up‐to‐date bladder anatomy to determine whether the original treatment plan (ori‐plan) is still applicable. For positive cases that the patients' anatomical changes and the associated dose calculations warrant re‐planning, we made rapid plan revisions (re‐plan) based on the ori‐plan. Results: The mean absolute error within the region‐of‐interests (i.e., body, bladder, fat, muscle) between the sCT and pCT are 41.2, 25.1, 26.5, and 29.0HU, respectively. Taking the calculated results of pCT doses as the standard, for PTV, the gamma passing rates of sCT doses at 1 mm/1%, 2 mm/2% are 87.92%, 98.78%, respectively. The Dice coefficients of the AD‐contours are 0.93 on pCT and 0.91 on sCT. According to the result of dose calculation, we found when the bladder volume underwent a substantial change (79.7%), the bladder dose is still within the safe limit, suggesting it is insufficient to solely use the bladder volume change as a criterion to determine whether adaptive treatment needs to be done. After AD‐contours of the bladder using sCT, there are two cases whose bladder dose Dmean>4000cGy${{\mathrm{D}}}_{{\mathrm{mean}}} > 4000{\mathrm{\ cGy}}$. For the two cases, we perform re‐planning to reduce the bladder dose to Dmean=3841cGy${{\mathrm{D}}}_{{\mathrm{mean}}} = 3841{\mathrm{\ cGy}}$, Dmean=3580cGy${{\mathrm{D}}}_{{\mathrm{mean}}} = 3580{\mathrm{\ cGy\ }}$under the condition that the PTV meets the prescribed dose. Conclusion: We provide a dose accurate adaptive workflow for prostate cancer patients by using deep learning approaches, and implement ART that adapts to bladder dose. Of note, the specific replanning criterion for whether ART needs to be performed can adapt to different centers' choices based on their experience and daily observations. [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: Deformable image registration is increasingly used in radiotherapy to adapt the treatment plan and accumulate the delivered dose. Consequently, clinical workflows using deformable image registration require quick and reliable quality assurance to accept registrations. Additionally, for online adaptive radiotherapy, quality assurance without the need for an operator to delineate contours while the patient is on the treatment table is needed. Established quality assurance criteria such as the Dice similarity coefficient or Hausdorff distance lack these qualities and also display a limited sensitivity to registration errors beyond soft tissue boundaries. Purpose: The purpose of this study is to investigate the existing intensity‐based quality assurance criteria structural similarity and normalized mutual information for their ability to quickly and reliably identify registration errors for (online) adaptive radiotherapy and compare them to contour‐based quality assurance criteria. Methods: All criteria were tested using synthetic and simulated biomechanical deformations of 3D MR images as well as manually annotated 4D CT data. The quality assurance criteria were scored for classification performance, for their ability to predict the registration error, and for their spatial information. Results: We found that besides being fast and operator‐independent, the intensity‐based criteria have the highest area under the receiver operating characteristic curve and provide the best input for models to predict the registration error on all data sets. Structural similarity furthermore provides spatial information with a higher gamma pass rate of the predicted registration error than commonly used spatial quality assurance criteria. Conclusions: Intensity‐based quality assurance criteria can provide the required confidence in decisions about using mono‐modal registrations in clinical workflows. They thereby enable automated quality assurance for deformable image registration in adaptive radiotherapy treatments. [ABSTRACT FROM AUTHOR]

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]

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]

