Quantifying the dosimetric impact of afterloader-specific source dynamics in HDR brachytherapy via 4D Monte Carlo...

This study aims to quantify the dosimetric impact of afterloader-specific source dynamics in high-dose-rate (HDR) interstitial prostate brachytherapy using time-resolved (4D) Monte Carlo (MC) simulations. While HDR brachytherapy is known for its ability to deliver conformal dose distributions via afterloader-driven radioactive sources, most treatment planning systems neglect dose contributions during source transit, i.e. when the source enters the body, moves between dwell positions, and returns to its safe position. To address this limitation, we modeled three widely used commercial HDR afterloaders (Varian GammaMedPlus, BEBIG MultiSource, and Elekta microSelectron), each with distinct source acceleration profiles, using detailed MC simulations implemented in TOPAS. Simulated treatment scenarios included varying needle and dwell point configurations, and transit dose contributions were evaluated for the tumor, prostate body, bladder, and rectum. The results indicate that increased needle and dwell counts lead to higher transit doses to both the target and organs at risk (OAR). Furthermore, systems with lower source acceleration produced significantly greater transit doses due to longer source travel times. Notably, the prostate tissue surrounding the tumor, referred to here as the prostate body, was shown to receive up to 14.2% of the prescribed tumor dose, with approximately 72% of that amount attributable to transit dose. This corresponds to a transit dose contribution of around 10.2% of the total prescription dose, underscoring the clinical relevance of transit effects even in non-target tissues. These findings demonstrate that neglecting source motion can result in clinically meaningful underestimation of OAR doses, particularly in treatment areas with limited separation between the target and sensitive normal tissues, and highlight the value of incorporating source transit effects into treatment planning for improved dose estimation in clinical practice.