## 4 Discussion
### Plan Complexity and Delivery Accuracy  
Modern planning techniques employ complex calculation algorithms to obtain the best dose distribution for a certain patient. Unfortunately, the planning optimization phase is vulnerable to several technical aspects, such as small-field modeling, dose calculation accuracy, tongue-and-groove effect, interleaf leakage, and transmission [40; 41]. All these issues highly impact the LINAC model developed and used by the TPS for plan calculation, and in order to verify if the LINAC can actually deliver the calculated dose distribution, patient-specific QA has been strongly recommended by both the AAPM and the ASTRO [30; 42] and is routinely performed in our center. Considering that the agreement between calculated and measured complex dose distributions is expected to decrease as plan modulation increases, [PERSON] et al. [31] evaluated the ability of the MCS index, calculated from the plans through the LINAC Watch software, to predict plan deliverability considering a logistic regression model trained and tested on an extensive plan population optimized using an autolanning module. In our study, a complexity evaluation was conducted using the LINAC Watch software to highlight the influence of the single parameter change for the plan calculation on the MCS value. In 85% of the calculated plans using personalized planning, the MCS was higher than 0.14. However, no relationship between MCS and experimental \(\gamma\)-passing rate can be identified from the results reported in this study, and the MCS = 0.14 threshold seems to be not valid in our case. This is likely because the abovementioned threshold was found for plans optimized using an autolanning module and the \(\gamma\)-passing rates derived by LINAC Watch. Even if much more work needs to be performed in this direction, there are not yet considered aspects whose contribution must be investigated. In particular, considering Elekta LINACs, the gantry speed (directly connected to the dose rate at each control point) should be taken into account when plan characteristics are related to delivery accuracy. In fact, when the required modulation is so high that the increase in leaf speed is insufficient, Pinnacle tends to create control points with a very low dose rate at which the LINAC pauses the rotation to allow the MLC to create the desired configuration. In these operations, the gantry position is not stable as expected and a tilting movement is usually observed and could highly affect the measured dose and, accordingly, the agreement with the calculated dose distribution. In this respect, a new complexity index, also considering, the gantry movement characteristics and the dose rate at each control point, could be more effective in dose deliverability prediction. As already mentioned, much more work must be performed to understand and address the entire problem fully. Moreover, it is essential to note that the MCS does not account for other critical delivery factors, such as gantry speed, which can significantly influence the agreement between calculated and delivered plans. Using autoplanning and a maximum delivery time of 90 s, the generated plans were suboptimal and did not respect the PTV coverage and OAR goals, while the generated personalized plans were developed to be theoretically deliverable in 90 s.  
In the investigated cases, a 40%/cm default for the target transition fall-off rate works well, but in specific situations where the dose gradients are expected to be stepper, the target transition fall-off rate could be increased to 70-90%/cm [31]. In personalized planning, full VMAT arcs (\(\sim\)360\({}^{\circ}\)) usually take 60-90 s, depending on complexity. The parameter maximum delivery time adheres more rigidly to user-defined times while ensuring deliverability. Reducing gantry rotation time (e.g., to 60 s) can increase MLC speed but demands higher dose rate modulation.