Characterization and validation of a Monte Carlo code for independent dose calculation in proton therapy treatments with pencil beam scanning

We propose a method of creating and validating a Monte Carlo (MC) model of a proton Pencil Beam Scanning (PBS) machine using only commissioning measurements and avoiding the nozzle modeling. Measurements with a scintillating screen coupled with a CCD camera, ionization chamber and a Faraday Cup were used to model the beam in TOPAS without using any machine parameter information but the virtual source distance from the isocenter. Then the model was validated on simple Spread Out Bragg Peaks (SOBP) delivered in water phantom and with six realistic clinical plans (many involving 3 or more fields) on an anthropomorphic phantom. In particular the behavior of the moveable Range Shifter (RS) feature was investigated and its modeling has been proposed. The gamma analysis (3%,3 mm) was used to compare MC, TPS (XiO-ELEKTA) and measured 2D dose distributions (using radiochromic film). The MC modeling proposed here shows good results in the validation phase, both for simple irradiation geometry (SOBP in water) and for modulated treatment fields (on anthropomorphic phantoms). In particular head lesions were investigated and both MC and TPS data were compared with measurements. Treatment plans with no RS always showed a very good agreement with both of them (γ-Passing Rate (PR) > 95%). Treatment plans in which the RS was needed were also tested and validated. For these treatment plans MC results showed better agreement with measurements (γ-PR > 93%) than the one coming from TPS (γ-PR < 88%). This work shows how to simplify the MC modeling of a PBS machine for proton therapy treatments without accounting for any hardware components and proposes a more reliable RS modeling than the one implemented in our TPS. The validation process has shown how this code is a valid candidate for a completely independent treatment plan dose calculation algorithm. This makes the code an important future tool for the patient specific QA verification process.