Track-structure modes in particle and heavy ion transport code system (PHITS): application to radiobiological research. (1st February 2022)

• Record Type: Journal Article
• Authors: Matsuya, Yusuke; Kai, Takeshi; Sato, Tatsuhiko; Ogawa, Tatsuhiko; Hirata, Yuho; Yoshii, Yuji; Parisi, Alessio; Liamsuwan, Thiansin
• Is Part Of: International journal of radiation biology. Volume 98:Number 2(2022)
• Page Start: 148
• Page End: 157
• Publication Date: 2022-02-01
• Subjects: Monte Carlo simulation -- track-structure simulation -- microdosimetry -- DNA damage; Radiation -- Physiological effect -- Periodicals; Radiobiology -- Periodicals; 571.45
• DOI: 10.1080/09553002.2022.2013572
• Languages: English
• ISSNs: 0955-3002

Abstract

Abstract: Purpose: In radiation physics, Monte Carlo radiation transport simulations are powerful tools to evaluate the cellular responses after irradiation. When investigating such radiation-induced biological effects, it is essential to perform track structure simulations by explicitly considering each atomic interaction in liquid water at the sub-cellular and DNA scales. The Particle and Heavy-Ion Transport code System (PHITS) is a Monte Carlo code which enables to calculate track structure at DNA scale by employing the track-structure modes for electrons, protons and carbon ions. In this paper, we review the recent development status and future prospects of the track-structure modes in the PHITS code. Conclusions: To date, the physical features of these modes have been verified using the available experimental data and Monte Carlo simulation results reported in literature. These track-structure modes can be used for calculating microdosimetric distributions to estimate cell survival and for estimating initial DNA damage yields. The use of PHITS track-structure mode is expected not only to clarify the underlying mechanisms of radiation effects but also to predict curative effects in radiation therapy. The results of PHITS simulations coupled with biophysical models will contribute to the radiobiological studies by precisely predicting radiation-induced biological effects based on the Monte Carlo approach.