AI 中文总结
本研究评估乳腺X线摄影基础模型能否在无心血管监督或BAC标注下，从筛查图像预测MACE，模型AUROC达0.82，优于年龄模型，提示其可作为机会性心血管风险评估工具。
AI 中文摘要
心血管疾病（CVD）仍然是女性死亡的主要原因，然而心血管风险评估通常依赖于临床变量，这些变量在常规护理中可能缺失、过时或不可用。筛查性乳腺X线摄影为机会性心血管风险分层提供了机会，因为它常规获取且包含与心血管风险和事件相关的血管特征，包括乳腺动脉钙化（BAC）。我们评估了原本为乳腺癌相关任务预训练的乳腺X线摄影专用基础模型，是否可以在没有心血管特定监督或明确BAC标注的情况下，迁移到心血管风险预测。我们构建了一个为期5年的主要不良心血管事件（MACE）队列，包含22,497名女性，与电子健康记录结果相关联，其中包括500个事件（2.22%的患病率）。基础模型实现了AUROC分别为0.823和0.822，显著超过了仅使用年龄的模型（AUROC 0.765），尽管仅使用筛查性乳腺X线摄影图像作为输入，没有使用任何临床变量。所评估的两个基础模型均将显著更高的预测风险分配给了有放射科医生记录的BAC的患者，尽管BAC从未用作训练标签，并且显示出与血管发现一致的激活模式。总之，这些发现表明，乳腺X线摄影基础模型可以直接从乳腺X线摄影像素中恢复临床相关的心血管风险信息，并提示筛查性乳腺X线摄影可能提供机会性的心血管风险信息来源，以补充常规临床评估，而无需额外的影像检查。代码可在以下https URL中获取。
英文摘要
Cardiovascular disease (CVD) remains the leading cause of death among women, yet cardiovascular risk assessment often relies on clinical variables that may be missing, outdated, or unavailable in routine care. Screening mammography offers an opportunity for opportunistic cardiovascular risk stratification because it is routinely acquired and contains vascular features, including breast arterial calcifications (BAC), that are associated with cardiovascular risk and events. We evaluate whether mammography specific foundation models, originally pretrained for breast cancer-related tasks, can transfer to cardiovascular risk prediction without cardiovascular specific supervision or explicit BAC annotation. We constructed a 5-year major adverse cardiovascular event (MACE) cohort of 22,497 women linked to electronic health record outcomes, including 500 events (2.22% prevalence). The foundation models achieved AUROCs of 0.823 and 0.822 substantially exceeding an age-only model (AUROC 0.765), despite using only the screening mammogram as input, with no clinical variables. Both foundation models evaluated assigned substantially higher predicted risk to patients with radiologist-documented BAC, despite BAC never being used as a training label, and showed activation patterns consistent with vascular findings. Together, these findings suggest that mammography foundation models can recover clinically relevant cardiovascular risk information directly from mammographic pixels and suggest that screening mammography may provide an opportunistic source of cardiovascular risk information to complement conventional clinical assessment without additional imaging. Code is available in https://github.com/PauFeld/MammoCVD
