Mass General AI Predicts Cancer Survival From a Selfie — SkimNews
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- Mass General Brigham researchers developed the FAHR-Face system, trained on more than 40 million facial photographs spanning diverse ethnicities and ages, to estimate biological age and predict cancer survival from a single photo.
- The system produced two complementary tools: FAHR-FaceAge, which estimates biological age and predicts survival, and FAHR-FaceSurvival, which predicts mortality risk directly from facial photographs without routing through an age estimate.
- Among US cancer patients treated with radiotherapy, those whose biological age read as 5+ years older than their chronological age had a 21% higher mortality risk over an average four-year follow-up, while those looking 5+ years younger had a 17% lower risk.
- Co-senior author Hugo Aerts called FAHR-FaceSurvival the first demonstration that an AI model can directly predict cancer survival from a simple photograph, with the findings published in Lancet Digital Health.
- Each tool relies on different facial regions — FAHR-FaceAge on nasolabial creases, the forehead above the eye sockets, and temples; FAHR-FaceSurvival on the area below the eyes and around the nasal bridge.
- The model held up across pose, lighting, expression, makeup and most plastic surgery procedures, with only a small post-rhinoplasty shift, according to co-senior author Dr. Raymond Mak.
- Lifestyle factors including heavy alcohol use, drug use and smoking were all linked to looking biologically older in the AI analysis, the study found.
Why it matters: With cancer populations skewing older, oncologists need low-cost tools to gauge physical reserve before recommending intensive treatments like radiation, surgery or systemic therapy — radiation oncologist Mak said the goal is to make face-based health assessment quantitative and scalable enough to support personalized treatment decisions without replacing physician judgment.
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