Radiotherapy Planning Research Articles

QLTI-05. EUROPEAN NEURO-ONCOLOGY QUALITY ASSURANCE (ENOQUA): AN EORTC BRAIN TUMOR GROUP STUDY

Abstract INTRODUCTION Neuro-Oncology, a complex and multidisciplinary field that is relatively recent, lacks well-established quality assurance measures and standards for patient care. METHODS We assessed the performance of 14 potential quality indicators across 19 EORTC Neuro-Oncology centers to validate these indicators as effective measures of quality. For each center, we abstracted records from 20 patients diagnosed with glioblastoma in 2018 who were not enrolled in an investigational study. We tested associations between patient survival, center characteristics, and the performance of these quality measures. RESULTS Most centers met several standard requirements, suggesting their potential acceptance for prospective validation. IDH expression data was universally recorded in the patients’ chart. Performing MRI within 72 hours of surgery and providing patient education for chemotherapy correlated with improved overall survival (OS). Standards met in the majority of centers were the presence of a multidisciplinary plan, a radiation plan and the extent of surgery recorded in the chart, as well as the referral for radiation within six weeks of diagnosis. Preliminary results indicate that academic centers outperformed others in most measures, although analysis is ongoing. Performance varied significantly across centers; some standards were consistently met poorly, highlighting areas needing improvement. Lack of universal acceptance of certain practices as necessary and/or important may render some standards inappropriate for further development. Final analysis will be presented at the meeting. CONCLUSION We are developing a comprehensive set of standards and measures for QA in neuro-oncology, which will require future prospective validation.

NIMG-41. MULTI-SITE, PROSPECTIVE TRIAL EVALUATING FET-PET IN GLIOBLASTOMA (FIG) STUDY (TROG 18.06): PRELIMINARY RESULTS OF CENTRAL NUCLEAR MEDICINE REVIEW OF FET-PET BIOLOGIC TARGET VOLUME DELINEATION FOR RADIATION PLANNING

Abstract BACKGROUND To provide an update from the multi-site trial evaluating O-(2-[18F]-fluoroethyl)-L-tyrosine Positron Emission Tomography (FET-PET) in Glioblastoma (FIG) study and 2) assess the impact of central Nuclear Medicine physician (NMP) review of prospective FET-PET1 delineation of the biological target volume (BTV) for radiotherapy (RT) planning. Material and methods Up to 210 adult GBM participants across 11 Australian sites will undergo FET-PET post-surgery/pre-chemo-RT [CRT] (FET-PET1), one month post CRT (FET-PET2) and at suspected progression (FET-PET3). Group 1 participants enter at timepoint 1 (FET-PET1 with MRI1), with Group 2 entering at timepoint 2. Adjuvant RT target volumes are derived per standard contrast MRI with hybrid post-hoc RT volumes then incorporating the FET-PET1 NM-derived BTV utilising MiM version 7.0. All trial sites and NMP have passed credentialling which included three benchmarking cases involving BTV delineation. RESULTS Trial recruitment commenced in January 2021, with 189 (n=126 Group 1 and n=63 Group 2) participants enrolled to date, with a target of 140 Group 1 participants. During trial credentialling, results demonstrated variations in FET-PET1-derived BTV in 25/72 (34.7%) - 13 minor and 12 major.Currently, 42 of 112 participant FET-PET1 with BTV delineation cases across 10 sites have undergone central review, with 7/42 (16.7%) requiring resubmission. Reasons for resubmission/protocol deviation included incorrect imaging sequence selection within the MiM workflow (n=3), Static GTV overcontouring (n=3) and dynamic volume of interest change in size/position during MiM workflow use (n=1). CONCLUSION The FIG trial will complete recruitment in 2024 with analyses planned at one year post CRT completion. Despite improvements in resubmission rates compared to the credentialling phase, central review of prospective FET-PET1-derived BTV delineation remains important in ensuring protocol adherence. The FIG study is the largest prospective multi-site study of its kind addressing FET-PET’s impact on adjuvant radiation planning and its role in management of pseudoprogression and prognostication.

