Optimization of radiation dosing schedules for proneural glioblastoma

H. Badri, K. Pitter, E. C. Holland, F. Michor, K. Leder

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Glioblastomas are the most aggressive primary brain tumor. Despite treatment with surgery, radiation and chemotherapy, these tumors remain uncurable and few significant increases in survival have been observed over the last half-century. We recently employed a combined theoretical and experimental approach to predict the effectiveness of radiation administration schedules, identifying two schedules that led to superior survival in a mouse model of the disease (Leder et al., Cell 156(3):603–616, 2014). Here we extended this approach to consider fractionated schedules to best minimize toxicity arising in early- and late-responding tissues. To this end, we decomposed the problem into two separate solvable optimization tasks: (i) optimization of the amount of radiation per dose, and (ii) optimization of the amount of time that passes between radiation doses. To ensure clinical applicability, we then considered the impact of clinical operating hours by incorporating time constraints consistent with operational schedules of the radiology clinic. We found that there was no significant loss incurred by restricting dosage to an 8:00 a.m. to 5:00 p.m. window. Our flexible approach is also applicable to other tumor types treated with radiotherapy.

Original languageEnglish (US)
Pages (from-to)1301-1336
Number of pages36
JournalJournal of Mathematical Biology
Volume72
Issue number5
DOIs
StatePublished - Apr 1 2016

Bibliographical note

Funding Information:
HB is partially supported by NSF Grants CMMI-1362236. KL is partially supported by NSF Grants DMS-1224362 and CMMI-1362236. FM is partially supported by the Grant NIH U54CA143798. E.H is supported by NIH grants U54 CA143798 and U54CA163167-01. We would like to thank an anonymous referee for their helpful comments.

Publisher Copyright:
© 2015, Springer-Verlag Berlin Heidelberg.

Keywords

  • Brain tumors
  • Linear-quadratic model
  • Nonlinear programming
  • Radiotherapy

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