Evidence map›Paper›PMID 42049711›Full record

ArticleNature communications2026

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

Fridolin Haugg, Grace Lee, John He, Andrew Warrington, Dennis Bontempi, Danielle S Bitterman, Paul J Catalano, Vasco Prudente, Suraj Pai, Christian Guthier and 3 more

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Fridolin Haugg *Department of Radiation Oncology, Mass General Brigham/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Grace Lee *Department of Radiation Oncology, Mass General Brigham/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
John HeDepartment of Radiation Oncology, Mass General Brigham/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.ORCID 0009-0002-9787-0426
Andrew WarringtonDepartment of Radiation Oncology, Mass General Brigham/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.ORCID 0009-0009-4830-5683
Dennis BontempiDepartment of Radiation Oncology, Mass General Brigham/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Danielle S BittermanDepartment of Radiation Oncology, Mass General Brigham/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Paul J CatalanoDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
Vasco PrudenteDepartment of Radiation Oncology, Mass General Brigham/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.ORCID 0000-0003-0623-1259
Suraj PaiDepartment of Radiation Oncology, Mass General Brigham/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-8043-2230
Christian GuthierDepartment of Radiation Oncology, Mass General Brigham/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Benjamin H KannDepartment of Radiation Oncology, Mass General Brigham/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-4313-2754
Hugo Jwl AertsDepartment of Radiation Oncology, Mass General Brigham/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-2122-2003
Raymond H MakDepartment of Radiation Oncology, Mass General Brigham/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. rmak@mgb.org.ORCID 0000-0002-8754-0565

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AgingFaceNeoplasmsAgedAlgorithmsArtificial IntelligenceFemaleHumansMaleMiddle AgedPhotographyPrognosis

Identifiers

PMID42049711
PMCPMC13125497

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.