NIMG-74. A NOVEL APPROACH FOR PERSONALIZED RADIOTHERAPY TARGET VOLUME DEFINITION USING METABOLIC MRI AND DEEP LEARNING-DRIVEN PREDICTIONS OF GLIOBLASTOMA RECURRENCE

Abstract BACKGROUND Machine-learning models have demonstrated great promise in predicting tumor infiltration beyond anatomical margins in glioblastoma. Yet standard-of-care (SOC) radiation therapy (RT) planning only utilizes a uniform, isotropic 2cm-expansion of the T1-post-contrast-lesion or T2-lesion to generate a clinical target volume (CTV), without considering heterogeneity in tumor infiltration. We hypothesize that using a novel deep-learning approach to predict regions of tumor progression with metabolic and diffusion-weighted MRI will result in personalized CTVs that are more sensitive to detecting normal-appearing infiltrating tumor beyond the T2-lesion, sparing more healthy brain compared to SOC-CTVs. METHODS Anatomical, diffusion-weighted, and metabolic MRI from 101 patients with glioblastoma, acquired post-surgery but prior to chemoradiation, were retrospectively used to predict regions of new contrast-enhancement or T2-hyperintensity at confirmed progression. A segmentation-based predictive deep-learning approach with personalized loss-functions and evaluation metrics (“Progression-Coverage-Coefficient”, PCC) that weight Dice coefficient terms by tumor size was employed with a 66/17/18 train/validation/test split of patients and the resulting DL-CTV compared to two SOC-CTVs: 1) the RTOG[T2-FLAIR+2cm]-CTV, 2) the EORTC[T1c+2cm]-CTV. RESULTS Our best deep-learning-CTV trained using anatomical, diffusion, and metabolic MRI achieved significantly improved median test specificity (0.87 vs. 0.77; p<0.005) and comparable sensitivity (0.92 vs. 0.95; p=0.1) and PCC (0.81 vs. 0.79; p=0.1) to the SOC RTOG[T2-FLAIR+2cm]-CTV, sparing more normal brain tissue. Compared to the more conservative EORTC[T1c+2cm]-CTV, our approach had significantly higher test sensitivity (0.92 vs. 0.85; p<0.00001) and PCC (0.81 vs. 0.77; p=0.003) in covering the progressed lesion, while maintaining similar specificity (0.87 vs. 0.89; p=0.3). Test performance dropped by 9.6% and 12.4% when removing metabolic and both metabolic and diffusion metrics, respectively (p<0.001). CONCLUSION This study demonstrates the benefit of using metabolic and diffusion MRI with deep learning for personalized RT planning by targeting regions of anatomically normal infiltrative disease that are highly likely to progress, while reducing treatment to normal brain.

Cardiotoxicity following thoracic radiotherapy for lung cancer.

Radiotherapy is the standard of care treatment for unresectable NSCLC, combined with concurrent chemotherapy and adjuvant immunotherapy. Despite technological advances in radiotherapy planning and delivery, the risk of damage to surrounding thoracic tissues remains high. Cardiac problems, including arrhythmia, heart failure and ischaemic events, occur in 20% of patients with lung cancer who undergo radiotherapy. As survival rates improve incrementally for this cohort, minimising the cardiovascular morbidity of RT is increasingly important. Problematically, the reporting of cardiac endpoints has been poor in thoracic radiotherapy clinical trials, and retrospective studies have been limited by the lack of standardisation of nomenclature and endpoints. How baseline cardiovascular profile and cardiac substructure radiation dose distribution impact the risk of cardiotoxicity is incompletely understood. As Thoracic Oncology departments seek to expand the indications for radiotherapy, and as the patient cohort becomes older and more comorbid, there is a pressing need for cardiotoxicity to be comprehensively characterised with sophisticated oncology, physics and cardio-oncology evaluations. This review synthesises the evidence base for cardiotoxicity in conventional radiotherapy, focusing on lung cancer, including current data, unmet clinical needs, and future scientific directions.

A Review of Contemporary Image Guidance Techniques in Head and Neck Cancer.

Traditional head and neck cancer treatment involves open surgery, cytotoxic chemotherapy, and conventional radiotherapy planning. Emerging techniques aim to improve precision and reduce associated toxicity and functional impairment in current practice. This review article describes four such adaptations in image guidance, tailored to next generation therapies. This is a review of current literature, including feasibility studies from our cancer center, relating to: saline-aided intra-oral ultrasound-guided retropharyngeal biopsy; intra-oral ultrasound guided trans-oral robotic surgery (TORS); ultrasound-guided injection of "directly injectedtherapies"; and magnetic resonance imaging-guided radiotherapy. Presented within the context of the wider literature, initial local experience and data indicate good technical outcomes and patient tolerance, and low technical complications in all four image guidance techniques. Initial findings suggest a potentially important future role for these four image guidance techniques, on which next generation therapies are reliant. The broader implications on cross-disciplinary collaboration are also explored herein.

