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Purpose: One-table treatments with treatment imaging, preparation and delivery occurring at one treatment couch, could increase patients' comfort and throughput for palliative treatments. On regular C-arm linacs, however, cone-beam CT (CBCT) imaging quality is currently insufficient. Therefore, our goal was to assess the suitability of AI-generated CBCT based synthetic CT (sCT) images for target delineation and treatment planning for palliative radiotherapy., Materials and Methods: CBCTs and planning CT-scans of 22 female patients with pelvic bone metastasis were included. For each CBCT, a corresponding sCT image was generated by a deep learning model in ADMIRE 3.38.0. Radiation oncologists delineated 23 target volumes (TV) on the sCTs (TVsCT) and scored their delineation confidence. The delineations were transferred to planning CTs and manually adjusted if needed to yield gold standard target volumes (TVclin). TVsCT were geometrically compared to TVclin using Dice coefficient (DC) and Hausdorff Distance (HD). The dosimetric impact of TVsCT inaccuracies was evaluated for VMAT plans with different PTV margins., Results: Radiation oncologists scored the sCT quality as sufficient for 13/23 TVsCT (median: DC = 0.9, HD = 11 mm) and insufficient for 10/23 TVsCT (median: DC = 0.7, HD = 34 mm). For the sufficient category, remaining inaccuracies could be compensated by +1 to +4 mm additional margin to achieve coverage of V95% > 95% and V95% > 98%, respectively in 12/13 TVsCT., Conclusion: The evaluated sCT quality allowed for accurate delineation for most targets. sCTs with insufficient quality could be identified accurately upfront. A moderate PTV margin expansion could address remaining delineation inaccuracies. Therefore, these findings support further exploration of CBCT based one-table treatments on C-arm linacs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective . Increasing cancer incidence, staff shortage and high burnout rate among radiation oncologists, medical physicists and radiation technicians are putting many departments under strain. Operations research (OR) tools could optimize radiotherapy processes, however, clinical implementation of OR-tools in radiotherapy is scarce since most investigated optimization methods lack robustness against patient-to-patient variation in duration of tasks. By combining OR-tools, a method was developed that optimized deployment of radiotherapy resources by generating robust pretreatment preparation schedules that balance the expected average patient preparation time ( F mean ) with the risk of working overtime ( RoO ). The method was evaluated for various settings of an one-stop shop (OSS) outpatient clinic for palliative radiotherapy. Approach . The OSS at our institute sees, scans and treats 3-5 patients within one day. The OSS pretreatment preparation workflow consists of a fixed sequence of tasks, which was manually optimized for radiation oncologist and CT availability. To find more optimal sequences, with shorter F mean and lower RoO , a genetic algorithm was developed which regards these sequences as DNA-strands. The genetic algorithm applied natural selection principles to produce new sequences. A decoder translated sequences to schedules to find the conflicting fitness parameters F mean and RoO . For every generation, fitness of sequences was determined by the distance to the estimated Pareto front of F mean and RoO . Experiments were run in various OSS-settings. Main results . According to our approach, the expected F mean of the current clinical schedule could be reduced with 37%, without increasing RoO . Additional experiments provided insights in trade-offs between F mean , RoO , working shift length, number of patients treated on a single day and staff composition. Significance . Our approach demonstrated that OR-tools could optimize radiotherapy resources by robust pretreatment workflow scheduling. The results strongly support further exploration of scheduling optimization for treatment preparation also outside a one-stop shop or radiotherapy setting., (© 2024 Institute of Physics and Engineering in Medicine.)

Introduction: Nowadays, deep inspiratory breath-hold is a common technique to reduce heart dose in left-sided breast radiotherapy. This study evaluates the evolution of the breath-hold technique in our institute, from portal imaging during dose delivery to continuous monitoring with surface-guided radiotherapy (SGRT)., Materials and Methods: Setup data and portal imaging results were analyzed for 98 patients treated before 2014, and SGRT data for 228 patients treated between 2018 and 2020. For the pre-SGRT group, systematic and random setup errors were calculated for different correction protocols. Residual errors and reproducibility of breath-holds were evaluated for both groups. The benefit of using SGRT for initial positioning was evaluated for another cohort of 47 patients., Results: Online correction reduced the population mean error from 3.9 mm (no corrections) to 1.4 mm. Despite online setup correction, deviations greater than 3 mm were observed in about 10% and 20% of the treatment beams in ventral-dorsal and cranial-caudal directions, respectively. However, these percentages were much smaller than with offline protocols or no corrections. Mean absolute differences between breath-holds within a fraction were smaller in the SGRT-group (1.69 mm) than in the pre-SGRT-group (2.10 mm), and further improved with addition of visual feedback (1.30 mm). SGRT for positioning did not improve setup accuracy, but slightly reduced the time for imaging and setup correction, allowing completion within 3.5 min for 95% of fractions., Conclusion: For accurate radiotherapy breast treatments using deep inspiration breath-hold, daily imaging and correction is required. SGRT provides accurate information on patient positioning during treatment and improves patient compliance with visual feedback., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The department has research collaborations with Elekta AB, Accuray Inc, VisionRT, and Varian Medical Systems., (© 2022 The Authors. Published by Elsevier B.V. on behalf of European Society for Radiotherapy & Oncology.)