Background: Delineation of Organs‐at‐Risks (OARs) is an important step in radiotherapy treatment planning. As manual delineation is time‐consuming, labor‐intensive and affected by inter‐ and intra‐observer variability, a robust and efficient automatic segmentation algorithm is highly desirable for improving the efficiency and repeatability of OAR delineation. Purpose: Automatic segmentation of OARs in medical images is challenged by low contrast, various shapes and imbalanced sizes of different organs. We aim to overcome these challenges and develop a high‐performance method for automatic segmentation of 10 OARs required in radiotherapy planning for brain tumors. Methods: A novel two‐stage segmentation framework is proposed, where a coarse and simultaneous localization of all the target organs is obtained in the first stage, and a fine segmentation is achieved for each organ, respectively, in the second stage. To deal with organs with various sizes and shapes, a stratified segmentation strategy is proposed, where a High‐ and Low‐Resolution Residual Network (HLRNet) that consists of a multiresolution branch and a high‐resolution branch is introduced to segment medium‐sized organs, and a High‐Resolution Residual Network (HRRNet) is used to segment small organs. In addition, a label fusion strategy is proposed to better deal with symmetric pairs of organs like the left and right cochleas and lacrimal glands. Results: Our method was validated on the dataset of MICCAI ABCs 2020 challenge for OAR segmentation. It obtained an average Dice of 75.8% for 10 OARs, and significantly outperformed several state‐of‐the‐art models including nnU‐Net (71.6%) and FocusNet (72.4%). Our proposed HLRNet and HRRNet improved the segmentation accuracy for medium‐sized and small organs, respectively. The label fusion strategy led to higher accuracy for symmetric pairs of organs. Conclusions: Our proposed method is effective for the segmentation of OARs of brain tumors, with a better performance than existing methods, especially on medium‐sized and small organs. It has a potential for improving the efficiency of radiotherapy planning with high segmentation accuracy. [ABSTRACT FROM AUTHOR]

Purpose: Variability in contouring contributes to large variations in radiation therapy planning and treatment outcomes. The development and testing of tools to automatically detect contouring errors require a source of contours that includes well‐understood and realistic errors. The purpose of this work was to develop a simulation algorithm that intentionally injects errors of varying magnitudes into clinically accepted contours and produces realistic contours with different levels of variability. Methods: We used a dataset of CT scans from 14 prostate cancer patients with clinician‐drawn contours of the regions of interest (ROI) of the prostate, bladder, and rectum. Using our newly developed Parametric Delineation Uncertainties Contouring (PDUC) model, we automatically generated alternative, realistic contours. The PDUC model consists of the contrast‐based DU generator and a 3D smoothing layer. The DU generator transforms contours (deformation, contraction, and/or expansion) as a function of image contrast. The generated contours undergo 3D smoothing to obtain a realistic look. After model building, the first batch of auto‐generated contours was reviewed. Editing feedback from the reviews was then used in a filtering model for the auto‐selection of clinically acceptable (minor‐editing) DU contours. Results: Overall, C values of 5 and 50 consistently produced high proportions of minor‐editing contours across all ROI compared to the other C values (0.936 ±$ \pm \;$0.111 and 0.552 ±$ \pm \;$0.228, respectively). The model performed best on the bladder, which had the highest proportion of minor‐editing contours (0.606) of the three ROI. In addition, the classification AUC for the filtering model across all three ROI is 0.724 ±$ \pm \;$0.109. Discussion: The proposed methodology and subsequent results are promising and could have a great impact on treatment planning by generating mathematically simulated alternative structures that are clinically relevant and realistic enough (i.e., similar to clinician‐drawn contours) to be used in quality control of radiation therapy. [ABSTRACT FROM AUTHOR]

Purpose: Stereotactic ablative body radiotherapy (SABR) in the liver, RTOG‐1112 guides the treatment modalities including the dose constraints for this technique but not the plan parameters. This study is not only analyzing the plan quality by utilizing the plan parameters and indexes but also compares treatment modalities from the protocol implementation. Method and material: Twenty‐five patients treated in the period from February 2020 to September 2022 were recruited in this analysis. Two planners randomly selected the patients and modalities. The modalities employed were Volumetric‐Modulated Arc Therapy (VMAT) and Helical Tomotherapy (HT). Various parameters and indexes were used to access not only the plan quality but also to compare each modality. The parameters and indexes studied were the homogeneity index (HI), conformity index (CI), gradient distance (GD), and the dose received by the organs at risk. Result: The data reveals that the mean volume of PTV is 60.8 ± 53.9 cc where these targets exhibit no significant difference between each modality. The HI shows a consistent value for both modalities. Between each modality, the CI value shows less deviation, but the HT shows slightly higher performance than VMAT. The value of GD is 1.5 ± 0.3 cm where the HT provides a shorter distance compared to VMAT as well. Conclusion: The parameters and indexes should be utilized for the plan evaluation although in the guidelines this was not required. Various modalities were employed for treatment. Both can achieve the treatment criteria with slightly low performance of VMAT. [ABSTRACT FROM AUTHOR]