Comparative analysis of fetal dose sparing between a C-arm linac and an O-ring linac in a SIB-VMAT sarcoma treatment for a pregnant patient: A technical note and case report.

To compare the effect of two linacs designs on fetal dose sparing on a pregnant patient, including estimation of the fetal dose, and the effect of a lead apron. A patient with a high-grade sarcoma located in the right knee/lower thigh was prescribed 51Gy (1.7Gy/Fx) with a simultaneous-integrated-boost (SIB) of 60Gy to a smaller volume, starting in the 26thgestational week. Volumetric modulated radiation therapy (VMAT) plans with 6MV-FFF were developed using identical dosimetric constraints on a Varian Truebeam Edge with HD-MLC and a Varian Halcyon with double-stacked MLC. Based on patient dimension measurements, an anthropomorphic phantom was constructed using a Rando phantom and saline bags in the patient's Vac-Lok bag. Phantom measurements were performed using OSLDs and TLDs placed at three different planes, corresponding to the pubis, the umbilicus, and the fundus based on patient measurements and projected gestational age, to estimate the fetal dose. Three experimental scenarios were measured, each with CBCT-based image guidance for an accurate, reproducible setup: Edge, Halcyon, and Halcyon with a tri-folded lead apron (0.5mm×3=1.5mmPb) over the phantom abdomen. Plan quality and total MUs are comparable between the Edge and Halcyon plans. The OSLD-measured whole-course dose to the pubis, umbilicus, and fundus were 18.8, 13.1, and 11.7 cGy, respectively, on Halcyon, on average 27.8% lower than Edge. The repeatability within either dosimeter was good, although TLD showed systematically lower doses. Importantly, both dosimetry systems showed a lower measured fetal dose for the Halcyon plan compared with the Edge plan. Adding a tri-folded lead apron over the abdomen did not meaningfully lower the measured dose. In this case study, Halcyon demonstrated a better sparing of out-of-field fetal dose compared to TrueBeam Edge. It was shown that lead aprons do not provide additional fetal dose sparing.

Radiosurgery-induced early changes in peritumoral tissue sodium concentration of brain metastases.

Stereotactic radiosurgery (SRS) is an effective therapy for brain metastases. Response is assessed with serial 1H magnetic resonance imaging (MRI). Early markers for response are desirable to allow for individualized treatment adaption. Previous studies indicated that radiotherapy might have impact on tissue sodium concentration. Thus, 23Na MRI could provide early quantification of response to SRS. We investigated whether longitudinal detection of tissue sodium concentration alteration within brain metastases and their peritumoral tissue after SRS with 23Na MRI was feasible. Prospective. Twelve patients with a total of 14 brain metastases from various primary tumors. 23Na MRI scans were acquired from patients 2 days before, 5 days after, and 40 days after SRS. Gross tumor volume (GTV) and healthy-appearing regions were manually segmented on the MPRAGE obtained 2 days before SRS, onto which all 23Na MR images were coregistered. Radiation isodose areas within the peritumoral tissue were calculated with the radiation planning system. Tissue sodium concentration before and after SRS within GTV, peritumoral tissue, and healthy-appearing regions as well as the routine follow-up with serial MRI were evaluated. Results were compared using Student's t-test and correlation was evaluated with Pearson's correlation coefficient. We found a positive correlation between the tissue sodium concentration within the peritumoral tissue and radiation dosage. Two patients showed local progression and a differing tissue sodium concentration evolution within GTV and the peritumoral tissue compared to mean tissue sodium concentration of the other patients. No significant tissue sodium concentration changes were observed within healthy-appearing regions. Tissue sodium concentration assessment within brain metastases and peritumoral tissue after SRS with 23Na MRI is feasible and might be able to quantify tissue response to radiation.

