Volumetric Modulated Arc Therapy Research Articles

A comprehensive Study of the Out of Field Non Target Dose Associated with 6 and 10 MV Flattened and Flattening Filter Free X Ray Beam in a True Beam Linear Accelerator.

To evaluate the out-of-field dose associated with flattened (FF) and flattening filter-free (FFF) 6 and 10 MV X-ray beams in a TrueBeam linear accelerator (Linac). Measurements were taken in a slab phantom using the metal oxide semiconductor field effect transistor (MOSFET) detector at varying depths (dmax, 5 cm, and 10 cm) for clinically relevant field sizes and up to 30 cm from the field edges for 6 and 10 MV FF and FFF beams in TrueBeam Linac. Dose calculation accuracy of the analytic anisotropic algorithm (AAA) and Acuros algorithm was investigated in the out-of-field region. Similarly, the out-of-field dose associated with volumetric modulated arc therapy (VMAT) head-and-neck plan delivered to a body phantom was evaluated. The out-of-field dose for both FF and FFF photon beams (6 and 10 MV) decreased with increasing distance from the field boundary and size. Furthermore, regardless of FF in the field, higher-energy photon beams were associated with lower out-of-field dose. Both algorithms underestimated the dose in the out-of-field region, with AAA failing to calculate the out-of-field dose at 15 cm from the field edge and Acuros failing to calculate out-of-field radiation at 20 cm. At 5 cm from the field edge, an average of 50% underestimation was observed, and at 10 cm, an average of 60% underestimation was observed for both FF and FFF (6 and 10 MV) beams. The VMAT head-and-neck plan performed with the FFF beam resulted in a lower out-of-field dose than the FF beam for a comparable dose distribution. Compared with flattened beams, the FFF modes on TrueBeam Linac exhibited a clinically relevant reduction in the out-of-field dose. Further dosimetric studies are warranted to determine the significant benefit of FFF beams across different cancer sites.

Dosimetric Validation of Treatment Planning System for Volumetric Modulated Arc Therapy Using AAPM Medical Physics Practice Guideline 5.b.

To assess the precision of dose calculations for Volumetric Modulated Arc Therapy (VMAT) using megavoltage (MV) photon beams, we validated the accuracy of two algorithms: AUROS XB and Analytical Anisotropic Algorithm (AAA). This validation will encompass both flattening filter (FF) and flattening filter-free beam (FFF) modes, using AAPM Medical Physics Practice Guideline (MPPG 5b). VMAT validation tests were generated for 6 MV FF and 6 MV FFF beams using the AAA and AXB algorithms in the Eclipse V.15.1 treatment planning system (TPS). Corresponding measurements were performed on a linear accelerator using a diode detector and a radiation field analyzer. Point dose (PD) and in-vivo measurements were conducted using an A1SL ion chamber and (TLD) from Thermofisher, respectively. The Rando Phantom was employed for end-to-end (E2E) tests. The mean difference (MD) between the TPS-calculated values and the measured values for the PDD and output factors were within 1% and 0.5%, respectively, for both 6 MV FF and 6 MV FFF. In the TG 119 sets, the MD for PD with both AAA and AXB was <0.9%. For the TG 244 sets, the minimum, maximum, and mean deviations in PD for both 6 MV FF and 6 MV FFF beams were 0.3%, 1.4% and 0.8% respectively. In the E2E test, using the Rando Phantom, the MD between the TLD dose and the TPS dose was within 0.08% for both 6 MV FF (p=1.0) and 6 MV FFF (0.018) beams. The accuracy of the TPS and its algorithms (AAA and AXB) has been successfully validated. The recommended tests included in the VMAT/IMRT validation section proved invaluable for verifying the PDD, output factors, and the feasibility of complex clinical cases. E2E tests were instrumental in validating the entire workflow from CT simulation to treatment delivery.

