WAM to SeeSaw model for cancer therapy – overcoming LQM difficulties –. (1st February 2021)

• Record Type: Journal Article
• Authors: Bando, Masako; Tsunoyama, Yuichi; Suzuki, Kazuyo; Toki, Hiroshi
• Is Part Of: International journal of radiation biology. Volume 97:Number 2(2021)
• Page Start: 228
• Page End: 239
• Publication Date: 2021-02-01
• Subjects: SeeSaw model -- WAM -- LQM -- radiotherapy -- dose rate -- risk estimation; Radiation -- Physiological effect -- Periodicals; Radiobiology -- Periodicals; 571.45
• DOI: 10.1080/09553002.2021.1854487
• Languages: English
• ISSNs: 0955-3002

Abstract

Abstract: Purpose: The assessment of biological effects caused by radiation exposure has been currently carried out with the linear-quadratic (LQ) model as an extension of the linear non-threshold (LNT) model. In this study, we suggest a new mathematical model named as SeaSaw (SS) model, which describes proliferation and cell death effects by taking account of Bergonie-Tribondeau's law in terms of a differential equation in time. We show how this model overcomes the long-standing difficulties of the LQ model. Materials and methods: We construct the SS model as an extended Wack-A-Mole (WAM) model by using a differential equation with respect to time in order to express the dynamics of the proliferation effect. A large number of accumulated data of such parameters as α and β in the LQ based models provide us with valuable pieces of information on the corresponding parameter b 1 and the maximum volume V m of the SS model. The dose rate b 1 and the notion of active cell can explain the present data without introduction of β, which is obtained by comparing the SS model with not only the cancer therapy data but also with in vitro experimental data. Numerical calculations are presented to grasp the global features of the SS model. Results: The SS model predicts the time dependence of the number of active- and inactive-cells. The SS model clarifies how the effect of radiation depends on the cancer stage at the starting time in the treatment. Further, the time dependence of the tumor volume is calculated by changing individual dose strength, which results in the change of the irradiation duration for the same effect. We can consider continuous irradiation in the SS model with interesting outcome on the time dependence of the tumor volume for various dose rates. Especially by choosing the value of the dose rate to be balanced with the total growth rate, the tumor volume is kept constant. Conclusions: The SS model gives a simple equation to study the situation of clinical radiation therapy and risk estimation of radiation. The radiation parameter extracted from the cancer therapy is close to the value obtained from animal experiment in vitro and in vivo . We expect the SS model leads us to a unified description of radiation therapy and protection and provides a great development in cancer-therapy clinical-planning.