A simulation framework for preclinical proton irradiation workflow

Objective.The integration of proton beamlines with x-ray imaging/irradiation platforms has opened up possibilities for image-guided Bragg peak irradiations in small animals. Such irradiations allow selective targeting of normal tissue substructures and tumours. However, their small size and location pose challenges in designing experiments. This work presents a simulation framework useful for optimizing beamlines, imaging protocols, and design of animal experiments. The usage of the framework is demonstrated, mainly focusing on the imaging part.Approach.The fastCAT toolkit was modified with Monte Carlo (MC)-calculated primary and scatter data of a small animal imager for the simulation of micro-CT scans. The simulated CT of a mini-calibration phantom from fastCAT was validated against a full MC TOPAS CT simulation. A realistic beam model of a preclinical proton facility was obtained from beam transport simulations to create irradiation plans in matRad. Simulated CT images of a digital mouse phantom were generated using single-energy CT (SECT) and dual-energy CT (DECT) protocols and their accuracy in proton stopping power ratio (SPR) estimation and their impact on calculated proton dose distributions in a mouse were evaluated.Main results.The CT numbers from fastCAT agree within 11 HU with TOPAS except for materials at the centre of the phantom. Discrepancies for central inserts are caused by beam hardening issues. The root mean square deviation in the SPR for the best SECT (90 kV/Cu) and DECT (50 kV/Al-90 kV/Al) protocols are 3.7% and 1.0%, respectively. Dose distributions calculated for SECT and DECT datasets revealed range shifts <0.1 mm, gamma pass rates (3%/0.1 mm) greater than 99%, and no substantial dosimetric differences for all structures. The outcomes suggest that SECT is sufficient for proton treatment planning in animals.Significance.The framework is a useful tool for the development of an optimized experimental configuration without using animals and beam time.

Understanding and modeling human-AI interaction of artificial intelligence tool in radiation oncology clinic using deep neural network: a feasibility study using three year prospective data.

&#xD;Artificial intelligence (AI) based treatment planning tools are being implemented in clinic. However, human interactions with such AI tools are rarely analyzed. This study aims to comprehend human planner's interaction with the AI planning tool and incorporate the analysis to improve the existing AI tool.&#xD;Materials and methods:&#xD;An in-house AI tool for whole breast radiation therapy planning was deployed in our institution since 2019, among which 522 patients were included in this study. The AI tool automatically generates fluence maps of the tangential beams to create an AI plan. Human planner makes fluence edits deemed necessary and after attending physician approval for treatment, it is recorded as final plan. Manual modification value (MMV) maps were collected, which is the difference between the AI-plan and the final plan. Subsequently, a human-AI interaction (HAI) model using full scale connected U-Net was trained to learn such interactions and perform plan enhancements. The trained HAI model automatically modifies the AI plan to generate AI-modified plans (AI-m plan), simulating human editing. Its performance is evaluated against original AI-plan and final plan.&#xD;Results:&#xD;AI-m plan showed statistically significant improvement in hotspot control over the AI plan, with an average of 25.2cc volume reduction in breast V105% (p=0.011) and 0.805% decrease in Dmax (p<.001). It also maintained the same PTV coverage as the final plan, demonstrating the model has captured the clinic focus of improving PTV hot spots without degrading coverage.&#xD;Conclusions:&#xD;The proposed HAI model has demonstrated capability of further enhancing the AI plan via modeling human-AI tool interactions. This study shows analysis of human interaction with the AI planning tool is a significant step to improve the AI tool.&#xD.

Simulation-free magnetic resonance-guided radiation therapy of prostate cancer

Background and purposeDespite recent advances of online image-guided high-precision patient positioning and adaptation using magnetic resonance imaging (MRI) or cone-beam computed tomography (CT), standard radiation therapy pathway still involves a dedicated simulation scan. The aim of this study was to evaluate the feasibility and planning quality of integrating a simulation-free treatment planning workflow for adaptive online MRI-guided radiation therapy on a 1.5 T MRI linear accelerator (MRI-Linac) in prostate cancer using diagnostic CT (dCT) scans. Materials and methodsFor ten patients with prostate cancer previously treated at the MRI-Linac with adaptive radiation therapy (42.7 Gy in 7 fractions), simulation-free reference plans based on dCT were retrospectively created, and adaptive plans were simulated for the first treatment fraction. Reference and adapted plans derived from both standard and simulation-free workflows were compared with regard to institutional dose volume criteria, followed by statistical assessment using the paired Wilcoxon signed-rank test with a Bonferroni-corrected significance level of α = 0.025. ResultsSimulation-free reference and adapted plans consistently met dose volume criteria. Statistical analysis revealed no significant differences between both workflows, except median values for near-maximum dose (D2%) in the planning target volume: 44.2 Gy (standard) vs. 44.5 Gy (simulation-free) in reference plans (p = 0.014), and 44.5 Gy vs. 44.6 Gy in adapted plans (p = 0.009). ConclusionThis study demonstrated the feasibility of simulation-free radiation therapy planning using dCT. Comparable treatment plan quality was observed for both reference and adapted radiation therapy plans in a curative setting for patients with prostate cancer.