TEAM participation in the irradiation of IROC phantoms for cooperative group clinical trials

The participation of radiation oncology team members in the irradiation of Imaging and Radiation Oncology Core (IROC) phantom for cooperative group clinical trials is essential to comply with the latest quality management philosophy. Medical dosimetrists are expected to develop treatment plans for the irradiation of IROC phantoms. For advanced treatment techniques, such as three-dimensional conformal radiation therapy (3DCRT), intensity-modulated radiation therapy (IMRT), and volumetric-modulated arc therapy (VMAT), the irradiation of the IROC phantoms serves as quality audit. If successful, the irradiation processes demonstrate that the institution has the knowledge of the protocol, and has the appropriate equipment to comply with the protocol requirements. This article describes three IROC phantoms used for credentialing external beam photon beam therapy, delivered using conventional medical linear accelerators, to the medical dosimetry community. Guidance and strategies for the development of treatment plans are discussed. Our institutional irradiation of the three IROC phantoms, delivered using the Truebeam medical linear accelerator, resulted in consistent dose accuracy to within ±1%. The participation of the team members may reduce the overall published failing rate stated to be about one-third of all participating institutions.

Dosimetric Comparison Between Proton and Photon Radiation Therapies for Pediatric Neuroblastoma

PurposeThe aim of this study is to determine the most beneficial radiation treatment technique for pediatric patients with thoracic and abdominal neuroblastoma (NBL), through a dosimetric comparison between photon Volumetric Modulated Arc Therapy and proton Intensity-Modulated Proton Therapy treatment plans. Materials and MethodsA retrospective analysis was conducted on a multicentre case series of 19 patients with thoracic and/or abdominal NBL who underwent radiation therapy, following the recommendations of the European protocol for high-risk NBL (HR-NBL2/SIOPEN). The prescribed dose was 21.6 Gy in 12 fractions (1.8 Gy/fraction) delivered over the preoperative disease volume. The dose volume histograms were analyzed for each patient, and a Wilcoxon signed-rank test with a significance level of 0.01 was employed to assess statistical differences between the dosimetric parameters investigated. Two homogeneity indices (HI and newHI) were compared to evaluate the uniformity in dose, delivered to the adjacent vertebrae (VBs_Adj). ResultsBoth radiation techniques conform to the protocol regarding CTV/PTV coverage for every location. Proton therapy resulted in statistically significant dose sparing for the heart and lungs in supradiaphragmatic locations and for the contralateral kidney, liver, spleen, and bowel in subdiaphragmatic locations. For both techniques, sparing the non-adjacent vertebrae (VBs_NAdj) results more challenging, although promising results were obtained. Furthermore, the dose delivered to the VBs_Adj was not statistically different, in terms of homogeneity, for the 2 radiation techniques that both met the protocol’s requirements. ConclusionThis dosimetric analysis highlights the potential of protons to reduce radiation dose to healthy tissue. These findings apply to all the investigated patients, regardless of primary tumor location, making proton therapy a valuable option for the treatment of neuroblastoma. However, a multidisciplinary assessment of each case is essential to ensure the selection of the most effective and suitable treatment modality.

260: Volumetric modulated arc therapy for early-stage gastric mucosa-associated lymphoid tissue lymphoma

260: Volumetric modulated arc therapy for early-stage gastric mucosa-associated lymphoid tissue lymphoma

3082: Volumetric modulated arc therapy for carotid sparing in node-positive breast cancer

3082: Volumetric modulated arc therapy for carotid sparing in node-positive breast cancer

LymphoDose: a lymphocyte dose estimation framework—application to brain radiotherapy