Purpose: Radiation therapy treatment planning can be viewed as an iterative hyperparameter tuning process to balance conflicting clinical goals. In this work, we investigated the performance of modern Bayesian optimization (BO) methods on automated treatment planning problems in high‐dimensional settings. Methods: Twenty locally advanced rectal cancer patients treated with intensity‐modulated radiation therapy (IMRT) were retrospectively selected as test cases. The adjustable planning parameters included both dose objectives and their corresponding weights. We implemented an automated treatment planning framework and tested the performance of two BO methods on the treatment planning task: one standard BO method (Gaussian Process with Expected Improvement [GPEI]) and one BO method dedicated to high‐dimensional problems (Sparse Axis Aligned Subspace BO [SAAS‐BO]). Another derivative‐free method (Nelder–Mead simplex search) and the random tuning method were also included as baselines. The four automated methods' plan quality and planning efficiency were compared with the clinical plans regarding target coverage and organs at risk (OAR) sparing. The predictive models in both BO methods were compared to analyze the different search patterns of the two BO methods. Results: For the target structures, the SAAS‐BO plans achieved comparable hot spot control (p=0.43$p=0.43$) and homogeneity (p=0.96$p=0.96$) with the clinical plans, significantly better than the GPEI and Nelder–Mead plans (p<0.05$p < 0.05$). Both SAAS‐BO and GPEI plans significantly outperformed the clinical plans in conformity and dose spillage (p<0.05$p < 0.05$). Compared with the clinical plans, the treatment plans generated by the four automated methods all made reductions in evaluated dosimetric indices for the femoral head and the bladder. The Nelder–Mead plans achieved similar plan quality scores compared with the BO plans, but exhibited poorer control in the target hot spot and dose spillage. The analysis of the underlying predictive models has shown that both BO methods have identified similar sensitive planning parameters. Conclusions: This work implemented a BO‐based hyperparameter tuning framework for automated treatment planning. Both tested BO methods were able to produce high‐quality treatment plans and reduce the workload of treatment planners. The model analysis also confirmed the intrinsic low dimensionality of the tested treatment planning problems. [ABSTRACT FROM AUTHOR]

Purpose: To incorporate four‐dimensional computed tomography (4DCT)‐based ventilation imaging into intensity‐modulated radiation therapy (IMRT) treatment planning for pulmonary functional avoidance. Methods and Materials: Nineteen locally advanced lung cancer patients are retrospectively studied. 4DCT images are employed to create ventilation maps for each patient via a density‐change‐based algorithm with mass correction. The regional ventilation is directly incorporated into the mathematical formulation of a direct aperture optimization model in IMRT treatment planning to achieve functional avoidance and a voxel‐based treatment plan. The proposed functional avoidance planning and voxel‐based planning are compared to the conventional treatment planning approach purely based on the anatomy of patients. Paired sample t‐tests are conducted to see whether dosimetric differences among the three approaches are significant. Results: Similar planning target volume (PTV) coverage is achieved by anatomical, functional avoidance, and voxel‐based approaches. The voxel‐based treatment planning performs better than both functional avoidance and anatomical planning to the lung. For a total lung, the average volume reductions in a functional avoidance plan from an anatomical plan, a voxel‐based plan from an anatomical plan, and a voxel‐based plan from a functional avoidance plan are 7.0%, 16.8%, and 10.6%, respectively for V40; and 0.4%, 6.4%, and 6.0%, respectively for mean Lung Dose (MLD). For a functional lung, the reductions are 8.8%, 17.2%, and 9.2%, respectively, for fV40; and 1.1%, 6.2%, and 5.2%, respectively, for functional mean lung dose (fMLD). These reductions are obtained without significantly increasing doses to other organs‐at‐risk. All the pairwise treatment planning comparisons for both total lung and functional lung are statistically significant (p‐value <α=0.05$< \alpha =0.05$) except for the functional avoidance plan with the anatomical plan pair in which the p‐value >α=0.05$> \alpha =0.05$. From these results, we can conclude that voxel‐based treatment planning outperforms both anatomical and functional‐avoidance planning. Conclusions: We propose a treatment planning framework that directly utilizes functional images and compares voxel‐based treatment planning with functional avoidance and anatomical treatment planning. [ABSTRACT FROM AUTHOR]

