## 4 Discussion
assessed the effect of the MUlimit in ten patients with breast cancer. The authors concluded that the MUlimit can be used in case of replanning to improve the mGPR, paying attention to the DVCs of OARs which could get compromised [17]. All of the optimization strategies investigated in this study are viable options to replan cases flagged by VQA and generate plans with lower complexity and an improved GPR. Our findings indicate that the optimal approach is the ASC_L, producing significant improvements in deliverability (MCS, pGPR, and mGPR) without compromising the dosimetric quality. Additionally, the MUlimit could be considered as well, being a general approach implemented across many TPSs. We highlight that the GPR criteria and action limit considered in this study (3%/1 mm, 95%) were stricter than those recommended by the AAPM TG-218 (3%/2 mm, 90%). In our clinical experience, only a minor fraction of the treatment plans delivered do not satisfy the recommended criteria [8]. Thus, with a view to continuous improvement in quality, we decided to train our ML model using more stringent criteria for PSQA. Specifically, VQA was added as an additional layer of control to our PSQA program (see Figure S1 in the Supplementary Material). The ten patients were selected based on the plan average pGPR given by our ML model. However, only one arc was found to be an actual failure (mGPR < 95%). Despite the expected specificity of the model being 0.90, this high false negative rate could be explained by the small sample size and the inherent uncertainty in the model predictions. As VQA is necessary to implement a targeted PSQA approach and reduce the workload of measurement-based PSQA, it is also crucial that the subsequent replannings do not slow down the radiotherapy workflow. Importantly, the automatable replanning strategies that we considered provided a means to re-optimize plans flagged by VQA and improve both the pGPR and mGPR. Given the results of our analysis, after re-optimization, the action limit on the pGPR was set to 90%. All plans were then considered acceptable by VQA. The automation of the replanning strategies is expected to reduce the QA workload by more than 10 h/month, assuming 10 patients are reoptimized per month and 1 h of work per plan. In busy clinical settings, real-time automatic replannings could be performed in the background before plan approval if additional licenses and computing power are available. Alternatively, replannings could be performed at the end of each working day. This study has some limitations. The improved deliverability and reduced complexity could improve patient safety due to reduced uncertainties in dose calculation and verification. However, long-term outcomes need to be monitored over a prolonged period in order to provide definitive conclusions on patient quality of life. Our ML model can be applied only to VMAT plans, and the applicability of the model to other treatment deliveries and settings remains an open question. Future investigations will need to address the applicability of the replanning strategies to other treatment regions and increase the number of cases, as well as considering a higher threshold of GPR (e.g., 97.5%) for continuous quality improvement.