Implementing verifiable oncological imaging by quality assurance and optimization (i‑Violin) : Protocol for aEuropean multicenter study.

Advanced imaging techniques play apivotal role in oncology. Alarge variety of computed tomography (CT) scanners, scan protocols, and acquisition techniques have led to awide range in image quality and radiation exposure. This study aims at implementing verifiable oncological imaging by quality assurance and optimization (i-Violin) through harmonizing image quality and radiation dose across Europe. The 2‑year multicenter implementation study outlined here will focus on CT imaging of lung, stomach, and colorectal cancer and include imaging for four radiological indications: diagnosis, radiation therapy planning, staging, and follow-up. Therefore, 480 anonymized CT data sets of patients will be collected by the associated university hospitals and uploaded to arepository. Radiologists will determine key abdominopelvic structures for image quality assessment by consensus and subsequently adapt apreviously developed lung CT tool for the objective evaluation of image quality. The quality metrics will be evaluated for their correlation with perceived image quality and the standardized optimization strategy will be disseminated across Europe. The results of the outlined study will be used to obtain European reference data, to build teaching programs for the developed tools, and to create aculture of optimization in oncological CT imaging. The study protocol and rationale for i‑Violin, aEuropean approach for standardization and harmonization of image quality and optimization of CT procedures in oncological imaging, is presented. Future results will be disseminated across all EU member states, and i‑Violin is thus expected to have asustained impact on CT imaging for cancer patients across Europe.

Deformable dose prediction network based on hybrid 2D and 3D convolution for nasopharyngeal carcinoma radiotherapy.

Radiotherapy is recognized as the primary treatment for nasopharyngeal carcinoma (NPC). Rapid and accurate dose prediction is crucial for enhancing the quality and efficiency of radiotherapy planning. However, the current dose prediction model based on 2D architecture cannot effectively learn the spatial information among slices. Although some studies have explored the incorporation of interslice features through 3D architecture, the resolution properties of medical image anisotropy significantly limit the predictive performance. To address the issues, we propose a novel deformable dose prediction network based on hybrid 2D and 3D convolution for NPC radiotherapy. Specifically, the proposed model innovatively incorporates a 2.5D architecture based on hybrid 2D and 3D convolution, and effectively utilizes the directional information within anisotropic resolutions to achieve cross-scale feature extraction. Additionally, deformable convolution is introduced into the model to enhance the receptive field and effectively handle multi-scale spatial transformations. To improve channel correlation and reduce redundant features, we design a Residual Deformable Squeeze-and-Excitation Module. We conduct extensive experiments on an internal dataset, and the results show that the proposed model outperforms other existing methods in most dosimetric criteria. The proposed model has superior dose prediction performance in NPC radiotherapy, and has important clinical significance for assisting physicists to optimize the treatment plan and improve standardization of radiotherapy planning. The source code is available at https://github.com/CDUTJ102/2.5D-Deformable-UNet .

