Review: Predictive approaches to breast cancer risk
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 ind...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023085523 |
| _version_ | 1797429917949886464 |
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| author | Shuai Huang Jun Tao Xu Mei Yang |
| author_facet | Shuai Huang Jun Tao Xu Mei Yang |
| author_sort | Shuai Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T09:19:49Z |
| format | Article |
| id | doaj.art-81e36cab6cf34bd495d76820a77d15b3 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-09T09:19:49Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-81e36cab6cf34bd495d76820a77d15b32023-12-02T07:01:56ZengElsevierHeliyon2405-84402023-11-01911e21344Review: Predictive approaches to breast cancer riskShuai Huang0Jun Tao Xu1Mei Yang2Department of Breast Oncology, Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong, ChinaJoint 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, ChinaDepartment of Breast Oncology, Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong, China; Corresponding author. Department of Breast Oncology, 4th Floor, 123 Huifu West Road, Yuexiu District, Guangzhou, Guangdong, China.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.http://www.sciencedirect.com/science/article/pii/S2405844023085523Breast cancerRisk factorsBRCA1/2Polygenic risk scoresArtificial intelligence |
| spellingShingle | Shuai Huang Jun Tao Xu Mei Yang Review: Predictive approaches to breast cancer risk Heliyon Breast cancer Risk factors BRCA1/2 Polygenic risk scores Artificial intelligence |
| title | Review: Predictive approaches to breast cancer risk |
| title_full | Review: Predictive approaches to breast cancer risk |
| title_fullStr | Review: Predictive approaches to breast cancer risk |
| title_full_unstemmed | Review: Predictive approaches to breast cancer risk |
| title_short | Review: Predictive approaches to breast cancer risk |
| title_sort | review predictive approaches to breast cancer risk |
| topic | Breast cancer Risk factors BRCA1/2 Polygenic risk scores Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023085523 |
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