Heterogeneity of tumor response has been established, and adaptive treatment

planning is necessary to properly account for this. We present a theoretical framework

for optimally adapting radiation therapy treatments to early radiation response

estimates as derived from pre- and mid-treatment functional imaging data. The

framework is based on the optimal stopping in radiation therapy (OSRT) framework.

Biological response is quantified using tumor control probability (TCP) and normal

tissue complication probability (NTCP) models, and these are directly optimized

for in the adaptation step. Two adaptation strategies are discussed: adaptive

uniform dose (de)-escalation and adaptive dose painting. In order to demonstrate

the OSRT framework, numerical results based on canine sinonasal patient data are

presented, using FLT PET imaging data and specific biological models. The results

show the possibilities of framework and the availability of the required mathematical

tools, and underline the need for reliable biomarkers and biological response models.