Kefah Mokbel, Chair of Breast Cancer Surgery at London Breast Institute and Honorary Professor of Medicine at Cardiff University School of Medicine, shared a post on LinkedIn:
“The Future of Breast Cancer Screening is Personalised
A fascinating new study published in the Journal of the National Cancer Institute evaluated the 10-year performance of clinical, polygenic, and mammography AI risk models in 82,957 women.
Key findings:
- 2,471 breast cancers developed during 10 years of follow-up.
- Mammography AI (Mirai) outperformed traditional clinical and polygenic models alone:
AI: C-index 0.66
Clinical model (BCSCv3): 0.62
Polygenic risk score (PRS313): 0.61
- The highest predictive accuracy was achieved by combining all three approaches: Combined model C-index 0.70
This study elegantly demonstrates that integrating mammographic AI, genetic susceptibility, and clinical risk factors substantially improves breast cancer risk prediction.
Rather than offering the same screening strategy to every woman, these findings support a future where screening and prevention are tailored according to an individual’s biological and clinical risk profile.
Personalised screening has the potential to improve early detection, optimise resource allocation, and reduce overdiagnosis while identifying women who would benefit most from intensified surveillance and preventive interventions.”
Title: Comparative 10-year performance of mammography artificial intelligence, polygenic, and clinical breast cancer risk models in the Kaiser Permanente Research Bank
Authors: Vignesh Arasu, Tejomay Gadgil, Joseph Rothstein, Stacey Alexeeff, Ninah Achacoso, Arjun Bhattacharya, Jason Cord, Laura Esserman, Woodward Galbraith, Lawrence Gerstley, Susan Taylor Head, Nola Hylton, Lawrence Kushi, Catherine Lee, Amethyst Leimpeter, Donald Lewis, Vincent Liu, Ben Marafino, Laurie Margolies, Daniel Navarro, Albert Pu, Lori Sakoda, Jun Shan, Yiwey Shieh, Adriana Sistig, Cara Smith Gueye, Laura van’t Veer, Marvella Villaseñor, Mark Westley, Dorota Wisner, Jeffrey Tice, Li Shen, Laurel Habel, Weiva Sieh
Read the Full Article.
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