Review: Predictive approaches to breast cancer risk

被引:0
|
作者
Huang, Shuai [1 ]
Xu, Jun Tao [2 ,3 ,4 ,5 ]
Yang, Mei [1 ,6 ]
机构
[1] Southern Med Univ, Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Breast Oncol, Guangzhou, Guangdong, Peoples R China
[2] Chinese Acad Sci, Joint Turing Darwin Lab Phil Rivers Technol Ltd, Beijing, Peoples R China
[3] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
[4] Phil Rivers Technol Ltd, Dept Computat Biol, Beijing, Peoples R China
[5] Chinese Acad Sci, China West Inst Comp Technol, Chongqing, Peoples R China
[6] Dept Breast Oncol, 4th Floor, 123 Huifu West Rd, Guangzhou, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Breast cancer; Risk factors; BRCA1/2; Polygenic risk scores; Artificial intelligence; COLLABORATIVE REANALYSIS; FAMILIAL BREAST; INDIVIDUAL DATA; OVARIAN-CANCER; WOMEN; ASSOCIATION; VALIDATION; DENSITY; BRCA1; MODEL;
D O I
10.1016/j.heliyon.2023.e21344
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:9
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