Background: Manual contouring is very labor‐intensive, time‐consuming, and subject to intra‐ and inter‐observer variability. An automated deep learning approach to fast and accurate contouring and segmentation is desirable during radiotherapy treatment planning. Purpose: This work investigates an efficient deep‐learning‐based segmentation algorithm in abdomen computed tomography (CT) to facilitate radiation treatment planning. Methods: In this work, we propose a novel deep‐learning model utilizing U‐shaped multi‐layer perceptron mixer (MLP‐Mixer) and convolutional neural network (CNN) for multi‐organ segmentation in abdomen CT images. The proposed model has a similar structure to V‐net, while a proposed MLP‐Convolutional block replaces each convolutional block. The MLP‐Convolutional block consists of three components: an early convolutional block for local features extraction and feature resampling, a token‐based MLP‐Mixer layer for capturing global features with high efficiency, and a token projector for pixel‐level detail recovery. We evaluate our proposed network using: (1) an institutional dataset with 60 patient cases and (2) a public dataset (BCTV) with 30 patient cases. The network performance was quantitatively evaluated in three domains: (1) volume similarity between the ground truth contours and the network predictions using the Dice score coefficient (DSC), sensitivity, and precision; (2) surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMS); and (3) the computational complexity reported by the number of network parameters, training time, and inference time. The performance of the proposed network is compared with other state‐of‐the‐art networks. Results: In the institutional dataset, the proposed network achieved the following volume similarity measures when averaged over all organs: DSC = 0.912, sensitivity = 0.917, precision = 0.917, average surface similarities were HD = 11.95 mm, MSD = 1.90 mm, RMS = 3.86 mm. The proposed network achieved DSC = 0.786 and HD = 9.04 mm on the public dataset. The network also shows statistically significant improvement, which is evaluated by a two‐tailed Wilcoxon Mann–Whitney U test, on right lung (MSD where the maximum p‐value is 0.001), spinal cord (sensitivity, precision, HD, RMSD where p‐value ranges from 0.001 to 0.039), and stomach (DSC where the maximum p‐value is 0.01) over all other competing networks. On the public dataset, the network report statistically significant improvement, which is shown by the Wilcoxon Mann–Whitney test, on pancreas (HD where the maximum p‐value is 0.006), left (HD where the maximum p‐value is 0.022) and right adrenal glands (DSC where the maximum p‐value is 0.026). In both datasets, the proposed method can generate contours in less than 5 s. Overall, the proposed MLP‐Vnet demonstrates comparable or better performance than competing methods with much lower memory complexity and higher speed. Conclusions: The proposed MLP‐Vnet demonstrates superior segmentation performance, in terms of accuracy and efficiency, relative to state‐of‐the‐art methods. This reliable and efficient method demonstrates potential to streamline clinical workflows in abdominal radiotherapy, which may be especially important for online adaptive treatments. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) has proven value in radiotherapy treatment planning (RTP). MRI provides excellent soft tissue contrast, and improves lesion detection, definition and extent, allowing for increased conformal treatment. Recent installation of dedicated MRI simulators and MRI-guided linear accelerators (MR Linacs) within radiation oncology departments has led to a sudden and rapid expansion in the scope of practice for many radiation therapists and MRI radiographers. The lack of current recommendations, guidelines and credentialing for both MRI radiographers and radiation therapists working within these atypical MRI environments poses a significant challenge for the education and training of staff, and the safe operation of these units. This commentary discusses current pathways for radiographers and radiation therapists entering the emerging field of MRIguided radiation oncology, and the future role of the MRI radiographer in addressing the unique issues found in non-standard MRI environments. The authors draw on their collective experience as MRI radiographers assisting the rollout of dedicated MRI simulators in radiation oncology departments across Australia and reflect on the need for close collaboration between radiographers, radiation therapists and their respective departments. There is also a critical role for professional bodies to play in supporting existing and future roles in MRI and recognising advanced practitioner scope of practice. [ABSTRACT FROM AUTHOR]

