TY - JOUR
T1 - Optimization of radiation dosing schedules for proneural glioblastoma
AU - Badri, H.
AU - Pitter, K.
AU - Holland, E. C.
AU - Michor, F.
AU - Leder, K.
N1 - Publisher Copyright:
© 2015, Springer-Verlag Berlin Heidelberg.
PY - 2016/4/1
Y1 - 2016/4/1
N2 - 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.
AB - 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.
KW - Brain tumors
KW - Linear-quadratic model
KW - Nonlinear programming
KW - Radiotherapy
UR - https://www.scopus.com/pages/publications/84959200925
UR - https://www.scopus.com/pages/publications/84959200925#tab=citedBy
U2 - 10.1007/s00285-015-0908-x
DO - 10.1007/s00285-015-0908-x
M3 - Article
C2 - 26094055
AN - SCOPUS:84959200925
SN - 0303-6812
VL - 72
SP - 1301
EP - 1336
JO - Journal of Mathematical Biology
JF - Journal of Mathematical Biology
IS - 5
ER -