With CT Imaging Biomarkers, Age Really Is Just a Number

Turning routine scans into a biological-age score to better predict health risks

BY NICK KLENSKE June 24, 2026

Lee

Pyrros

CT imaging can help address shortcomings that exist with current methods of calculating biological age and provide quantitative biomarkers of aging associated with survival, cardiovascular events, metabolic disease and fragility fractures.

“Imaging biomarkers are an underutilized tool that can improve our understanding of aging and age-related disease,” explained Matthew Lee, MD, a radiologist at the University of Wisconsin School of Medicine & Public Health, and co-author of a RadioGraphics review on CT biomarkers of aging.

From screenings to vaccines, chronological age drives many of our healthcare decisions. Yet it tells us very little about health and healthspan, the period of life free from disease and disability.

Filling this gap is biological age, which looks at the cumulative effects of age-related diseases not captured by years lived.

“While chronological age still holds sway in most clinical workflows, it is a crude measure, akin to judging a car’s reliability solely by its model year,” said Ayis Pyrros, MD, a radiologist with Duly Health and Care in Tinley Park, IL, who provided a commentary on the Radiographics review. “Biological age, by contrast, reflects actual wear and tear—the mileage, the maintenance history and whether the wheels are about to fall off.”

Calculating biological age is typically done using a variety of molecular and cellular omics associated with the underlying mechanisms of aging. These include epigenomics, transcriptomics, proteomics and metabolomics.

The problem is that these omics-based approaches face significant barriers in terms of clinical utility and practical translation, including high costs, limited accessibility and lack of actionable insights for clinical care.

CT biomarkers offer a practical alternative.

Older woman sitting in wheelchair in a physical therapy setting is accompanied by a doctor who is showing her medical imaging

Potential for Patient-Specific Risk Stratification

Individual CT biomarkers such as muscle attenuation for sarcopenia, bone mineral density for osteoporosis and aortic calcification for atherosclerosis are all recognized predictors of survival and clinical outcomes. They’re also valuable data that are present on CT scans that go unused in clinical practice.

“While these individual CT biomarkers are prognostically informative, they become stronger predictors of clinical outcomes when combined,” Dr. Lee noted. “This forms the foundation of how the biomarkers of aging can be used for CT-based biological age estimation.”

With the advent of fully automated deep learning tools, these quantitative body composition measures can now be extracted from existing CT examinations at scale, either retrospectively or prospectively, and without the need for additional imaging or testing.

“These features are not just aesthetic curiosities—they’re harbingers of morbidity and mortality, and their automated detection using explainable artificial intelligence opens new doors for patient-specific risk stratification,” Dr. Pyrros added.

Explainable AI techniques reveal the reasoning behind AI outputs, making the decisions understandable, transparent and trustworthy to humans.

“When folded into risk models alongside labs and comorbidities, biological age gives us a far more honest picture of who is actually vulnerable.”

— AYIS PYRROS, MD

A Paradigm Shift for Radiology

CT-based biological age has the potential to refine healthcare decision making and clinical intervention. For example, it could be used to identify high-risk individuals earlier. It could also enable secondary prevention efforts, supporting more precise counseling and prevention and providing a more individualized assessment of health than chronological age alone.

“When folded into risk models alongside labs and comorbidities, biological age gives us a far more honest picture of who is actually vulnerable,” Dr. Pyrros noted.

For radiologists, biological age represents a paradigm shift. “CT-based biological age expands the role and impact of imaging from disease detection to opportunistic, value-added health assessment with direct relevance to prevention, prognosis and longitudinal care that could transform the healthcare landscape,” Dr. Lee added.

However, for that to happen, more work needs to be done.

According to Drs. Lee and Pyrros, future research should prioritize integrating CT imaging biomarkers with other measures of biological age, including genomic, epigenomic and proteomic data. Specifically, head-to-head comparisons between CT-based phenotypic models and leading omics aging clocks are needed to clarify their relative and complementary value.

Such research could lead to the development of integrated, multimodal aging clocks that incorporate both molecular and imaging information.

From Unused to Actionable

To truly harness the clinical potential of imaging-derived aging biomarkers, they must be fully embedded into established care pathways. “The usual playbook of suggesting exercise or handing out a gym membership rarely moves the needle,” Dr. Pyrros said. “And, flagging sarcopenia or myosteatosis without follow-through is a missed opportunity.”

It also prompts a broader question about the responsibility assumed when these findings are flagged but not acted upon.

Instead, what’s needed are structured, actionable workflows: targeted nutritional support, supervised physical therapy, longitudinal tracking and closed-loop communication with the ordering clinician.

Dr. Lee said radiologists can drive this by pushing for structured reporting and co-building pathways with primary care, geriatrics and endocrinology where none exist.

“This is how imaging-derived aging biomarkers move from being reported and forgotten to providing insight and impact,” Dr. Pyrros concluded.