## 4 Discussion  
The advent of AI is revolutionising the RT clinical workflow, opening new opportunities in terms of research and clinical innovation to enhance the clinical impact of this cutting-edge discipline. The development of single-session workflow is a new frontier in radiotherapy, not imaginable a few years ago and now made possible by the advent of AI, that will reduce treatment waiting times, offering an important perspective on speeding up the treatment time of patients subject to palliative care. The application of Deep Learning techniques to generate synthetic CT from Cone Beam Computed Tomography images, bypassing the traditional CT simulation, has been explored by various research groups across different treatment sites and DL architectures. [PERSON] et al. were one of the first groups to propose a DL solution in this framework, implementing in 2020 a U-Net architecture able to guarantee a MAE of 18.98 HU in the case of patients affected by head and neck tumours [24]. In the last few years, some studies have also investigated the differences among different DL architectures. [PERSON] et al. compared U-Net, CycleGAN, and cGAN (in the paper stated as pix2 pix) for sCT generation from breast CBCT images, obtaining better results using the U-Net architecture in terms of image similarity and dose accuracy [25]. While various experiences related to the pelvic site have been reported in the literature, a limited number have meticulously investigated the aspects related to dose accuracy. CycleGAN architecture was one of the most diffused solutions; this was adopted by different research groups on paired [26] and unpaired [27] data, yielding significant results in terms of image quality (the best reported values were 16.1 for MAE and 14.6 HU for ME), but without presenting results in terms of dose accuracy. The superiority of GAN over U-Net and CycleGAN in the pelvis was recently demonstrated by [PERSON] et al., who limited the dose evaluation to a single example case. This paper focused on a comprehensive dose accuracy analysis to assess the possibility of integrating this approach into clinical radiotherapy practice. The results of this study indicated that GAN-based solutions can rapidly and reliably generate synthetic CT images, ensuring high dose accuracy in both palliative and curative cases. This opens the door to the effective implementation of clinical workflows, removing CT simulation from clinical practice. Notably, our proposed approach maintains high dose accuracy even in the challenging scenario of treatment plans for bone metastasis, where Hounsfield Unit attribution in bone regions is inherently uncertain. Unlike the experiences of sCT generation on MR-Linac, where MR and CT images are acquired on the same day, in CBCT-focused studies, it is common for CBCT and  
Figure 5: Gamma passing rates calculated for the whole test cohort considering the three tolerance criteria: 1%/1 mm, 2%/2 mm, and 3%/3 mm. CT images to be acquired on different days. To reduce potential bias due to errors in HU estimation not attributable to the neural network's performance but simply to differences in patient positioning or anatomical conditions, a preliminary visual consistency analysis on the test set was conducted to select only cases where a reliable matching was presented between first-day CBCT and simulation CT. The present study suffers from different limitations that should be addressed before moving towards full implementation of the algorithm in clinical practice. Firstly, the network's performance should be evaluated on a larger cohort of patients, including patients acquired using other Varian Ethos systems, to assess the generalisability of the network to similar systems. Additional validation should be conducted using CBCT images acquired with other RT machines to evaluate the applicability of this network to other conventional linear accelerators. A larger, multicentric validation is one of the future directions of this study.