Objective. Severe radiation-induced lymphopenia occurs in 40% of patients treated for primary brain tumors and is an independent risk factor of poor survival outcomes. We developed an in-silico framework that estimates the radiation doses received by lymphocytes during volumetric modulated arc therapy brain irradiation. Approach. We implemented a simulation consisting of two interconnected compartmental models describing the slow recirculation of lymphocytes between lymphoid organs ( M1 ) and the bloodstream ( M2 ). We used dosimetry data from 33 patients treated with chemo-radiation for glioblastoma to compare three cases of the model, corresponding to different physical and biological scenarios: (H1) lymphocytes circulation only in the bloodstream i.e. circulation in M2 only; (H2) lymphocytes recirculation between lymphoid organs i.e. circulation in M1 and M2 interconnected; (H3) lymphocytes recirculation between lymphoid organs and deep-learning computed out-of-field (OOF) dose to head and neck (H&N) lymphoid structures. A sensitivity analysis of the model’s parameters was also performed. Main results. For H1, H2 and H3 cases respectively, the irradiated fraction of lymphocytes was 99.8 ± 0.7%, 40.4 ± 10.2% et 97.6 ± 2.5%, and the average dose to irradiated pool was 309.9 ± 74.7 mGy, 52.6 ± 21.1 mGy and 265.6 ± 48.5 mGy. The recirculation process considered in the H2 case implied that irradiated lymphocytes were irradiated in the field only 1.58 ± 0.91 times on average after treatment. The OOF irradiation of H&N lymphoid structures considered in H3 was an important contribution to lymphocytes dose. In all cases, the estimated doses are low compared with lymphocytes radiosensitivity, and other mechanisms could explain high prevalence of RIL in patients with brain tumors. Significance. Our framework is the first to take into account OOF doses and recirculation in lymphocyte dose assessment during brain irradiation. Our results demonstrate the need to clarify the indirect effects of irradiation on lymphopenia, in order to potentiate the combination of radio-immunotherapy or the abscopal effect.

Clinical VMAT machine parameter optimization for localized prostate cancer using deep reinforcement learning.

Volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) remains computationally expensive and sensitive to input dose objectives creating challenges for manual and automatic planning. Reinforcement learning (RL) involves machine learning through extensive trial-and-error, demonstrating performance exceeding humans, and existing algorithms in several domains. To develop and evaluate an RL approach for VMAT MPO for localized prostate cancer to rapidly and automatically generate deliverable VMAT plans for a clinical linear accelerator (linac) and compare resultant dosimetry to clinical plans. We extended our previous RL approach to enable VMAT MPO of a 3D beam model for a clinical linac through a policy network. It accepts an input state describing the current control point and predicts continuous machine parameters for the next control point, which are used to update the input state, repeating until plan termination. RL training was conducted to minimize a dose-based cost function for prescription of 60Gy in 20 fractions using CT scans and contours from 136 retrospective localized prostate cancer patients, 20 of which had existing plans used to initialize training. Data augmentation was employed to mitigate over-fitting, and parameter exploration was achieved using Gaussian perturbations. Following training, RL VMAT was applied to an independent cohort of 15 patients, and the resultant dosimetry was compared to clinical plans. We also combined the RL approach with our clinical treatment planning system (TPS) to automate final plan refinement, and creating the potential for manual review and edits as required for clinical use. RL training was conducted for 5000 iterations, producing 40 000 plans during exploration. Mean±SD execution time to produce deliverable VMAT plans in the test cohort was 3.3±0.5 s which were automatically refined in the TPS taking an additional 77.4±5.8 s. When normalized to provide equivalent target coverage, the RL+TPS plans provided a similar mean±SD overall maximum dose of 63.2±0.6Gy and a lower mean rectum dose of 17.4±7.4 compared to 63.9±1.5Gy (p=0.061) and 21.0±6.0 (p=0.024) for the clinical plans. An approach for VMAT MPO using RL for a clinical linac model was developed and applied to automatically generate deliverable plans for localized prostate cancer patients, and when combined with the clinical TPS shows potential to rapidly generate high-quality plans. The RL VMAT approach shows promise to discover advanced linac control policies through trial-and-error, and algorithm limitations and future directions are identified and discussed.

