Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis
Introduction Accurate breast cancer risk prediction is essential for early detection and personalized prevention strategies. While traditional models, such as Gail and Tyrer–Cuzick, are widely utilized, machine learning-based approaches may offer enhanced predictive performance. This systematic revi...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Annals of Medicine |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2025.2534522 |
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