FaceAge as a biomarker for prognosis and treatment stratification in a large-scale oncology cohort
Bibliographic record
Abstract
BACKGROUND: Humans age at different rates, and facial characteristics may yield insight into biological age and physiologic health. FaceAge, a deep learning system designed to estimate biological age based on facial photographs, has shown potential as a biomarker for cancer prognosis. This study investigated 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 patients with cancer aged 60 years or older treated with radiation therapy between 2008 and 2023 were analyzed. FaceAge estimates were compared with chronological age across different diagnoses/clinical contexts, and survival analyses were performed. All tests were 2-sided. RESULTS: The FaceAge estimate was older than the chronological age in 65% of participants (median FaceAge = 74 years vs chronological age = 70 years). Younger patients, female patients, patients with diagnoses associated with worse prognosis, and patients receiving treatment with palliative intent had a higher likelihood of FaceAge - Age ≥10 years. Patients with FaceAge - Age ≥10 years had statistically significantly worse survival, while patients with FaceAge - Age ≤‒5 years had better survival. On multivariate analysis, FaceAge - Age ≥10 years predicted higher mortality risk (hazard ratio = 1.26; P < .001) and early mortality at 30 days (odds ratio = 1.38, P = .004) and 60 days (odds ratio = 1.33, P < .001), whereas FaceAge - Age ≤‒5 years predicted lower mortality risk (hazard ratio = 0.90; P = .002). CONCLUSIONS: Patients with more advanced cancers tend to have statistically significantly older FaceAge than chronological 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".