Any time there is new or emerging technology that might impact mortality curves, the insurance industry takes notice. And the promise of multi-cancer early detection (MCED) technology certainly ticks that box.
However, the data from the field’s most high-profile trial suggests that the technology has yet to produce the definitive ‘signal’ required to influence long-term mortality modelling.
The NHS-Galleri trial, a three-year study involving 142,000 participants, aimed to demonstrate that multi-cancer early detection (MCED) could significantly reduce late-stage (Stage III and IV) cancers. GRAIL, the biotechnology company that produces the Galleri test, announced in February that the trial didn’t meet the primary endpoint.
The bar in the medical field is high, however, and the results weren’t entirely without merit. Despite the primary endpoint miss, GRAIL maintains that the data reflects a “favorable trend” in specific areas. In an emailed statement to Longevity and Mortality Investor, a GRAIL spokesperson noted that while the trial didn’t meet the initial goal of reducing combined Stage III and IV cancers, the data showed a more than 20% reduction in Stage IV diagnoses for 12 deadly cancers over repeated rounds of screening.
The company also highlighted a decrease in cancers first identified during emergency room visits—a factor often linked to higher costs and worse patient outcomes. GRAIL expects to provide more detailed results at the ASCO 2026 Annual Meeting at the end of May.
For the insurance industry, however, the news serves as a practical illustration of the gap between clinical promise and the actuarial hurdles around screening broad populations. Even in areas where cancer screening is more established, such as with mammograms, the challenge of ‘noise’ in the data persists; false positive results can lead to unnecessary and/or costly follow-up interventions.
The difficulty is compounded when shifting from diagnostic testing to screening a low-risk, asymptomatic population such as the one in the Galleri trial. In these groups, the lower prevalence of disease statistically reduces the reliability of a positive result, which is a hurdle that is amplified when testing for multiple types of cancer simultaneously.
“There’s unfortunately no way to get around the mathematical reality that when you do a test in a low-risk population, the positive predictive value is negatively affected,” said Dr. Steven Rigatti, an independent life insurance medical director and Founder of Rigatti Risk Analytics in Connecticut.
“It’s just math – you can’t really do anything about that except make a test that’s so good that it virtually never calls anybody positive when they’re negative and negative when they’re positive. That’s extremely hard to do.”
Despite any statistical challenges, the industry continues to model the potential upside of a successful rollout of MCED technology. In a study of the likely impact of MCEDs on mortality and morbidity in the UK, the US, and Hong Kong, reinsurer RGA concluded that earlier detection could result in a measurable shift in mortality curves.
“Results from mortality impact modeling suggest a tangible reduction in mortality rates, with the greatest reductions in cancer mortality occurring at the ages with the highest expected screening rates,” the report stated.
However, the report also introduced significant caveats regarding over-diagnosis and over-treatment, which are factors that could complicate morbidity claims and healthcare costs. RGA also raised the critical question of clinical utility: the benefit of early detection is significantly diminished for cancers that currently lack effective treatment paths.
The conclusion for insurers is one of continued vigilance rather than immediate action.
“The full extent of the possible benefits and harms… are not yet known, and a cost-benefit analysis of performing the tests has yet to be carried out,” the report noted, suggesting that the era of MCED-adjusted pricing is not yet here.
That sense of caution permeates the discussion around MCEDs. For mortality and longevity modelling, the challenge lies in distinguishing between life-extending intervention and the detection of “incidental” tumours. These slow-growing, non-lethal cancers might never have caused symptoms or death, meaning their detection, while technically a success for the test, might lead to unnecessary treatment without actually shifting the mortality curve.
Furthermore, the current economic requirements for repeated MCED testing may present a barrier to the consistent, multi-year monitoring necessary to produce a robust longitudinal data set. Without high-frequency, widespread adoption across a broad cohort, the resulting data signals may remain fragmented, making the establishment of clear, long-term trends that are required to justify adjustments to mortality assumptions difficult for risk modellers.
What is not in doubt is the promise of the concept, but medical science is still at the starting line of the MCED era. Given the actuarial requirements needed to include any dataset into a model, the likelihood is that it is premature to factor these early signals into longevity calculations at the moment.
“It’s a slippery target. Things are proving that detecting occult cancer in people is a hard job and not everybody’s cancers are the same even in the same organ,” said Dr. Rigatti.
“But there’s certainly a lot of tests out there and I’m still hopeful that somebody is going to hit on something that’s more broadly useful. Would I necessarily start pricing it in [to mortality calculations]? Perhaps not. My habits, developed over years in the industry, say you want to wait to see something that demonstrates that signal.”
