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- Vol. 5 Issue 3
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Breast Cancer Care: From Traditional Treatments to AI-Driven Risk Prediction and Future Prevention
Authors
1 2 3 4 Department of Information Technology, Stanley College of Engineering and Technology for Women, Hyderabad, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 846-849
- Article Type
- Original Article
References
1. World Health Organization, "Breast cancer," WHO Fact Sheet, 2025. https://www.who.int/news-room/fact-sheets/detail/breastcancer.
2. S. M. McKinney et al., "International evaluation of an AI system for breast cancer screening,"Nature, vol. 577, pp. 89-94, 2020.
doi:10.1038/s41586-019-1799-6.
3. A. Yala et al., "Toward robust mammography-based models for breast cancer risk,"Science Translational Medicine, vol. 13, no. 578,
eaba4373, 2021.doi:10.1126/scitranslmed.aba4373.
4. A. Yala et al., "Multi-institutional validation of a mammography-based breast cancer risk model," Journal of Clinical Oncology, vol.
40, no. 16, pp. 1732-1740, 2022.doi:10.1200/JCO.21.01337.
5. V. A. Arasu et al., "Comparison of mammography AI algorithms with a clinical risk model for 5-year breast cancer risk prediction,"
Radiology, vol. 307, no. 5, e222733, 2023. doi:10.1148/radiol.222733.
6. B. Fisher et al., "Twenty-year follow-up of a randomized trial comparing total mastectomy, lumpectomy, and lumpectomy plus
irradiation,"New England Journal of Medicine, vol. 347, pp. 1233-1241, 2002.doi:10.1056/NEJMoa022152.
7. Early Breast Cancer Trialists' Collaborative Group, "Effect of radiotherapy after breast-conserving surgery on 10-year recurrence
and 15-year breast cancer death," The Lancet, vol. 378, pp. 1707–1716, 2011. doi:10.1016/S0140-6736(11)61629-2.
8. C. M. Schopf et al., "Artificial intelligence-driven mammography-based future breast cancer risk prediction: A systematic review,"
Journal of the American College of Radiology, 2024. doi:10.1016/j.jacr.2023.10.018.
9. K. P. Lowry et al., "Current state of mammography-based artificial intelligence for future breast cancer risk prediction: A systematic
review," Journal of the National Cancer Institute, 2026. doi:10.1093/jnci/djag002.
10. N. Mavaddat et al., "Polygenic risk scores for prediction of breast cancer and breast cancer subtypes," American Journal of Human
Genetics, vol. 104, pp. 21–34,2019. doi:10.1016/j.ajhg.2018.11.002.
11. Z. Du et al., "Evaluating polygenic risk scores for breast cancer in women of African ancestry," Journal of the National Cancer
Institute, vol. 113, pp.1168–1176, 2021. doi:10.1093/jnci/djab050.
12. V. A. Arasu et al., “Comparative 10-year performance of mammography artificial intelligence, polygenic, and clinical breast cancer
risk models in the Kaiser Permanente Research Bank,” Journal of the National Cancer Institute, 2026. doi:10.1093/jnci/djag154.
13. National Cancer Institute, “Breast Cancer Treatment (PDQ®)–Health Professional Version.”
https://www.cancer.gov/types/breast/hp/breast-treatment-pdq.
14. J. A. Tice et al., “Validation of the Breast Cancer Surveillance Consortium model of breast cancer risk,” Breast Cancer Research and
Treatment, vol. 175, pp.519-523, 2019. doi:10.1007/s10549-019-05167-2
15. O. J. Omoleye et al., “External evaluation of a mammography-based deep learning model forpredicting breast cancer in an ethnically
diverse population,”Radiology: Artificial Intelligence, vol. 5, e220299, 2023. doi:10.1148/ryai.220299.
16. Polygenic Score Catalog, “PRS313_BC,” PGS000004, 2019.https://www.pgscatalog.org/score/PGS000004/.
17. J. Jeong et al., “The Emory Breast Imaging Dataset (EMBED),” Radiology: Artificial Intelligence, vol. 5, e220047, 2023.
doi:10.1148/ryai.220047.
18. H. T. Nguyen et al., “VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital
mammography,” Scientific Data, vol.10, 277, 2023. doi:10.1038/s41597-023-02100-7.
19. R. S. Lee et al., “A curated mammography data set for use in computer-aided detection and diagnosis research,” Scientific Data, vol.
4, 170177, 2017.doi:10.1038/sdata.2017.177.
20. National Cancer Institute, Genomic Data Commons, "TCGA-BRCA: Breast Invasive Carcinoma."
https://portal.gdc.cancer.gov/projects/TCGA-BRCA.
