Date Published: June 28, 2019
Publisher: Public Library of Science
Author(s): Cameron Meaney, Marek Stastna, Mehran Kardar, Mohammad Kohandel, Natalia L. Komarova.
Glioblastomas are the most common primary brain tumours. They are known for their highly aggressive growth and invasion, leading to short survival times. Treatments for glioblastomas commonly involve a combination of surgical intervention, chemotherapy, and external beam radiation therapy (XRT). Previous works have not only successfully modelled the natural growth of glioblastomas in vivo, but also show potential for the prediction of response to radiation prior to treatment. This suggests that the efficacy of XRT can be optimized before treatment in order to yield longer survival times. However, while current efforts focus on optimal scheduling of radiotherapy treatment, they do not include a similarly sophisticated spatial optimization. In an effort to improve XRT, we present a method for the spatial optimization of radiation profiles. We expand upon previous results in the general problem and examine the more physically reasonable cases of 1-step and 2-step radiation profiles during the first and second XRT fractions. The results show that by including spatial optimization in XRT, while retaining a constant prescribed total dose amount, we are able to increase the total cell kill from the clinically-applied uniform case.
Glioblastomas are the most aggressive, and unfortunately most common, form of primary brain tumour [1–5]. They are characterized by rapid growth and invasiveness, yielding survival times that seldom exceed a year . Because of this, treatments for glioblastomas are swift and aggressive, usually involving a combination of surgical intervention, chemotherapy, and external beam radiation therapy (XRT). Furthermore, the tendency for recurrence of glioblastomas after surgery makes postoperative chemotherapy and XRT a crucial part of effective treatments. Although current treatment plans do often extend survival time, they are far from perfect and leave much room for improvement. However, while these efforts focus on optimal scheduling of radiotherapy, they do not include a similarly sophisticated spatial optimization.
In this paper we pose the question of how to spatially shape a sequence of XRT treatments to best eliminate tumour cells. To answer this question, we need to know (i) how the tumour grows in time; (ii) how it responds to treatment; and (iii) what constraints apply to radiation dosage. Answers to all questions need to be expressed in mathematical terms, which necessitates simplifications and approximations. We have relied on assumptions and mathematical models commonly used in the literature, and hope our general results are insensitive to choice of model.