Characterisation of Portuguese radiotherapy departments: Organisation, occupational exposure values and diagnostic reference levels for breast and prostate computed tomography planning

IntroductionPortugal currently hosts 24 active radiotherapy departments, 8 public and 16 privates, presenting potential radiation exposure risks to multidisciplinary teams. Patients in these treatments also face ionising radiation during treatment planning and verification. MethodsAuthorisation and ethical approval were secured for a national online survey, disseminated to radiotherapy departments across Portugal. The survey encompassed three sections: equipment, staff, and radiographer role characterisation; occupational exposure values for one month; and exposure parameters, including computed tomography (CT) dose values [CT dose index (CTDIvol) and dose length product (DLP)] for breast and prostate cancer CT planning. Local Diagnostic Reference Levels (DRLs) derived were based on the 75th percentile of median dose values. ResultsThe study garnered a 50% response rate from public institutions, 12,5% from private and 25% from all active radiotherapy institutions in Portugal. All departments employ Three-Dimensional Conformal Radiation Therapy (3D–CRT) and incorporate Intensity Modulated Radiation Therapy (IMRT) and/or Volumetric Modulated Arc Therapy (VMAT) irradiation techniques. Additionally, half of the departments also perform Brachytherapy (BT). Radiographers demonstrated an occupational dose of zero mSv. CT planning dose values were 13 mGy and 512 mGy cm for breast CT and 16 mGy and 689 mGy cm for prostate CT, pertaining to CTDIvol and DLP, respectively. ConclusionMost aspects of national radiotherapy characterisation align with the established literature. Occupational exposure values exhibited consistency across radiotherapy modalities. An approach to national DRLs was formulated for breast and prostate CT planning, yielding values congruent with recent European studies. Implications for practiceThis study offers vital insights for analysing occupational contexts and risk prevention, serving as the initial characterisation of the national radiotherapy landscape. It also pioneers the calculation of DRLs for CT planning in radiotherapy to optimise procedures.

Dosimetric comparison of advanced radiation techniques for scalp-sparing in low-grade gliomas.

Alopecia causes significant distress for patients and negatively impacts quality of life for low-grade glioma (LGG) patients. We aimed to compare and evaluate variations in dose distribution for scalp-sparing in LGG patients with proton therapy and photon therapy, namely intensity-modulated proton therapy (IMPT), intensity-modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT), and helical tomotherapy (HT). This retrospective study utilized adataset comprising imaging data from 22patients with LGG who underwent postoperative radiotherapy. Treatment plans were generated for each patient with scalp-optimized (SO) approaches and scalp-non-optimized (SNO) approaches using proton techniques and photons techniques; all plans adhered to the same dose constraint of delivering atotal radiation dose of 54.04 Gy to the target volume. All treatment plans were subsequently analyzed. All the plans generated in this study met the dose constraints for the target volume and OARs. The SO plans resulted in reduced maximum scalp dose (Dmax), mean scalp dose (Dmean), and volume of the scalp receiving 30 Gy (V30) and 40 Gy (V40) compared with SNO plans in all radiation techniques. Among all radiation techniques, the IMPT plans exhibited superior performance compared to other plans for dose homogeneity as for SO plans. Also, IMPT showed lower values for Dmean and Dmax than all photon radiation techniques. Our study provides evidence that the SO approach is afeasible technique for reducing scalp radiation dose. However, it is imperative to conduct prospective trials to assess the benefits associated with this approach.

In vivo EPID-based daily treatment error identification for volumetric-modulated arc therapy in head and neck cancers with a hierarchical convolutional neural network: a feasibility study.

We proposed a deep learning approach to classify various error types in daily VMAT treatment of head and neck cancer patients based on EPID dosimetry, which could provide additional information to support clinical decisions for adaptive planning. 146 arcs from 42 head and neck patients were analyzed. Anatomical changes and setup errors were simulated in 17,820 EPID images of 99 arcs obtained from 30 patients using in-house software for model training, validation, and testing. Subsequently, 141 clinical EPID images from 47 arcs belonging to the remaining 12 patients were utilized for clinical testing. The hierarchical convolutional neural network (HCNN) model was trained to classify error types and magnitudes using EPID dose difference maps. Gamma analysis with 3%/2mm (dose difference/distance to agreement) criteria was also performed. The F1 score, a combination of precision and recall, was utilized to evaluate the performance of the HCNN model and gamma analysis. The adaptive fractioned doses were calculated to verify the HCNN classification results. For error type identification, the overall F1 score of the HCNN model was 0.99 and 0.91 for primary type and subtype identification, respectively. For error magnitude identification, the overall F1 score in the simulation dataset was 0.96 and 0.70 for the HCNN model and gamma analysis, respectively; while the overall F1 score in the clinical dataset was 0.79 and 0.20 for the HCNN model and gamma analysis, respectively. The HCNN model-based EPID dosimetry can identify changes in patient transmission doses and distinguish the treatment error category, which could potentially provide information for head and neck cancer treatment adaption.

