旨在验证 Mirai（一种基于乳房 X 光检查的深度学习模型）在预测墨西哥女性 1-5 年内患乳腺癌风险方面的性能。这项回顾性单中心研究纳入了 2014 年 1 月期间接受筛查性乳房 X 光检查的墨西哥女性的乳房 X 光检查2016 年 12 月。对于研究期间连续接受乳房 X 光检查的女性，仅包括初始乳房 X 光检查。病理学和影像学随访作为参考标准。评估了整个数据集中的模型性能，包括一致性指数（C-Index）和受试者工作特征曲线下面积（AUC）。 Mirai 在 AUC 方面的性能也在乳房 X 线摄影系统（Hologic 与 IMS）之间进行了评估。通过确定 Mirai 连续风险指数的截止点来评估临床实用性，该指数基于确定高风险类别中前 10% 的患者。在 3110 名患者中（中位年龄 52.6 岁 ± 8.9 岁），在整个 5 年随访中在此期间，3034 名患者没有患癌症，而 76 名患者患上了乳腺癌。 Mirai 整个数据集的 C 指数为 0.63（95% CI：0.6-0.7）。 Mirai 在 Hologic 亚组（0.63 [95% CI: 0.5-0.7]）中实现了比 IMS 亚组（0.55 [95% CI: 0.4-0.7]）更高的平均 C 指数。研究显示，以 Mirai 指数评分 > 0.029（10% 阈值）来识别高风险个体，高风险组中的个体患乳腺癌的风险是低风险组中的近三倍。 Mirai 在预测墨西哥女性未来乳腺癌方面表现中等。未来的努力应该完善和应用 Mirai 模型，特别是针对目前未作为常规筛查目标的少数民族人群和 30 至 40 岁的女性。AI 模型的适用性对于非白人、少数族裔人口的研究仍然不足。 Mirai 模型与墨西哥女性未来的癌症事件有关。需要进一步研究来增强模型性能并建立使用指南。© 2024。作者。

To validate the performance of Mirai, a mammography-based deep learning model, in predicting breast cancer risk over a 1-5-year period in Mexican women.This retrospective single-center study included mammograms in Mexican women who underwent screening mammography between January 2014 and December 2016. For women with consecutive mammograms during the study period, only the initial mammogram was included. Pathology and imaging follow-up served as the reference standard. Model performance in the entire dataset was evaluated, including the concordance index (C-Index) and area under the receiver operating characteristic curve (AUC). Mirai's performance in terms of AUC was also evaluated between mammography systems (Hologic versus IMS). Clinical utility was evaluated by determining a cutoff point for Mirai's continuous risk index based on identifying the top 10% of patients in the high-risk category.Of 3110 patients (median age 52.6 years ± 8.9), throughout the 5-year follow-up period, 3034 patients remained cancer-free, while 76 patients developed breast cancer. Mirai achieved a C-index of 0.63 (95% CI: 0.6-0.7) for the entire dataset. Mirai achieved a higher mean C-index in the Hologic subgroup (0.63 [95% CI: 0.5-0.7]) versus the IMS subgroup (0.55 [95% CI: 0.4-0.7]). With a Mirai index score > 0.029 (10% threshold) to identify high-risk individuals, the study revealed that individuals in the high-risk group had nearly three times the risk of developing breast cancer compared to those in the low-risk group.Mirai has a moderate performance in predicting future breast cancer among Mexican women.Prospective efforts should refine and apply the Mirai model, especially to minority populations and women aged between 30 and 40 years who are currently not targeted for routine screening.The applicability of AI models to non-White, minority populations remains understudied. The Mirai model is linked to future cancer events in Mexican women. Further research is needed to enhance model performance and establish usage guidelines.© 2024. The Author(s).