## 3 Results
### Effect of Personalized Planning Parameters  
Considering personalized planning, varying the weighting balance, the target transition fall-off rate, and the target edge weight did not produce a high variation in MUs. On the contrary, high differences were observed in OAR DVHs when these parameters were varied, which has proven to be effective. The DVHs obtained from increasing values of the target transition fall-off rate are reported in Figure 4c. To summarize, for the impact of each advanced parameter investigated on the dose distribution (in terms of OAR sparing and PTV coverage) and on the total MUs necessary to deliver the plan, a degree of influence was expressed. In particular, the low grade (indicated as +) was associated with changes in DVH points (for at least one of the defined structures) below 2 Gy and MU variations not higher than 30 MUs; the medium grade (indicated as ++) was associated with changing in DVH points between 2 and 5 Gy and MU variations between 30 and 50 MUs; and finally, the high grade (indicated as ++) was associated with changing in DVH points higher than 5 Gy and MUs exceeding 50 MUs. In addition, we did not observe a statistically significant correlation between \"control MLC modulation\" and OAR doses, but setting the parameter to \"yes\" led to better results in terms of OAR sparing. Plans with MU level set to \"off\" were the most complex and had the highest MU values and should be avoided to reduce unnecessary complexity and produce plans with sub-optimal PSAA results. A 40%/cm default for the target transition fall-off rate works well for PCa and HNCa in which a difference in the prescribed doses can be reached in a few millimeters. An overview of the estimated degree of influence is reported in Table 4.  
Figure 4: The impact of (**a**) modulation amount, (**b**) normal tissue control mode, and (**c**) target transition fall-off rate on the DVHs of PCa plans. Finally, two treatment templates corresponding to plans fulfilling all the constraints and deliverable with high \(\gamma\)-passing rates were based for the following advanced parameters: \(\text{CLM}=0.58\text{ cm}/\text{deg}\); \(\text{MSA}=9\text{ cm}^{2}\); \(\text{CC}=8\); \(\text{RC}=3\); initialize with the conformal opening = no; control MLC modulation = yes; modulation amount: high; MU level: medium; normal tissue dose control = standard fractionation; dose fall-off priority (weight) = 1; weighting balance = 10; target transition fall-off rate (%/cm) = 40; match target coverage = \"yes\" for PCa and \"no\" for HNCa; target edge weight = 5. The difference in the match target coverage between PCa and HNCa can be explained by considering that it is effective in increasing the target coverage only if the target is uniform enough. This condition is generally fulfilled for PCa but not always for HNCa cases. The optimal advanced option parameter values for the proposed treatment templates were determined through a two-step iterative optimization process, ensuring robustness and adaptability across clinical scenarios. Based on the outcomes of the initial phase, the first optimized template was developed. This template was then subjected to a second round of refinement, where the interplay between parameters was fine-tuned to achieve optimal balance between target coverage, OAR sparing, and deliverability. The refined parameters were validated by applying the second template to a test set of clinical plans. The templates were validated using PSQA metrics, including gamma analysis and dosimetric endpoints, across a range of clinical settings. For example, the second optimized template was tested on anatomically complex HNCa cases and simpler PCa cases to confirm its robustness. The high passing rates and consistent results indicate the templates' reliability.