ArticleInsights into imaging2024
Validation of the Mirai model for predicting breast cancer risk in Mexican women.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current state of mammography-based artificial intelligence for future breast cancer risk prediction: a systematic review.Journal of the National Cancer Institute · 2026Pooled it
- Cross-Device Adaptation of Mirai for Mammography-Based Breast Cancer Risk Prediction.medRxiv : the preprint server for health sciences · 2026Article
- Prognostic integration of tumor microenvironment and parthanatos-related genes in gastric cancer: a machine learning-driven risk model and immune landscape profiling.Frontiers in immunology · 2026Article
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Authors and funding
12 authors.
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Abstract
objectivesTo 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.
methodsThis 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.
resultsOf 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.
conclusionsMirai 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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