A typical fractionated radiotherapy (RT) course is a long and arduous process, demanding significant financial, physical, and mental commitments from patients. Each additional session of RT significantly increases the physical and psychological burden on patients and leads to higher radiation exposure in organs-at-risk (OAR), while, in some cases, the therapeutic benefits might not be high enough to justify the risks. Today, through technological advancements in molecular biology, imaging, and genetics more information is gathered about individual patient response before, during, and after the treatment. Personalized RT aims at using these biological information to better understand the differences in patient-specific response, and ultimately, to individualize the treatment plan based on these differences. In the first part of the talk, we will review the basic concepts of a mathematically-inspired biological framework for RT plan personalization, which we term “Optimal Stopping in RT (OSRT)'' after a similar concept in the fields of dynamic programming and Markov decision processes. In the second part, we will discuss how the OSRT framework could be applied in the clinic to personalize the treatment of liver SBRT patients. Specifically, we will highlight the potential to combine mid-treatment functional imaging (hepatocyte-specific contrast-enhanced MRI) and blood biomarkers (plasma cytokines) to predict the treatment outcome of individual patients, already during the RT course.