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

IF 4.1 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Insights into Imaging Pub Date : 2024-10-10 DOI:10.1186/s13244-024-01808-3
Daly Avendano, Maria Adele Marino, Beatriz A Bosques-Palomo, Yesika Dávila-Zablah, Pedro Zapata, Pablo J Avalos-Montes, Cecilio Armengol-García, Carmelo Sofia, Margarita Garza-Montemayor, Katja Pinker, Servando Cardona-Huerta, José Tamez-Peña
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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.

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验证用于预测墨西哥妇女乳腺癌风险的 Mirai 模型。
目的验证基于乳腺 X 射线照相术的深度学习模型 Mirai 在预测墨西哥妇女 1-5 年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 月至 2016 年 12 月期间接受乳房 X 光筛查的墨西哥女性的乳房 X 光照片。对于在研究期间连续接受乳房X光检查的女性,仅纳入首次乳房X光检查。病理和成像随访作为参考标准。对整个数据集的模型性能进行了评估,包括一致性指数(C-Index)和接收者工作特征曲线下面积(AUC)。还评估了不同乳腺 X 射线摄影系统(Hologic 与 IMS)之间 Mirai 的 AUC 性能。通过确定Mirai连续风险指数的临界点来评估临床实用性,该临界点是基于识别高风险类别中前10%的患者:在3110名患者(中位年龄为52.6岁±8.9岁)中,有3034名患者在5年的随访期内没有罹患癌症,76名患者罹患乳腺癌。在整个数据集中,Mirai 的 C 指数为 0.63(95% CI:0.6-0.7)。与IMS亚组(0.55 [95% CI: 0.4-0.7])相比,Hologic亚组(0.63 [95% CI: 0.5-0.7])的Mirai平均C指数更高。研究显示,如果米莱指数得分大于 0.029(10% 临界值),就能识别高风险人群,与低风险人群相比,高风险人群罹患乳腺癌的风险几乎是低风险人群的三倍:结论:Mirai 在预测墨西哥女性未来乳腺癌方面表现一般:前瞻性工作应完善并应用 Mirai 模型,尤其是针对少数民族人群和年龄在 30-40 岁之间的女性,因为她们目前还不是常规筛查的目标人群:关键点:人工智能模型对非白人、少数民族人群的适用性研究仍然不足。Mirai 模型与墨西哥妇女未来的癌症事件有关。需要进一步研究以提高模型性能并制定使用指南。
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来源期刊
Insights into Imaging
Insights into Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
7.30
自引率
4.30%
发文量
182
审稿时长
13 weeks
期刊介绍: Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere! I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe. Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy. A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field. I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly. The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members. The journal went open access in 2012, which means that all articles published since then are freely available online.
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