21. National Cancer Institute, "Breast Cancer Prevention." https://www.cancer.gov/types/breast/causes-risk-factors/prevention.
2. S. M. McKinney et al., "International evaluation of an AI system for breast cancer screening,"Nature, vol. 577, pp. 89-94, 2020.
doi:10.1038/s41586-019-1799-6.
3. A. Yala et al., "Toward robust mammography-based models for breast cancer risk,"Science Translational Medicine, vol. 13, no. 578,
eaba4373, 2021.doi:10.1126/scitranslmed.aba4373.
4. A. Yala et al., "Multi-institutional validation of a mammography-based breast cancer risk model," Journal of Clinical Oncology, vol.
40, no. 16, pp. 1732-1740, 2022.doi:10.1200/JCO.21.01337.
5. V. A. Arasu et al., "Comparison of mammography AI algorithms with a clinical risk model for 5-year breast cancer risk prediction,"
Radiology, vol. 307, no. 5, e222733, 2023. doi:10.1148/radiol.222733.
6. B. Fisher et al., "Twenty-year follow-up of a randomized trial comparing total mastectomy, lumpectomy, and lumpectomy plus
irradiation,"New England Journal of Medicine, vol. 347, pp. 1233-1241, 2002.doi:10.1056/NEJMoa022152.
7. Early Breast Cancer Trialists' Collaborative Group, "Effect of radiotherapy after breast-conserving surgery on 10-year recurrence
and 15-year breast cancer death," The Lancet, vol. 378, pp. 1707–1716, 2011. doi:10.1016/S0140-6736(11)61629-2.
8. C. M. Schopf et al., "Artificial intelligence-driven mammography-based future breast cancer risk prediction: A systematic review,"
Journal of the American College of Radiology, 2024. doi:10.1016/j.jacr.2023.10.018.
9. K. P. Lowry et al., "Current state of mammography-based artificial intelligence for future breast cancer risk prediction: A systematic
review," Journal of the National Cancer Institute, 2026. doi:10.1093/jnci/djag002.
10. N. Mavaddat et al., "Polygenic risk scores for prediction of breast cancer and breast cancer subtypes," American Journal of Human
Genetics, vol. 104, pp. 21–34,2019. doi:10.1016/j.ajhg.2018.11.002.
11. Z. Du et al., "Evaluating polygenic risk scores for breast cancer in women of African ancestry," Journal of the National Cancer
Institute, vol. 113, pp.1168–1176, 2021. doi:10.1093/jnci/djab050.
12. V. A. Arasu et al., “Comparative 10-year performance of mammography artificial intelligence, polygenic, and clinical breast cancer
risk models in the Kaiser Permanente Research Bank,” Journal of the National Cancer Institute, 2026. doi:10.1093/jnci/djag154.
13. National Cancer Institute, “Breast Cancer Treatment (PDQ®)–Health Professional Version.”
https://www.cancer.gov/types/breast/hp/breast-treatment-pdq.
14. J. A. Tice et al., “Validation of the Breast Cancer Surveillance Consortium model of breast cancer risk,” Breast Cancer Research and
Treatment, vol. 175, pp.519-523, 2019. doi:10.1007/s10549-019-05167-2
15. O. J. Omoleye et al., “External evaluation of a mammography-based deep learning model forpredicting breast cancer in an ethnically
diverse population,”Radiology: Artificial Intelligence, vol. 5, e220299, 2023. doi:10.1148/ryai.220299.
16. Polygenic Score Catalog, “PRS313_BC,” PGS000004, 2019.https://www.pgscatalog.org/score/PGS000004/.
17. J. Jeong et al., “The Emory Breast Imaging Dataset (EMBED),” Radiology: Artificial Intelligence, vol. 5, e220047, 2023.
doi:10.1148/ryai.220047.
18. H. T. Nguyen et al., “VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital
mammography,” Scientific Data, vol.10, 277, 2023. doi:10.1038/s41597-023-02100-7.
19. R. S. Lee et al., “A curated mammography data set for use in computer-aided detection and diagnosis research,” Scientific Data, vol.
4, 170177, 2017.doi:10.1038/sdata.2017.177.
20. National Cancer Institute, Genomic Data Commons, "TCGA-BRCA: Breast Invasive Carcinoma."
https://portal.gdc.cancer.gov/projects/TCGA-BRCA.
21. National Cancer Institute, "Breast Cancer Prevention." https://www.cancer.gov/types/breast/causes-risk-factors/prevention.
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