Abstract Chronological age predicts cancer survival but does not capture differences in biological aging rates. We apply FaceAge, an artificial intelligence algorithm that predicts biological age from a facial photograph, to serial clinical facial photographs to calculate the Face Aging Rate (FAR; change in FaceAge divided by the time between photographs). We analyze data from 2276 cancer patients receiving radiation therapy, using photographs captured during routine care. Higher FAR is associated with worse overall survival in stratified analyses of cohorts with the following intervals between photographs: short 10-365 days (adjusted hazard ratio [aHR] and 95% confidence interval: 1.25 [1.03-1.51]), mid 366–730 days (aHR: 1.37 [1.00-1.86]), and long 731-1,460 days (aHR: 1.65 [1.22-2.22]) after adjustment for time between photographs, sex, race, and diagnosis. FAR provides additional prognostic information beyond single time-point measures of FaceAge. FAR is a non-invasive prognostic biomarker that captures dynamic changes in biological aging. Similar content being viewed by others Introduction Aging is a multifaceted biological process characterized by the decline of physiological functions and increased vulnerability to disease and death1. It affects nearly all living organisms and is intricately linked with various diseases, particularly cancer, as both result from the accumulation of cellular damage over time2,3,4,5. Chronological age has long been recognized as a predictor of survival; this relationship is particularly evident in cancer patients6,7,8,9,10,11,12. However, it treats all individuals within an age group identically, disregarding variations in biological aging rates13. Recognizing these limitations, it becomes essential to explore biological age indicators that can be readily implemented into clinical practice, especially those that quantify the rate of aging and are easily accessible, for personalized risk assessment. Recent longitudinal evidence demonstrates that significant variation in biological aging trajectories can already be quantified in young adulthood, underscoring the potential for early interventions before disease manifestation14. This study aims to