Face aging rate quantifies change in biological age to predict cancer outcomes

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. Chronological age is a known predictor of cancer survival, but it cannot fully capture variations in biological aging rates. Here, the authors introduce Face Aging Rate (FAR) as a non-invasive biomarker that predicts biological age from serial facial photographs using artificial intelligence, which can serve as a prognostic factor in cancer patients.