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Introduction: Around 300 children in Australia and New Zealand (ANZ) undergo a course of radiation treatment (RT) each year. A fortnightly videoconference for radiation oncologists managing children started in 2013. We conducted an audit of the videoconference to assess its influence on the care of children who receive RT in ANZ. Methods: De-identified data from minutes (August 2013-December 2019) were analysed retrospectively using three categories: meeting participation, case presentations and management decisions. Results: There were 119 meetings and 334 children discussed over the six-year audit period with regular attendance from four of 11 centres treating children in ANZ. Most cases (80%) were discussed prior to RT. A change in the overall management plan was recommended for around one in eight patients (35/ 334, 13%). RT plan reviews were performed in 79 cases (23%). Adjustments were made to the target volume contours or treatment plan in 8% (6/79). Conclusion: Increasing the frequency of the meeting to weekly and compliant with the RANZCR Peer Review Audit Tool has the capacity to review all paediatric RT patients in ANZ prior to RT and initiate changes for as many as one in eight children treated by RT each year. The meeting should be considered a core component necessary to maintain expertise in paediatric RT in all centres providing RT for children in ANZ while also acting as a proton referral panel as more children are referred abroad for proton therapy before the Australian Bragg Centre for Proton Therapy opens in Adelaide in 2024.

Objectives: To identify variables predicting inter fractional anatomical variationsmeasured with cone-beam CT (CBCT) throughout abdominal paediatric radiotherapy, and to assess the potential of surface-guided radiotherapy (SGRT) to monitor these changes. Methods: Metrics of variation in gastrointestinal (GI) gas volume andseparation of the body contour and abdominal wallwere calculated from 21 planning CTs and 77 weekly CBCTs for 21 abdominal neuroblastoma patients (median 4y, range: 2 –19y). Age, sex, feeding tubes, and general anaesthesia (GA) were explored as predictive variables for anatomical variation. Furthermore,GI gas variationwas correlated with changes in body and abdominal wall separation, as well as simulated SGRT metrics of translational and rotationalcorrections between CT/CBCT. Results: GI gas volumes varied 74 ± 54 ml across all scans, while body and abdominal wall separationvaried 2.0 ± 0.7 mm and4.1±1.5mmfrom planning, respectively. Patients < 3.5y (p = 0.04) and treated under GA (p < 0.01) experienced greater GI gas variation; GA was the strongest predictor in multivariate analysis (p < 0.01). Absence of feeding tubes was linked to greater body contour variation (p = 0.03). GI gas variation correlated with body (R = 0.53) and abdominal wall (R = 0.63) changes. The strongest correlations with SGRT metrics were found for anteroposterior translation (R = 0.65) androtation of the left-right axis (R = −0.36). Conclusions: Young age, GA, and absence of feeding tubes were linked to stronger inter fractional anatomical variation and are likely indicative of patients benefiting from adaptive/robust planning pathways.Our data suggests a role for SGRT toinformthe need for CBCT at each treatment fractionin this patient group. Advances in knowledge: This is the first study to suggest the potential role of SGRT for the management of internal inter fractional anatomical variation in paediatric abdominal radiotherapy.

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Objective: Adaptive radiotherapy workflows require images with the quality of computed tomography&#xD;(CT) for re-calculation and re-optimisation of radiation doses. In this work we aim to improve quality of&#xD;cone beam CT (CBCT) images for dose calculation using deep learning.&#xD;Approach: We propose a novel framework for CBCT-to-CT synthesis using cycle-consistent Generative Adversarial Networks (cycleGANs). The framework was tailored for paediatric abdominal patients, a&#xD;challenging application due to the inter-fractional variability in bowel filling and smaller patient numbers.&#xD;We introduced the concept of global residuals only learning to the networks and modified the cycleGAN&#xD;loss function to explicitly promote structural consistency between source and synthetic images. Finally, to&#xD;compensate for the anatomical variability and address the difficulties in collecting large datasets in the paediatric population, we applied a smart 2D slice selection based on the common field-of-view across&#xD;the dataset (abdomen). This acted as a weakly paired data approach that allowed us to take advantage of&#xD;scans from patients treated for a variety of malignancies (thoracic-abdominal-pelvic) for training&#xD;purposes. We first optimised the proposed framework and benchmarked its performance on a&#xD;development dataset. Later, a comprehensive quantitative evaluation was performed on an unseen dataset, which included calculating global image similarity metrics, segmentation-based measures and&#xD;proton therapy-specific metrics.&#xD;Main results: We found improved performance, compared to a baseline implementation, on imagesimilarity&#xD;metrics such as Mean Absolute Error calculated for a matched virtual CT (55.0±16.6 proposed, 58.9±16.8 baseline). There was also a higher level of structural agreement for gastrointestinal gas between source and synthetic images measured through dice similarity overlap (0.872±0.053 proposed, 0.846±0.052 baseline). Differences found in water-equivalent thickness metrics were also smaller for&#xD;our method (3.3±2.4% proposed, 3.7±2.8% baseline).&#xD;Significance: Our findings indicate that our innovations to the cycleGAN framework improved the quality&#xD;and structure consistency of the synthetic CTs generated.