Automated Organ Segmentation for Radiation Therapy: A Comparative Analysis of AI-Based Tools Versus Manual Contouring in Korean Cancer Patients

Background: Accurate delineation of tumors and organs at risk (OARs) is crucial for intensity-modulated radiation therapy. This study aimed to evaluate the performance of OncoStudio, an AI-based auto-segmentation tool developed for Korean patients, compared with Protégé AI, a globally developed tool that uses data from Korean cancer patients. Methods: A retrospective analysis of 1200 Korean cancer patients treated with radiotherapy was conducted. Auto-contours generated via OncoStudio and Protégé AI were compared with manual contours across the head and neck and thoracic, abdominal, and pelvic organs. Accuracy was assessed using the Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff distance (HD). Feedback was obtained from 10 participants, including radiation oncologists, residents, and radiation therapists, via an online survey with a Turing test component. Results: OncoStudio outperformed Protégé AI in 85% of the evaluated OARs (p &lt; 0.001). For head and neck organs, OncoStudio achieved a similar DSC (0.70 vs. 0.70, p = 0.637) but significantly lower MSD and 95% HD values (p &lt; 0.001). In thoracic organs, OncoStudio performed excellently in 90% of cases, with a significantly greater DSC (male: 0.87 vs. 0.82, p &lt; 0.001; female: 0.95 vs. 0.87, p &lt; 0.001). OncoStudio also demonstrated superior accuracy in abdominal (DSC 0.88 vs. 0.81, p &lt; 0.001) and pelvic organs (male: DSC 0.95 vs. 0.85, p &lt; 0.001; female: DSC 0.82 vs. 0.73, p &lt; 0.001). Clinicians favored OncoStudio in 70% of cases, with 90% endorsing its clinical suitability for Korean patients. Conclusions: OncoStudio, which is tailored for Korean patients, demonstrated superior segmentation accuracy across multiple anatomical regions, suggesting its suitability for radiotherapy planning in this population.

Hypofractionated radiotherapy with simultaneous integrated boost for localized prostate cancer patients: effects on immune system and prediction of toxicity

BackgroundThe other side of radiotherapy (RT), in addition to the cytotoxic effect, is the ability to modulate the immune system in terms of activation or suppression, also depending on the dose and fractionation delivered. This immune RT effect can be detected both locally in the irradiated tumor site and in the peripheral blood. The aim of this study was to assess the consequence of pelvic irradiation on peripheral immune cells and cytokine secretions in localized prostate cancer (PC) patients undergoing pelvic irradiation with a simultaneous moderately hypofractionated prostate/prostate bed boost by Volumetric Modulated Arc Therapy (VMAT). Furthermore, we analyzed whether there was a correlation between these peripheral immune parameters and acute and late genitourinary (GU) and gastrointestinal (GI) toxicity.MethodsThirty-eight PC patients were treated with pelvis irradiation (dose per fraction 1.8 Gy) and simultaneous hypofractionated (median dose per fraction: 2.7 Gy) prostate/prostate bed boost. A longitudinal analysis was performed for 12 months on peripheral blood to assess changes in 9 different lymphocyte subpopulations by flow cytometry and 10 circulating cytokines by Multiplex Luminex assay and ELISA.ResultsOur analysis revealed that basal IFN-γ serum values were significantly lower in the definitive (curative intent for patients with prostate) patient group respect to the post-operative one. All the lymphocyte subsets and IFN-α, IFN-β and Il-2 peripheral concentrations displayed significant variations between the different time points considered. The immune cell population that suffers the greatest RT toxicity in the blood was B lymphocyte. We found an interesting correlation between basal TGF-β1 and late GU toxicity. In particular, TGF-β1 concentrations before RT were significantly higher in patients that experienced grade 2-3 of late GU toxicity, respect to grade 0-1. Exploring possible correlations between some clinical/biological findings and radiation planning parameters, we found no statistical significance.ConclusionsOur study analyzed, in the context of hypofractionated radiotherapy in prostate cancer, different parameters of the peripheral immune system. We have highlighted longitudinally the peripheral behavior of the different lymphocyte subpopulations and of a group of 10 cytokines during the first year after RT. One of the analyzed cytokines, such as TGF-β1, has proven to be promising predictive factor of severe late GU toxicity.

The Australian paediatric brachytherapy experience: A pathway to a national programme.

