Shin Sujeong, Chang Yoosoo, Ryu Seungho
Department of Family Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Breast Cancer. 2025 Sep 3. doi: 10.1007/s12282-025-01772-w.
Although numerous breast cancer risk prediction models have been developed to categorize individuals by risk, a substantial gap persists in evaluating how well these models predict actual mortality outcomes. This study aimed to investigate the association between Mirai, a deep learning model for risk prediction based on mammography, and breast cancer-specific mortality in a large cohort of Korean women.
This retrospective cohort study examined 124,653 cancer-free women aged ≥ 34 years who underwent mammography screening between 2009-2020. Participants were stratified into tertiles by Mirai risk scores and categorized into four groups based on risk changes over time. Cox proportional hazards regression models were used to evaluate the associations of both baseline Mirai scores and temporal risk changes with breast cancer-specific mortality.
Over 1,075,177 person-years of follow-up, 31 breast cancer-related deaths occurred. The highest Mirai risk tertile showed significantly higher breast cancer-specific mortality than the lowest tertile (hazard ratio [HR], 5.34; 95% confidence interval [CI] 1.17-24.39; p for trend = 0.020). Temporal Mirai score changes were associated with mortality risk: those remaining in the high-risk (HR, 5.92; 95% CI 1.43-24.49) or moving from low to high risk (HR, 5.57; 95% CI 1.31-23.63) had higher mortality rates than those staying in low-risk.
The Mirai model, developed to predict breast cancer incidence, was significantly associated with breast cancer-specific mortality. Changes in Mirai risk scores over time were also linked to breast cancer-specific mortality, supporting AI-based risk models in guiding risk-stratified screening and prevention of breast cancer-related deaths.
尽管已经开发了许多乳腺癌风险预测模型来按风险对个体进行分类，但在评估这些模型对实际死亡率结果的预测效果方面仍存在很大差距。本研究旨在调查基于乳房X线摄影的深度学习风险预测模型Mirai与大量韩国女性队列中乳腺癌特异性死亡率之间的关联。
这项回顾性队列研究检查了124,653名年龄≥34岁的无癌女性，她们在2009年至2020年期间接受了乳房X线摄影筛查。参与者按Mirai风险评分分为三分位数，并根据随时间的风险变化分为四组。使用Cox比例风险回归模型来评估基线Mirai评分和时间风险变化与乳腺癌特异性死亡率之间的关联。
在超过1,075,177人年的随访中，发生了31例与乳腺癌相关的死亡。Mirai风险最高的三分位数显示出比最低三分位数显著更高的乳腺癌特异性死亡率（风险比[HR]，5.34；95%置信区间[CI]1.17-24.39；趋势p值=0.020）。Mirai评分随时间的变化与死亡风险相关：那些保持高风险（HR，5.92；95%CI 1.43-24.49）或从低风险转变为高风险（HR，5.57；95%CI 1.31-23.63）的人比那些保持低风险的人有更高的死亡率。
为预测乳腺癌发病率而开发的Mirai模型与乳腺癌特异性死亡率显著相关。Mirai风险评分随时间的变化也与乳腺癌特异性死亡率相关，支持基于人工智能的风险模型指导风险分层筛查和预防乳腺癌相关死亡。
