Review: Predictive approaches to breast cancer risk

Shuai Huang; Jun Tao Xu; Mei Yang

doi:10.1016/j.heliyon.2023.e21344

Review: Predictive approaches to breast cancer risk

Heliyon. 2023 Oct 31;9(11):e21344. doi: 10.1016/j.heliyon.2023.e21344. eCollection 2023 Nov.

Authors

Shuai Huang¹, Jun Tao Xu², Mei Yang¹

Affiliations

¹ Department of Breast Oncology, Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong, China.
² Joint Turing-Darwin Laboratory of Phil Rivers Technology Ltd. and Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China Department of Computational Biology, Phil Rivers Technology Ltd, Beijing, China West Institute of Computing Technology, Chinese Academy of Sciences, Chongqing, China.

Abstract

Despite the deployment of specific breast cancer screening strategies, breast cancer incidence rates have escalated significantly over recent decades. In a bid to reverse this trend, scientists have engaged in extensive epidemiological research into breast cancer prevalence, identifying numerous individual risk factors and promoting population-wide health education. Coupled with advances in genetic testing, risk prediction models based on breast cancer genes have been developed, albeit with inherent limitations. In the new millennium, the emergence of artificial intelligence (AI) as a dominant technological force suggests that breast cancer prediction models developed with AI may represent the next frontier in research.

Keywords: Artificial intelligence; BRCA1/2; Breast cancer; Polygenic risk scores; Risk factors.

Publication types

Review