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• W2896581616 abstract "The need for radiotherapy personalization is nowadays widely recognized, however it would require considerations not only about the likelihood of tumor control and survival, but also about possible toxic effects, the expected quality of life, and the economic efficiency of the treatment. In this paper, we propose a simulation tool that can be integrated into a decision support system enabling the selection of the most suitable irradiation regimen. In this paper, we used a macroscale mathematical model, which includes active and necrotic tumor dynamics as well as the role of oxygenation to simulate the effects of different hypo- / hyper-fractional regimens on seven virtual patients from as many cervical cancer patients used for its training in a previous study. The results confirmed the heterogeneous response across the patients as a function of treatment regimen and suggested the tumor growth rate as a main factor in the final tumor regression. In addition to the maximum regression, another criterion was suggested to select the most suitable regimen (minimum number of fractions to achieve 80% regression) while minimizing the toxicity and maximizing the cost-effectiveness. Despite the lack of direct validation, the simulation results are in agreement with the literature findings suggesting the need for hypo-fractionated regimens in case of aggressive tumor phenotypes. Finally, the paper suggests a possible exploitation of the model within a clinical decision support tool." @default.
• W2896581616 created "2018-10-26" @default.
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• W2896581616 date "2018-10-15" @default.
• W2896581616 modified "2023-09-24" @default.
• W2896581616 title "Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects" @default.
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• W2896581616 doi "https://doi.org/10.3389/fphys.2018.01445" @default.
• W2896581616 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6197078" @default.
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• W2896581616 hasPublicationYear "2018" @default.
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