Paediatric cancers are rare, and most children requiring radiation therapy receive external beam radiation (EBRT). Although EBRT may offer organ preservation compared to surgery, it can be associated with significant late effects. Image-guided brachytherapy is a highly specialised technique offering both organ preservation and dose conformity to minimise late toxicity. This is a retrospective mono-institutional review of paediatric brachytherapy in one of the largest paediatric centres in Australia. Outcomes and toxicities are presented as well as brachytherapy versus proton plan comparison in four patients. A total of 14 patients were treated with adjuvant brachytherapy between 2012 and 2022. The predominant histology was rhabdomyosarcoma, and all patients had pelvic tumours. High-dose rate (HDR) brachytherapy was given for 13 patients with one patient receiving low-dose rate (LDR) brachytherapy. Only one grade three late toxicity was reported and two patients developed metastatic disease within one year of completion of treatment. The brachytherapy plan was superior to protons in two of four patients and equivocal in one patient. This is the first Australian publication of a paediatric brachytherapy series from a single institution. This retrospective series demonstrates the feasibility and safety of brachytherapy in paediatric pelvic tumours. The initial work presented here demonstrates the value of a comprehensive radiation plan review in selecting the optimal modality for an individual paediatric patient.

Monte Carlo dose calculation for photon and electron radiotherapy on dynamically deforming anatomy.

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% / 2mm (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 50Gy 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 26.9Gy (lung) and up to 7.6Gy (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.

Artificial intelligence-assisted delineation for postoperative radiotherapy in patients with lung cancer: a prospective, multi-center, cohort study

BackgroundPostoperative radiotherapy (PORT) is an important treatment for lung cancer patients with poor prognostic features, but accurate delineation of the clinical target volume (CTV) and organs at risk (OARs) is challenging and time-consuming. Recently, deep learning-based artificial intelligent (AI) algorithms have shown promise in automating this process.ObjectiveTo evaluate the clinical utility of a deep learning-based auto-segmentation model for AI-assisted delineating CTV and OARs in patients undergoing PORT, and to compare its accuracy and efficiency with manual delineation by radiation oncology residents from different levels of medical institutions.MethodsWe previously developed an AI auto-segmentation model in 664 patients and validated its contouring performance in 149 patients. In this multi-center, validation trial, we prospectively involved 55 patients and compared the accuracy and efficiency of 3 contouring methods: (i) unmodified AI auto-segmentation, (ii) fully manual delineation by junior radiation oncology residents from different medical centers, and (iii) manual modifications based on AI segmentation model (AI-assisted delineation). The ground truth of CTV and OARs was delineated by 3 senior radiation oncologists. Contouring accuracy was evaluated by Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance of agreement (MDA). Inter-observer consistency was assessed by volume and coefficient of variation (CV).ResultsAI-assisted delineation achieved significantly higher accuracy compared to unmodified AI auto-contouring and fully manual delineation by radiation oncologists, with median HD, MDA, and DCS values of 20.03 vs. 21.55 mm, 2.57 vs. 3.06 mm, 0.745 vs. 0.703 (all P&amp;lt;0.05) for CTV, respectively. The results of OARs contours were similar. CV for OARs was reduced by approximately 50%. In addition to better contouring accuracy, the AI-assisted delineation significantly decreased the consuming time and improved the efficiency.ConclusionAI-assisted CTV and OARs delineation for PORT significantly improves the accuracy and efficiency in the real-world setting, compared with pure AI auto-segmentation or fully manual delineation by junior oncologists. AI-assisted approach has promising clinical potential to enhance the quality of radiotherapy planning and further improve treatment outcomes of patients with lung cancer.

Optimising PTV margins in oesophageal cancer radiotherapy: Amodern radiotherapy planning, treatment delivery and verification approach - A single institutional analysis.

Radiotherapy plays a key role in the multidisciplinary management of oesophageal cancers across neoadjuvant, definitive and palliative settings. Improved precision in radiotherapy planning and delivery techniques have allowed treating disease with tighter margins reducing toxicity. In this study, we examine the appropriateness of current practice in defining the planning target volume (PTV). This is a single institutional retrospective study of patients who received radiotherapy for oesophageal cancers using volumetric modulated arc therapy (VMAT) during 2020. All cone-beam computerised tomography (CBCT) scans were reviewed to assess whether PTV expansions appropriately accounted for tumour motion and interfractional variation. Of the 27 patients, 2 (7%), 5 (19%) and 20 (74%) had cervical, thoracic and distal/gastro-oesophageal junction (GOJ) disease, respectively. 16 (59%) had adenocarcinoma and 9 (33%) had squamous cell carcinoma. 9 of 20 distal/GOJ patients were planned and treated according to the institutional stomach filling protocol. 521/528 (98.7%) CBCTs demonstrated adequate target coverage. Cervical, thoracic and GOJ regions demonstrated adequate target coverage in 57/58, 96/97 and 368/373 CBCTs with median PTV expansions of 5, 7 and 7 mm, respectively. In four patients with GOJ disease, CBCT review identified five episodes where the target volume was insufficiently covered during the treatment course. In this single institutional retrospective study, for the vast majority of patients, our institutional practice of defining PTV margins achieved satisfactory target treatment. The interfractional variations observed in patients with GOJ tumours due to target motion and variable gastric volume, highlights the role of further refinements to motion management techniques in this cohort.