Deep learning-based optimization of field geometry for total marrow irradiation delivered with volumetric modulated arc therapy.

Total marrow (lymphoid) irradiation (TMI/TMLI) is a radiotherapy treatment used to selectively target the bone marrow and lymph nodes in conditioning regimens for allogeneic hematopoietic stem cell transplantation. A complex field geometry is needed to cover the large planning target volume (PTV) of TMI/TMLI with volumetric modulated arc therapy (VMAT). Five isocenters and ten overlapping fields are needed for the upper body, while, for patients with large anatomical conformation, two specific isocenters are placed on the arms. The creation of a field geometry is clinically challenging and is performed by a medical physicist (MP) specialized in TMI/TMLI. To develop convolutional neural networks (CNNs) for automatically generating the field geometry of TMI/TMLI. The dataset comprised 117 patients treated with TMI/TMLI between 2011 and 2023 at our Institute. The CNN input image consisted of three channels, obtained by projecting along the sagittal plane: (1) average CT pixel intensity within the PTV; (2) PTV mask; (3) brain, lungs, liver, bowel, and bladder masks. This "averaged" frontal view combined the information analyzed by the MP when setting the field geometry in the treatment planning system (TPS). Two CNNs were trained to predict the isocenters coordinates and jaws apertures for patients with (CNN-1) and without (CNN-2) isocenters on the arms. Local optimization methods were used to refine the models output based on the anatomy of the patient. Model evaluation was performed on a test set of 15 patients in two ways: (1) by computing the root mean squared error (RMSE) between the CNN output and ground truth; (2) with a qualitative assessment of manual and generated field geometries-scale: 1=not adequate, 4=adequate-carried out in blind mode by three MPs with different expertise in TMI/TMLI. The Wilcoxon signed-rank test was used to evaluate the independence of the given scores between manual and generated configurations (p<0.05 significant). The average and standard deviation values of RMSE for CNN-1 and CNN-2 before/after local optimization were 15±2/13±3mm and 16±2/18±4mm, respectively. The CNNs were integrated into a planning automation software for TMI/TMLI such that the MPs could analyze in detail the proposed field geometries directly in the TPS. The selection of the CNN model to create the field geometry was based on the PTV width to approximate the decision process of an experienced MP and provide a single option of field configuration. We found no significant differences between the manual and generated field geometries for any MP, with median values of 4versus 4 (p=0.92), 3versus 3 (p=0.78), 4versus 3 (p=0.48), respectively. Starting from October 2023, the generated field geometry has been introduced in our clinical practice for prospective patients. The generated field geometries were clinically acceptable and adequate, even for an MP with high level of expertise in TMI/TMLI. Incorporating the knowledge of the MPs into the development cycle was crucial for optimizing the models, especially in this scenario with limited data.

Incorporating plan complexity into the statistical process control of volumetric modulated arc therapy pre-treatment verifications.