Background: Contouring of internal gross target volume (iGTV) is an essential part of treatment planning in radiotherapy to mitigate the impact of intrafractional target motion. However, it is usually time-consuming and easily subjected to intra-observer and inter-observer variability.So far,few studies have been explored to directly predict iGTV by deep learning technique, because the iGTV contains not only the gross target volume (GTV) but also the motion information of the GTV. Purpose: This work was an exploratory study to present a deep learning-based framework to segment iGTV rapidly and accurately in 4D CT images for lung cancers. Methods: Five models, including 3D UNet, mmUNet with point-wise add merging approach (mmUNet-add), mmUNet with concatenate fusion strategy (mmUNet-cat), gruUNet with point-wise add fusion approach (gruUNet-add), and gruUNet with concatenate method (gruUNet-cat), were adopted for iGTV segmentation. All the models originated from the 3D UNet network, with multichannel multi-path and convolutional gated recurrent unit (GRU) added in the mmUNet and gruUNet networks, respectively. Seventy patients with lung cancers were collected and 55 cases were randomly selected as the training set, and 15 cases as the testing set. In addition, the segmentation results of the five models were compared with the ground truths qualitatively and quantitatively. Results: In terms of Dice Similarity Coefficient (DSC), the proposed four networks (mmUNet-add, mmUNet-cat, gruUNet-add, and gruUNet-cat) increased the DSC score of 3D UNet from 0.6945 to 0.7342, 0.7253, 0.7405, and 0.7365, respectively. However, the differences were not statistically significant (p > 0.05). After a simple post-processing to remove the small isolated connected regions, the mean 95th percentile Hausdorff distances (HD_95s) of the 3D UNet, mmUNet-add, mmUNet-cat, gruUNet-add, and gruUNet-cat networks were 19.70, 15.75, 15.84, 15.61, and 15.83 mm, respectively, corresponding to 25.35, 25.96, 25.11, 28.23, and 24.47 mm before the post-processing. With regard to runtime, significant elapsed time growths (about 70s and 230s) were observed both in the mmUNet and gruUNet architectures due to the increasing parameters. But the mmUNet structure showed less growth. Conclusion: Our study demonstrated the ability of the deep learning technique to predict iGTVs directly.With the introduction of multi-channel multi-path and convolutional GRU, the segmentation accuracy was improved under certain conditions with a reduced segmentation efficiency and a further research topic when the 3D UNet network would lead to poor performance is elicited. Less efficiency degradation was observed in the mmUNet structure. Besides, the element-wise add fusing strategy was favorable to increase DSC, whereas HD_95 benefited from the concentrate merging approach. Nevertheless, the segmentation accuracy by deep learning still remains to be improved. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate an automated whole-brain radiotherapy (WBRT) treatment planning pipeline with a deep learning–based autocontouring and customizable landmark-based field aperture design. Methods: The pipeline consisted of the following steps: (1) Auto-contour normal structures on computed tomography scans and digitally reconstructed radiographs using deep learning techniques, (2) locate the landmark structures using the beam’s-eye-view, (3) generate field apertures based on eight different landmark rules addressing different clinical purposes and physician preferences. Two parallel approaches for generating field apertures were developed for quality control. The performance of the generated field shapes and dose distributions were compared with the original clinical plans. The clinical acceptability of the plans was assessed by five radiation oncologists from four hospitals. Results: The performance of the generated field apertures was evaluated by the Hausdorff distance (HD) and mean surface distance (MSD) from 182 patients’ field apertures used in the clinic. The average HD and MSD for the generated field apertures were 16 ± 7 and 7 ± 3 mm for the first approach, respectively, and 17 ± 7 and 7 ± 3 mm, respectively, for the second approach. The differences regarding HD and MSD between the first and the second approaches were 1 ± 2 and 1 ± 3 mm, respectively. A clinical review of the field aperture design, conducted using 30 patients, achieved a 100% acceptance rate for both the first and second approaches, and the plan review achieved a 100% acceptance rate for the first approach and a 93% acceptance rate for the second approach.The average acceptance rate for meeting lens dosimetric recommendations was 80% (left lens) and 77% (right lens) for the first approach, and 70% (both left and right lenses) for the second approach, compared with 50% (left lens) and 53% (right lens) for the clinical plans. Conclusion: This study provided an automated pipeline with two field aperture generation approaches to automatically generate WBRT treatment plans. Both quantitative and qualitative evaluations demonstrated that our novel pipeline was comparable with the original clinical plans. [ABSTRACT FROM AUTHOR]