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Background and Purpose: COVID-19 constitutes a worldwide threat, prompting Italian Government to implement specific measures on March 8, 2020, to protect patients and health workers from disease transmission. The impact of preventive measures on daily activity of a radiotherapy facility may hamper the ability to fulfill normal workload burden. Thus, we assessed the number of delivered treatments in a specific observation period after the adoption of preventive measures (since March 11 to April 24, 2020) and compared it with the corresponding period of the year 2019., Materials and Methods: Overall number of delivered fractions was related to actual time of platform daily activity and reported as a ratio between number of delivered fractions and activity hours (Fr/Hrs). Fr/Hrs were calculated and compared for two different periods of time, March 11-April 24, 2019 (Fr/Hrs1), and March 11-April 24, 2020 (Fr/Hrs2)., Results: Fr/Hrs1 and Fr/Hrs2 were 2.66 and 2.54 for year 2019 and 2020, respectively, for a Fr/Hrs ratio of 1.07 (95% CI 1.03-1.12, p = 0.0005). Fr/Hrs1 was significantly higher than Fr/Hrs2 for Sli R and Precise R , with Fr/Hrs ratio of 1.92 (95% CI 1.66-2.23, p < 0.0001) and 1.11 (95% CI 1.03-1.2, p = 0.003), respectively. No significant difference was reported for Synergy R and Cyberknife R with Fr/Hrs ratio of 0.99 (95% CI 0.91-1.08, p = 0.8) and 0.9 (95% CI 0.77-1.06, p = 0.2), respectively. Fr/Hrs1 was significantly lower than Fr/Hrs2 for Tomotherapy R , with Fr/Hrs ratio of 0.88 (95% CI 0.8-0.96, p = 0.007)., Conclusion: Preventive measures did not influence workload burden performed. Automation in treatment delivery seems to compensate effectively for health workers number reduction.

Featured Application: The neural network proposed allows for the removal of the CT simulation from the clinical workflow, paving the way for fast-track radiotherapy. Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Computed Tomography (CT) images from low-dose Cone Beam CT (CBCT) acquired on a modern linear accelerator integrating AI. Methods: A total of 53 patients treated in the pelvic region were enrolled and split into training (30), validation (9), and testing (14). A Generative Adversarial Network (GAN) was trained for 200 epochs. The image accuracy was evaluated by calculating the mean and mean absolute error (ME and ME) between sCT and CT. RT treatment plans were calculated on CT and sCT images, and dose accuracy was evaluated considering Dose Volume Histogram (DVH) and gamma analysis. Results: A total of 4507 images were selected for training. The MAE and ME values in the test set were 36 ± 6 HU and 7 ± 6 HU, respectively. Mean gamma passing rates for 1%/1 mm, 2%/2 mm, and 3%/3 mm tolerance criteria were respectively 93.5 ± 3.4%, 98.0 ± 1.3%, and 99.2 ± 0.7%, with no difference between curative and palliative cases. All the DVH parameters analysed were within 1 Gy of the difference between sCT and CT. Conclusion: This study demonstrated that sCT generation using the DL approach is feasible on low-dose CBCT images. The proposed approach can represent a valid tool to speed up the online adaptive procedure and remove CT simulation from the RT workflow. [ABSTRACT FROM AUTHOR]