Long-term performance of an image-based short-term risk model for breast cancer
Open accessAbstract
PURPOSE Image-derived artificial intelligence–based short-term risk models for breast cancer have shown high discriminatory performance compared with traditional lifestyle/familial-based risk models. The long-term performance of image-derived risk models has not been investigated. METHODS We performed a case-cohort study of 8,604 randomly selected women within a mammography screening cohort initiated in 2010 in Sweden for women age 40-74 years. Mammograms, age, lifestyle, and familial risk factors were collected at study entry. In all, 2,028 incident breast cancers were identified through register matching in May 2022 (206 incident breast cancers were found in the subcohort). The image-based model extracted mammographic features (density, microcalcifications, masses, and left-right breast asymmetries of these features) and age from study entry mammograms. The Tyrer-Cuzick v8 risk model incorporates self-reported lifestyle and familial risk factors and mammographic density to estimate risk. Absolute risks were estimated, and age-adjusted AUC model performances (aAUCs) were compared across the 10-year period. RESULTS The aAUCs of the image-based risk model ranged from 0.74 (95% CI, 0.70 to 0.78) to 0.65 (95% CI, 0.63 to 0.66) for breast cancers developed 1-10 years after study entry; the corresponding Tyrer-Cuzick aAUCs were 0.62 (95% CI, 0.56 to 0.67) to 0.60 (95% CI, 0.58 to 0.61). For symptomatic cancers, the aAUCs for the image-based model were ≥0.75 during the first 3 years. Women with high and low mammographic density showed similar aAUCs. Throughout the 10-year follow-up, 20% of all women with breast cancers were deemed high-risk at study entry by the image-based risk model compared with 7.1% using the lifestyle familial-based model ( P < .01). CONCLUSION The image-based risk model outperformed the Tyrer-Cuzick v8 model for both short-term and long-term risk assessment and could be used to identify women who may benefit from supplemental screening and risk reduction strategies.
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AI risk prediction algorithms have outperformed traditional breast cancer risk models.
“These AI risk prediction algorithms have been shown to outperform traditional risk models (13,14)”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [14]
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Requesens, M. G., Mankan, A. K., Cantini, L., Calabresi, M., Vidal, L., Brown, J., Lang, S., Kaura, B., Veselkov, K., Xénarios, I., et al. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1885797
- Authors
- Eriksson, M., Czene, K., Vachon, C., Conant, E. F., & Hall, P.
- Affiliations
- Karolinska Institutet (SE)
- Mayo Clinic (US)
- University of Pennsylvania (US)
- Corresponding author
- Eriksson, Mikael
- Venue
- Journal of Clinical Oncology
- Published
- 2023-05-10
- Volume
- 41
- Issue
- 14
- Pages
- 2536-2545
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- PMC
- PMC10414699
- OpenAlex
- W4327697245
- Cited by
- 43
- References
- 40
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 12 · 2023: 7
- FWCI
- 7.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Journal of Clinical Oncology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Cancerfonden (CAN 2015/649, 19 0267)
- Stockholms Läns Landsting (20200102)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- breast cancer risk prediction, breast density, microcalcifications, breast mass, breast asymmetry, mammography screening, supplemental screening
- MeSH terms
- Breast Density, Adult, Aged, Artificial Intelligence, Breast, Breast Neoplasms, Female, Humans, Mammography, Middle Aged, Risk Factors, Cohort Studies, Risk Assessment, Genetic Predisposition to Disease
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