Dual convolution-transformer UNet (DCT-UNet) for organs at risk and clinical target volume segmentation in MRI for cervical cancer brachytherapy

Objective. MRI is the standard imaging modality for high-dose-rate brachytherapy of cervical cancer. Precise contouring of organs at risk (OARs) and high-risk clinical target volume (HR-CTV) from MRI is a crucial step for radiotherapy planning and treatment. However, conventional manual contouring has limitations in terms of accuracy as well as procedural time. To overcome these, we propose a deep learning approach to automatically segment OARs (bladder, rectum, and sigmoid colon) and HR-CTV from female pelvic MRI.Approach. In the proposed pipeline, a coarse multi-organ segmentation model first segments all structures, from which a region of interest is computed for each structure. Then, each organ is segmented using an organ-specific fine segmentation model separately trained for each organ. To account for variable sizes of HR-CTV, a size-adaptive multi-model approach was employed. For coarse and fine segmentations, we designed a dual convolution-transformer UNet (DCT-UNet) which uses dual-path encoder consisting of convolution and transformer blocks. To evaluate our model, OAR segmentations were compared to the clinical contours drawn by the attending radiation oncologist. For HR-CTV, four sets of contours (clinical + three additional sets) were obtained to produce a consensus ground truth as well as for inter/intra-observer variability analysis.Main results. DCT-UNet achieved dice similarity coefficient (mean ± SD) of 0.932 ± 0.032 (bladder), 0.786 ± 0.090 (rectum), 0.663 ± 0.180 (sigmoid colon), and 0.741 ± 0.076 (HR-CTV), outperforming other state-of-the-art models. Notably, the size-adaptive multi-model significantly improved HR-CTV segmentation compared to a single-model. Furthermore, significant inter/intra-observer variability was observed, and our model showed comparable performance to all observers. Computation time for the entire pipeline per subject was 12.59 ± 0.79 s, which is significantly shorter than the typical manual contouring time of >15 min.Significance. These experimental results demonstrate that our model has great utility in cervical cancer brachytherapy by enabling fast and accurate automatic segmentation, and has potential in improving consistency in contouring. DCT-UNet source code is available athttps://github.com/JHU-MICA/DCT-UNet.

Transformer-Integrated Hybrid Convolutional Neural Network for Dose Prediction in Nasopharyngeal Carcinoma Radiotherapy.

Radiotherapy is recognized as the major treatment of nasopharyngeal carcinoma. Rapid and accurate dose prediction can improve the efficiency of the treatment planning process and the quality of radiotherapy plans. Currently, deep learning-based methods have been widely applied to dose prediction for radiotherapy treatment planning. However, it is important to note that existing models based on Convolutional Neural Networks (CNN) often overlook long-distance information. Although some studies try to use Transformer to solve the problem, it lacks the ability of CNN to process the spatial information inherent in images. Therefore, we propose a novel CNN and Transformer hybrid dose prediction model. To enhance the transmission ability of features between CNN and Transformer, we design a hierarchical dense recurrent encoder with a channel attention mechanism. Additionally, we propose a progressive decoder that preserves richer texture information through layer-wise reconstruction of high-dimensional feature maps. The proposed model also introduces object-driven skip connections, which facilitate the flow of information between the encoder and decoder. Experiments are conducted on in-house datasets, and the results show that the proposed model is superior to baseline methods in most dosimetric criteria. In addition, the image analysis metrics including PSNR, SSIM, and NRMSE demonstrate that the proposed model is consistent with ground truth and produces promising visual effects compared to other advanced methods. The proposed method could be taken as a powerful clinical guidance tool for physicists, significantly enhancing the efficiency of radiotherapy planning. The source code is available at https://github.com/CDUTJ102/THCN-Net .