Monte Carlo-based PET range verification for proton therapy in breast cancer patients with silicone implants: material assignment impact analysis

<i>Objective.</i>To validate a Monte Carlo (MC)-based offline positron emission tomography (PET) range verification framework and quantitatively evaluate range uncertainties associated with material assignment strategies, specifically focusing on the clinical practice of overriding silicone implants as adipose tissue in breast proton therapy.<i>Approach.</i>A FLUKA-based MC model was developed. Validation followed a stepwise program using (1) a PMMA phantom, (2) a homogeneous silicone block, and (3) a composite prosthesis-PMMA setup to verify intrinsic precision and material-specific accuracy. Additionally, a cohort of eight breast cancer patients (three with implants) was included for clinical demonstration. Range deviations were quantified by measuring the depth difference at the 50% distal activity falloff (ΔR50) between simulated and measured PET images. For silicone scenarios, material assignment impact was analyzed by overriding the implant as silicone, adipose tissue, or applying no override.<i>Main results.</i>The MC model demonstrated intrinsic millimeter-level accuracy. In homogeneous silicone phantom experiments, defining the material as silicone yielded the highest agreement with measurements (ΔR50: -0.05-0.88 mm). Reassigning silicone as adipose tissue introduced a systematic deviation but remained within acceptable limits (-1.24--0.83 mm), whereas applying no override resulted in the largest errors (-3.25--2.48 mm). Composite phantom results followed a similar trend. In clinical cases, the method successfully verified range delivery, although positioning uncertainties contributed to larger inter-field variability.<i>Significance.</i>This study validates an MC-based PET verification framework with 2 mm intrinsic accuracy, establishing it as a reliable tool for silicone-implanted breast cancer patients. The results quantitatively confirm that overriding silicone as adipose tissue keeps range uncertainty within 2 mm despite distinct elemental compositions, whereas failing to override the material leads to errors exceeding 3 mm.