FaceAge as a biomarker for prognosis and treatment stratification in large-scale oncology cohort

BACKGROUND: Humans age at different rates and facial characteristics may yield insight into biological age and physiologic health. FaceAge, a deep learning system estimating biological age from facial photographs, has shown potential as a biomarker for cancer prognosis. This study investigates the prognostic value of extreme discordance between FaceAge and chronological age (FaceAge-Age) in predicting survival and early mortality across a large clinical dataset of 28 cancer types. METHODS: Data from 24,556 cancer patients aged =60 treated with radiation therapy between 2008-2023 were analyzed. FaceAge estimates were compared with chronological age across different diagnoses/clinical contexts, and survival analyses were performed. All tests were two-sided. RESULTS: FaceAge was older than chronological age in 65% (median FaceAge 74 versus age 70). Younger patients, female sex, diagnoses with worse prognosis, and treated for palliative intent had higher likelihood of FaceAge-Age =10 years. Patients with FaceAge-Age =10 years had significantly worse survival while those with FaceAge-Age =-5 years had better survival. On multivariate analysis, FaceAge-Age =10 years predicted higher mortality risk (HR 1.26, P<.001) and early mortality at 30 days (OR 1.38, P=.004) and 60 days (OR 1.33, P<.001), whereas FaceAge-Age =-5 years predicted lower mortality risk (HR 0.90, P<.001). CONCLUSIONS: Patients with more advanced cancers tend to have significantly older FaceAge compared with age, and extreme discordance between FaceAge and chronological age is a novel, independent predictor of survival and early mortality. These findings support further development of facial health assessments for clinical prognostication models and personalized treatment decision-making.