Statistical process control (SPC) is a powerful statistical tool for process monitoring that has been highly recommended in healthcare applications, including radiation therapy quality assurance (QA). The AAPM TG-218 report described the clinical implementation of SPC for Volumetric Modulated Arc Therapy (VMAT) pre-treatment verifications, pointing out the need to adjust tolerance limits based on plan complexity. However, the quantification of plan complexity and its integration into SPC remains an unresolvedchallenge. The primary aim of this study is to investigate the incorporation of plan complexity into the SPC framework for VMAT pre-treatment verifications. The study explores and evaluates various strategies for this incorporation, discussing their merits and limitations, and provides recommendations for clinicalapplication. A retrospective analysis was conducted on 309 VMAT plans from diverse anatomical sites using the PTW OCTAVIUS 4D device for QA measurements. Gamma Passing Rates (GPR) were obtained, and lower control limits were computed using both the conventional Shewhart method and three heuristic methods (scaled weighted variance, weighted standard deviations, and skewness correction) to accommodate non-normal data distributions. The 'Identify-Eliminate-Recalculate' method was employed for robust analysis. Eight complexity metrics were analyzed and two distinct strategies for incorporating plan complexity into SPC were assessed. The first strategy focused on establishing control limits for different treatment sites, while the second was based on the determination of control limits as a function of individual plan complexity. The study extensively examines the correlation between control limits and plan complexity and assesses the impact of complexity metrics on the controlprocess. The control limits established using SPC were strongly influenced by the complexity of treatment plans. In the first strategy, a clear correlation was found between control limits and average plan complexity for each site. The second approach derived control limits based on individual plan complexity metrics, enabling tailored tolerance limits. In both strategies, tolerance limits inversely correlated with plan complexity, resulting in all highly complex plans being classified as in control. In contrast, when plans were collectively analyzed without considering complexity, all the out-of-control plans were highlycomplex. Incorporating plan complexity into SPC for VMAT verifications requires meticulous and comprehensive analysis. To ensure overall process control, we advocate for stringent control and minimization of plan complexity during treatment planning, especially when control limits are adjusted based on plancomplexity.

Evaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning.

Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool. For patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep-learning and RapidPlan models developed in-house. The autoplans were, then, applied to the original, physician-drawn contours, which were used as a ground truth (GT) to compare with the autocontours (RPA). Using a "two one-sided tests" (TOST) procedure, we evaluated whether the autocontour normal tissue dose was equivalent to that of the ground truth by a margin, δ, that we determined based on clinical judgement. We also calculated the number of plans that met established clinically accepted dosimetric criteria. For HN plans, 91.8% and 91.7% of structures met dosimetric criteria for automatic and manual contours, respectively; for cervical plans, 95.6% and 95.7% of structures met dosimetric criteria for automatic and manual contours, respectively. Autocontours were equivalent to the ground truth for 71% and 75% of common DVH metrics for the HN and cervix, respectively. This study shows that dosimetrically equivalent normal tissue contours can be created for HN and cervical cancers using deep learning techniques. In general, differences between the contours did not affect the passing or failing of clinical dose tolerances.

Neural Network Model Based on Branch Architecture for the Quality Assurance of Volumetric Modulated Arc Therapy.

Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples' model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic.

Evaluating Peer Review of Palliative Radiation Plans at a Canadian Tertiary Care Cancer Center.

Peer review (PR) of palliative-intent radiation plans is an importantbut understudied component of quality assurance. This retrospective review aims to improve our understanding of palliative PR by examining the characteristics of reviewed plans and peer feedback along with the associated time burden of two different types of PR processes. This single-institution, quality assurance project assessed palliative PR between 2018 and 2020. Initially, the PR involved a multi-disciplinary team PR. Subsequently, it transitioned to independent PR by a single physician. Characteristics of reviewed plans and feedback on PR were captured and abstracted. Time requirements of PR were based on self-reported estimates and attendance records. A total of 1942 cases were reviewed, representing 85.7% (1942/2266) of all palliative-intent plans between 2018 and 2020. A total of41.1% (n=799) were simple (2D/3D) radiation plans while 56.0% (n=1087) were complex (volumetric modulated arc therapy (VMAT) or tomotherapy) plans. Approximately one-third (30.4%, n=590) of all plans were stereotactic treatments. The rate of any peer feedback was 2.3% (n=45), while the rate of a specific recommended or implemented change was 1.2% (n=24) and 0.9% (n=18), respectively. PR before the start of treatment was associated with more frequent recommended (p=0.005) and implemented changes (p=0.008). Most other factors, including plan complexity and use of stereotactic radiation, were not predictive in this analysis. Comparing the independent versus team PR approach, there was no significant difference in recommended or implemented changes. The mean±standard deviation (SD) staff time required per plan reviewed was 36±6 and 37±6 minutes, including 21±6 and 10±6 minutes of physician time, for team and independent PR, respectively. This work highlights the high frequency of complex and stereotactic radiation in the palliative setting, along with the importance of timely PR and the potential benefit of reviewing even simple, 2D/3D radiation plans. Additionally, from a process perspective, our work showed that independent PR may require less dedicated physician time.

