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. 2018 Oct 15:9:1445.
doi: 10.3389/fphys.2018.01445. eCollection 2018.

Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects

Antonella Belfatto  1 , Barbara Alicja Jereczek-Fossa  2   3 , Guido Baroni  1 , Pietro Cerveri  1
Affiliations

Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects

Antonella Belfatto et al. Front Physiol. .

Abstract

The need for radiotherapy personalization is now widely recognized, however, it would require considerations not only on the probability of control and survival of the tumor, but also on the possible toxic effects, on the quality of the expected 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 that allows selection of the most suitable irradiation regimen. We used a macroscale mathematical model, which includes active and necrotic tumor dynamics and the role of oxygenation to simulate the effects of different hypo-/hyper-fractional regimens using retrospective data of 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 a regression of 80%) minimizing the toxicity and maximizing the cost-effectiveness ratio. Despite the lack of direct validation, the simulation results are in agreement with the literature findings that suggest the need for hypo-fractionated regimens in case of aggressive tumor phenotypes. Finally, the paper suggests a possible exploitation of the model within a tool to support clinical decisions.

Keywords: cervical cancer; hypofractionation; mathematical model; oxygenation; radiotherapy; simulation.

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Figures

FIGURE 1
FIGURE 1
Model at glance. Upper panel: example of tumor evolution through time. Lower panel: active volume, necrotic volume, irradiation, and oxygenation interplay diagram.
FIGURE 2
FIGURE 2
Summary of the simulation scheme.
FIGURE 3
FIGURE 3
Example of active volume evolution. Viable tumor regression according to different constant-dose fractionation schemes for Patient B.
FIGURE 4
FIGURE 4
Non-constant dose administration – simulation results. The viable tumor regression is shown for each patient simulating a decreasing dose administration trend (left panel) and an increasing one (right panel).
FIGURE 5
FIGURE 5
Model supported treatment planning.
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References

    1. Ahmed K. A., Correa C. R., Dilling T. J., Rao N. G., Shridhar R., Trotti A. M., et al. (2014). Altered fractionation schedules in radiation treatment: a review. Semin. Oncol. 41 730–750. 10.1053/j.seminoncol.2014.09.012 - DOI - PubMed
    1. Astrahan M. (2008). Some implications of linear-quadratic-linear radiation dose-response with regard to hypofractionation. Med. Phys. 35 4161–4172. 10.1118/1.2969065 - DOI - PubMed
    1. Belfatto A., Riboldi M., Ciardo D., Cattani F., Cecconi A., Lazzari R., et al. (2016a). Kinetic models for predicting cervical cancer response to radiation therapy on individual basis using tumor regression measured in vivo with volumetric imaging. Technol. Cancer Res. Treat. 15 146–158. 10.1177/1533034615573796 - DOI - PubMed
    1. Belfatto A., Riboldi M., Ciardo D., Cattani F., Cecconi A., Lazzari R., et al. (2016b). Modeling the interplay between tumor volume regression and oxygenation in uterine cervical cancer during radiotherapy treatment. IEEE J. Biomed. Health Inform. 20 596–605. 10.1109/JBHI.2015.2398512 - DOI - PubMed
    1. Belfatto A., Riboldi M., Ciardo D., Cecconi A., Lazzari R., Jereczek-Fossa B. A., et al. (2016c). Adaptive mathematical model of tumor response to radiotherapy based on CBCT data. IEEE J. Biomed. Health Inform. 20 802–809. 10.1109/JBHI.2015.2453437 - DOI - PubMed