## 1 Introduction  
Nowadays, several commercially available Treatment Planning Systems (TPSs) incorporate specific engines for automated solution calculation able to optimize the treatment personalization process, thus improving quality and consistency among treatment plans and reducing the workload of the Department of Medical Physics. Several CE- and/or FDA-approved algorithms have been proposed and integrated into commercial TPSs. These are often classified into three categories: (a) automated rule implementation and reasoning (ARIR) [1, 2, 3, 4, 5, 6], multicriteria optimization (MCO) [7, 8, 9, 10, 11, 12, 13], and knowledge-based planning (KBP) [14, 15]. The plans derived from the automated planning modules are generally indistinguishable or non-inferior to manual ones [16] and represent a solid planner-independent starting point for further optimization. Among automated solutions, the protocol-based automatic iterative optimization algorithms are commercially included in the Pinnacle3 TPS in the autoplanning module [1]. In particular, the algorithm exploits an iterative approach of progressive optimization to mimic all the steps typically performed by experienced and skilled planners. In this case, the automated planning is based on the creation of templates (defined as \"techniques\" in Pinnacle) in which all the plan parameters (i.e., beam setup, dose prescriptions, and planning objectives) are customized for each treatment protocol and site. During the inverse planning phase, autoplanning defines all the optimization objectives from the indicated goals according to their respective priority and automatically generates the dummy structures typical of manual planning. Ad hoc cost functions are then used to define new objectives on these structures to achieve the desired dose distribution regarding PTV coverage and uniformity, managing cold and hot spots and dose fall-off outside the target and OAR sparing. Recently, the autoplanning module has been successfully applied on several treatment sites, as published in [1; 3; 4; 5], and a comprehensive evaluation of the algorithm has been reported by [PERSON] et al. in [6], who demonstrated that the use of the Autoplanning engine could lead to the development of high-quality treatment. Another step forward in the selection of personalized organ-at-risk (OAR) planning goals was based on a priori estimation of the best possible sparing according to delineated targets, and OAR unit feasibility represents an idealized dose distribution, assuming 100% target coverage, a dose gradient determined by energy-dependent fall off, and a uniform 360\({}^{\circ}\) low-dose spread [17]. It is a theoretical benchmark for plan quality to reflect optimal target coverage and OAR sparing without modeling beam entry, scatter, or machine-specific constraints. The adoption of the feasibility DVH approach might enhance the efficiency of knowledge-based model building for treatment planning [18] and identifying personalized constraints [19]. More recently Philips introduced the personalized planning engine in Pinnacle Evolution (version 16.4.3). Personalized planning employs the next generation of automated planning algorithms and integrates the feasibility DVH (fDVH) and the definition of treatment techniques to speed up the ARIR approach [20]. Although the applicability of personalized planning was evaluated for several cancer districts [20; 21; 22; 23], the studies mentioned above lack a deep investigation of the impact of every single adjustable advanced parameter on dose distribution, plan complexity, and delivery accuracy. However, we observed that employing specific advanced parameters can enhance or compromise absorbed dose distribution, using the same initial optimization goals for the target and OAR based on fDVH. Thus, a comprehensive understanding of possible input parameters is required during the commissioning steps to reduce the medical physicist's workload while ensuring the creation of the optimal treatment plan template for each specific district.