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用于预测墨西哥女性乳腺癌风险的未来模型的验证

Validation of the Mirai model for predicting breast cancer risk in Mexican women.

作者信息

Avendano Daly, Marino Maria Adele, Bosques-Palomo Beatriz A, Dávila-Zablah Yesika, Zapata Pedro, Avalos-Montes Pablo J, Armengol-García Cecilio, Sofia Carmelo, Garza-Montemayor Margarita, Pinker Katja, Cardona-Huerta Servando, Tamez-Peña José

机构信息

School of Medicine and Health Sciences, Tecnologico de Monterrey, Monterrey, Nuevo León, México.

Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario "G. Martino," University of Messina, Messina, Italy.

出版信息

Insights Imaging. 2024 Oct 10;15(1):244. doi: 10.1186/s13244-024-01808-3.

Abstract

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

Mirai has a moderate performance in predicting future breast cancer among Mexican women.

CRITICAL RELEVANCE STATEMENT

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.

KEY POINTS

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.

摘要

目的

验证基于乳腺钼靶的深度学习模型Mirai在预测墨西哥女性1至5年内患乳腺癌风险方面的性能。

方法

这项回顾性单中心研究纳入了2014年1月至2016年12月期间接受乳腺钼靶筛查的墨西哥女性的乳腺钼靶图像。对于在研究期间有连续乳腺钼靶图像的女性,仅纳入初始乳腺钼靶图像。病理和影像随访作为参考标准。评估了整个数据集中模型的性能,包括一致性指数(C指数)和受试者操作特征曲线下面积(AUC)。还评估了Mirai在不同乳腺钼靶系统(Hologic与IMS)之间的AUC性能。通过根据识别高危类别中前10%的患者来确定Mirai连续风险指数的临界点,评估其临床实用性。

结果

在3110名患者(中位年龄52.6岁±8.9)中,在整个5年随访期内,3034名患者未患癌症,而76名患者患了乳腺癌。Mirai在整个数据集中的C指数为0.63(95%置信区间:0.6 - 0.7)。Mirai在Hologic亚组中的平均C指数(0.63 [95%置信区间:0.5 - 0.7])高于IMS亚组(0.55 [95%置信区间:0.4 - 0.7])。以Mirai指数评分>0.029(10%阈值)来识别高危个体,研究表明高危组个体患乳腺癌的风险几乎是低危组个体的三倍。

结论

Mirai在预测墨西哥女性未来患乳腺癌方面表现中等。

关键相关声明

前瞻性努力应完善并应用Mirai模型,特别是针对目前未纳入常规筛查目标的少数族裔人群以及30至40岁的女性。

要点

人工智能模型在非白人、少数族裔人群中的适用性仍研究不足。Mirai模型与墨西哥女性未来的癌症事件相关。需要进一步研究以提高模型性能并制定使用指南。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db3e/11466924/7f01b62d33eb/13244_2024_1808_Fig1_HTML.jpg

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