Simple SummaryWe investigated the benefits of adding lifestyle and familial risk factors to a mammographic image-derived short-term AI risk model in a 10-year follow-up study for its potential use in personalized screening and prevention of breast cancer (BC). In a case–cohort study, 8110 women were selected from women aged 40–74 participating in a Swedish mammography screening cohort. The women had no BC diagnosis at enrollment. In all, 1661 incident BCs were developed in the case–cohort. The lifestyle/familial-expanded AI risk model showed a significantly higher discriminatory performance in the long term and short term than the imaging-only risk model and the clinical Tyrer–Cuzick v8 model. The expanded model also showed the highest risk classification performance using positive predictive value (PPV). The results suggest that a lifestyle/familial-expanded image-derived AI risk model could most efficiently refine the identification of women who may benefit from personalized screening and/or risk-reducing intervention.AbstractBackground: Image-derived artificial intelligence (AI) risk models have shown promise in identifying high-risk women in the short term. The long-term performance of image-derived risk models expanded with clinical factors has not been investigated. Methods: We performed a case–cohort study of 8110 women aged 40–74 randomly selected from a Swedish mammography screening cohort initiated in 2010 together with 1661 incident BCs diagnosed before January 2022. The imaging-only AI risk model extracted mammographic features and age at screening. Additional lifestyle/familial risk factors were incorporated into the lifestyle/familial-expanded AI model. Absolute risks were calculated using the two models and the clinical Tyrer–Cuzick v8 model. Age-adjusted model performances were compared across the 10-year follow-up. Results: The AUCs of the lifestyle/familial-expanded AI risk model ranged from 0.75 (95%CI: 0.70–0.80) to 0.68 (95%CI: 0.66–0.69) 1–10 years after study entry. Corresponding AUCs were 0.72 (95%CI: 0.66–0.78) to 0.65 (95%CI: 0.63–0.66) for the imaging-only model and 0.62 (95%CI: 0.55–0.68) to 0.60 (95%CI: 0.58–0.61) for Tyrer–Cuzick v8. The increased performances were observed in multiple risk subgroups and cancer subtypes. Among the 5% of women at highest risk, the PPV was 5.8% using the lifestyle/familial-expanded model compared with 5.3% using the imaging-only model,p< 0.01, and 4.6% for Tyrer–Cuzick,p< 0.01. Conclusions: The lifestyle/familial-expanded AI risk model showed higher performance for both long-term and short-term risk assessment compared with imaging-only and Tyrer–Cuzick models.