Evaluating the relationship between contouring variability and modelled treatment outcome for prostate bed radiotherapy.

Objectives.Contouring similarity metrics are often used in studies of inter-observer variation and automatic segmentation but do not provide an assessment of clinical impact. This study focused on post-prostatectomy radiotherapy and aimed to (1) identify if there is a relationship between variations in commonly used contouring similarity metrics and resulting dosimetry and (2) identify the variation in clinical target volume (CTV) contouring that significantly impacts dosimetry.Approach.The study retrospectively analysed CT scans of 10 patients from the TROG 08.03 RAVES trial. The CTV, rectum, and bladder were contoured independently by three experienced observers. Using these contours reference simultaneous truth and performance level estimation (STAPLE) volumes were established. Additional CTVs were generated using an atlas algorithm based on a single benchmark case with 42 manual contours. Volumetric-modulated arc therapy (VMAT) treatment plans were generated for the observer, atlas, and reference volumes. The dosimetry was evaluated using radiobiological metrics. Correlations between contouring similarity and dosimetry metrics were calculated using Spearman coefficient (Γ). To access impact of variations in planning target volume (PTV) margin, the STAPLE PTV was uniformly contracted and expanded, with plans created for each PTV volume. STAPLE dose-volume histograms (DVHs) were exported for plans generated based on the contracted/expanded volumes, and dose-volume metrics assessed.Mainresults. The study found no strong correlations between the considered similarity metrics and modelled outcomes. Moderate correlations (0.5 <Γ< 0.7) were observed for Dice similarity coefficient, Jaccard, and mean distance to agreement metrics and rectum toxicities. The observations of this study indicate a tendency for variations in CTV contraction/expansion below 5 mm to result in minor dosimetric impacts.Significance. Contouring similarity metrics must be used with caution when interpreting them as indicators of treatment plan variation. For post-prostatectomy VMAT patients, this work showed variations in contours with an expansion/contraction of less than 5 mm did not lead to notable dosimetric differences, this should be explored in a larger dataset to assess generalisability.

Low-dose radiation therapy for COVID-19 pneumonia: Comparison of dosimetry and life-time attributable risk of cancer with conventional AP-PA fields and bone marrow sparing VMAT.

Low-dose radiation therapy (LDRT) to lungs did show encouraging results in COVID-19 patients in some clinical trials. However, there has been some concern regarding the long-term risk of radiation-induced cancer (RIC). Compared to the conventional AP-PA field technique, volumetric modulated arc therapy (VMAT) can potentially reduce the dose to the marrow and other organs at risk (OARs) and thus minimize the risk of cancer. We designed a dosimetry study to study if VMAT can reduce the exposure to the marrow and other OAR doses and curtail the estimated life-time attributable risk (LAR) of cancer. We retrieved the computed tomography scan data of 10 patients (aged 40-60 years, median 48 years) who have been already treated for any malignancy in the region of the thorax. A dose of 1.0 Gy in single fraction was prescribed to both lungs. All the organs were delineated as per the established guidelines. The dosimetry achieved by the two plans was compared to find the difference. Mean OAR doses were used to estimate the LAR for both plans and compared. Planning target volume coverage parameters like conformity index and homogeneity index were significantly better with VMAT (P value < 0.05 for all). The mean dose to most OARs was significantly lower with VMAT (P value < 0.05 for all). The mean dose to the marrow was significantly lower with VMAT (59.05 vs 81.9 cGy with P value < 0.05). The overall LAR was significantly lower with VMAT as compared to the conventional plan (0.357% vs 0.398%, P value < 0.05). Compared to the conventional technique, VMAT provides better OAR dosimetry for lung irradiation (a prescription dose of 1.0 Gy or more) in COVID-19 pneumonia. VMAT significantly reduces the risk of RIC. We therefore suggest if lung LDRT is used for COVID-19 patients, VMAT is the preferred technique for a prescription dose of ≥1.0 Gy.

