| Title: | Projecting Individualized Probabilities of Lifetime Total Cancer Risk Across a Population |
| Journal: | JCO Oncology Advances |
| Published: | 1 Apr 2026 |
| DOI: | https://doi.org/10.1200/oa-25-00209 |
Publication 18127
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Abstract
PURPOSE A substantial proportion of cancers are preventable through established risk factor modification and evidence-based screening strategies. However, emerging screening technologies have created new challenges for clinicians in identifying which individuals may benefit most and how to contextualize these approaches within existing prevention frameworks. METHODS We projected individualized probabilities of 10-year and lifetime cancer risk across a population as well as potential improvement with healthy behaviors in the UK Biobank (UKB). RESULTS A total of 118 distinct variables were included across 38 cancer-specific models. The distribution of lifetime cancer risk had a rightward skew and wide variation for both men and women. The median lifetime cancer risk was 29.5% for men (IQR, 8.4%) and 21.0% for women (IQR, 8.8%). If all modifiable risk factors were set to the ideal state, this decreased to 20.5% for men (IQR, 3.9%) and 16.5% for women (IQR, 4.9%). There was considerable overlap between age groups, with men age 50-59 years at the 90th percentile having greater risk (11.9%) compared with men age 60-70 years at the 25th percentile (11.8%), and women age 40-49 years at the 90th percentile having greater risk (7.4%) compared with women age 50-59 years at the 60th percentile (6.8%) and women age 60-70 years at the 20th percentile (7.3%). CONCLUSION Lifetime cancer risk varies widely across the UKB cohort, but this risk decreases substantially with risk factor modification. There was considerable overlap in 10-year cancer risk between age groups, suggesting that discussion regarding future screening guidelines should account for more than age and sex as more evidence becomes available in the future. </p>
8 Authors
- Neel M. Butala
- Noor Al-Hammadi
- Asiri Ediriwickrema
- Jaime L. Schneider
- Aaron Fullerton
- Jeya Balasubramanian
- Parichoy Pal Choudhury
- Nilanjan Chatterjee
1 Application
| Application ID | Title |
|---|---|
| 176948 | Development of personalized models for lifetime risk of chronic disease using detailed phenotypic data in the UK Biobank |
Enabling scientific discoveries that improve human health