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https://doi.org/10.21256/zhaw-19594Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Scheidegger, Stephan | - |
| dc.contributor.author | Fellermann, Harold | - |
| dc.date.accessioned | 2020-03-05T09:30:14Z | - |
| dc.date.available | 2020-03-05T09:30:14Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/19594 | - |
| dc.description.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. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Massachusetts Institute of Technology | de_CH |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | de_CH |
| dc.subject | Fractionation | de_CH |
| dc.subject | Tumour control probability | de_CH |
| dc.subject.ddc | 615: Pharmakologie und Therapeutik | de_CH |
| dc.title | Optimizing radiation therapy treatments by exploring tumour ecosystem dynamics in-silico | de_CH |
| dc.type | Konferenz: Paper | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Angewandte Mathematik und Physik (IAMP) | de_CH |
| dc.identifier.doi | 10.1162/isal_a_00167 | de_CH |
| dc.identifier.doi | 10.21256/zhaw-19594 | - |
| zhaw.conference.details | International Conference on Artificial Life (ALIFE), Newcastle, United Kingdom, 29 July - 2 August 2019 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.end | 242 | de_CH |
| zhaw.pages.start | 236 | de_CH |
| zhaw.parentwork.editor | Fellermann, Harold | - |
| zhaw.parentwork.editor | Bacardit, Jaume | - |
| zhaw.parentwork.editor | Goñi-Moreno, Ángel | - |
| zhaw.parentwork.editor | Füchslin, Rudolf M. | - |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.title.proceedings | Proceedings of the Artificial Life Conference 2019 | de_CH |
| zhaw.webfeed | Digital Health Lab | de_CH |
| zhaw.author.additional | No | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2019_Scheidegger-etal_Optimizing-radiation-therapy.pdf | 461.47 kB | Adobe PDF |  View/Open |
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Scheidegger, S., & Fellermann, H. (2019). Optimizing radiation therapy treatments by exploring tumour ecosystem dynamics in-silico [Conference paper]. In H. Fellermann, J. Bacardit, Á. Goñi-Moreno, & R. M. Füchslin (Eds.), Proceedings of the Artificial Life Conference 2019 (pp. 236–242). Massachusetts Institute of Technology. https://doi.org/10.1162/isal_a_00167
Scheidegger, S. and Fellermann, H. (2019) ‘Optimizing radiation therapy treatments by exploring tumour ecosystem dynamics in-silico’, in H. Fellermann et al. (eds) Proceedings of the Artificial Life Conference 2019. Massachusetts Institute of Technology, pp. 236–242. Available at: https://doi.org/10.1162/isal_a_00167.
S. Scheidegger and H. Fellermann, “Optimizing radiation therapy treatments by exploring tumour ecosystem dynamics in-silico,” in Proceedings of the Artificial Life Conference 2019, 2019, pp. 236–242. doi: 10.1162/isal_a_00167.
SCHEIDEGGER, Stephan und Harold FELLERMANN, 2019. Optimizing radiation therapy treatments by exploring tumour ecosystem dynamics in-silico. In: Harold FELLERMANN, Jaume BACARDIT, Ángel GOÑI-MORENO und Rudolf M. FÜCHSLIN (Hrsg.), Proceedings of the Artificial Life Conference 2019. Conference paper. Massachusetts Institute of Technology. 2019. S. 236–242
Scheidegger, Stephan, and Harold Fellermann. 2019. “Optimizing Radiation Therapy Treatments by Exploring Tumour Ecosystem Dynamics In-Silico.” Conference paper. In Proceedings of the Artificial Life Conference 2019, edited by Harold Fellermann, Jaume Bacardit, Ángel Goñi-Moreno, and Rudolf M. Füchslin, 236–42. Massachusetts Institute of Technology. https://doi.org/10.1162/isal_a_00167.
Scheidegger, Stephan, and Harold Fellermann. “Optimizing Radiation Therapy Treatments by Exploring Tumour Ecosystem Dynamics In-Silico.” Proceedings of the Artificial Life Conference 2019, edited by Harold Fellermann et al., Massachusetts Institute of Technology, 2019, pp. 236–42, https://doi.org/10.1162/isal_a_00167.
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