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Publication type: Conference paper
Type of review: Peer review (publication)
Title: Optimizing radiation therapy treatments by exploring tumour ecosystem dynamics in-silico
Authors: Scheidegger, Stephan
Fellermann, Harold
et. al: No
DOI: 10.21256/zhaw-19594
Proceedings: Alife 2019 : proceedings of the Artificial Life Conference 2019
Editors of the parent work: Fellermann, Harold
Bacardit, Jaume
Goñi-Moreno, Ángel
Füchslin, Rudolf M.
Pages: 236
Pages to: 242
Conference details: Alife 2019, Newcastle, United Kingdom, 29 July - 2 August 2019
Issue Date: 2019
Publisher / Ed. Institution: Massachusetts Institute of Technology
Language: English
Subjects: Fractionation; Tumour control probability
Subject (DDC): 615: Pharmacology and therapeutics
Abstract: In this contribution, we propose a system-level compartmental population dynamics model of tumour cells that interact with the patient (innate) immune system under the impact of radiation therapy (RT). The resulting in silico - model enables us to analyse the system-level impact of radiation on the tumour ecosystem. The Tumour Control Probability (TCP) was calculated for varying conditions concerning therapy fractionation schemes, radio-sensitivity of tumour sub-clones, tumour population doubling time, repair speed and immunological elimination parameters. The simulations exhibit a therapeutic benefit when applying the initial 3 fractions in an interval of 2 days instead of daily delivered fractions. This effect disappears for fast-growing tumours and in the case of incomplete repair. The results suggest some optimisation potential for combined hyperthermia-radiotherapy. Regarding the sensitivity of the proposed model, cellular repair of radiation-induced damages is a key factor for tumour control. In contrast to this, the radio-sensitivity of immune cells does not influence the TCP as long as the radio-sensitivity is higher than those for tumour cells. The influence of the tumour sub-clone structure is small (if no competition is included). This work demonstrates the usefulness of in silico – modelling for identifying optimisation potentials.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Mathematics and Physics (IAMP)
Appears in Collections:Publikationen School of Engineering

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