Role of external radiation in benign tumors: A clinical outcome and safety audit of 7 years from a tertiary care center in India.

Radiotherapy (RT) is a well-established modality for treating malignancies, but its role in treatment of benign lesions has not been well explored. Herein, we present a retrospective analysis of a 7-year data regarding the benefit and the safety profile of RT for treating benign tumors in our institute. Data of 23 patients who received RT for benign tumors from January 2015 to April 2022 were retrieved, and a retrospective analysis was conducted. All the pertinent demographic data, treatment and follow-up data were retrieved. The most common presentations were nasopharyngeal angiofibroma, vertebral hemangioma, paraganglioma, and others. The most common sites of occurrence were head and neck (43%) and paravertebral region (22%). Volumetric modulated arc therapy was the most commonly employed RT technique (39%), followed by three-dimensional conformal RT (34%) and two-dimensional conventional radiotherapy (26%). The median RT dose delivered was 36 Gy (range: 20-54 Gy). The median follow-up duration was 53 months (range: 3-120 months). Nine (39%) patients had progressive disease with a median time to progression of 8 months (range: 1-30 months). The median disease-free survival (DFS) was 70 months, while the 1-, 3-, and 5-year DFS rates were 97%, 88%, and 62%, respectively. Four patients (17%) died, all due to disease progression. The 1-, 3-, and 5-year overall survival rates was 97%, 85%, and 50%, respectively. The rate of radiation-induced cancer (RIC) was 0% as none of the patients had developed RIC secondary to radiation. RT is a safe and an effective option to manage benign tumors either in an adjuvant setting or in inoperable patients requiring definitive treatment, as well as in a setting to alleviate symptoms, providing excellent survival benefits. However, further prospective studies with much higher sample size are required to establish the absolute benefit and to estimate the risk of RIC, which will further direct for a better utilization of RT in treating benign tumors.

Microbiome profiling and Co-metabolism pathway analysis in cervical cancer patients with acute radiation enteritis

BackgroundIntestinal bacteria significantly contribute to the metabolism of intestinal epithelial tissues. As the occurrence and development of radiation enteritis (RE) depend on the “co-metabolism” microenvironment formed by the host and intestinal microbiota, which involves complex influencing factors and strong correlations, ordinary techniques struggle to fully explain the underlying mechanisms. However, given that it is based on systems biology, metabolomics analysis is well-suited to address these issues. This study aimed to analyze the metabolomic changes in urine, serum, and fecal samples during volumetric modulated arc therapy (VMAT) for cervical cancer and screen for characteristic metabolites of severe acute radiation enteritis (SARE) and RE. MethodsWe enrolled 50 patients who received radiotherapy for cervical cancer. Urine, serum, and fecal samples of patients were collected at one day before radiotherapy and the second week, fourth week, and sixth week after the start of radiotherapy. Control group samples were collected during the baseline period. Differential metabolites were identified by metabolomics analysis; co-metabolic pathways were clarified. We used the mini-SOM library for incorporating characteristic metabolites, and established metabolite classification models for predicting SARE and RE. ResultsUrine and serum sample data showed remarkable clustering effect; metabolomics data of the fecal supernatant were evidently disturbed. Patient sample analyses during VMAT revealed the following. Urine samples: Downregulation of the pyrimidine and riboflavin metabolism pathways as well as initial upregulation followed by downregulation of arginine and proline metabolism pathways and the arginine biosynthesis pathway. Fecal samples: Upregulation of linoleic acid and phenylalanine metabolic pathways and initial downregulation followed by upregulation of arachidonic acid (AA) metabolic pathways. Serum samples: Initial upregulation followed by downregulation of the arginine biosynthesis pathway and downregulation of glutathione, AA, and arginine and proline metabolic pathways. ConclusionPatients with cervical cancer exhibited characteristic metabolic pathways and characteristic metabolites predicting RE and SARE were screened out. An effective RE mini-SOM classification model was successfully established.