Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review

BACKGROUND: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demon...

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Detalles Bibliográficos
Autores principales: Gastounioti, Aimilia, Desai, Shyam, Ahluwalia, Vinayak S., Conant, Emily F., Kontos, Despina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8859891/
https://www.ncbi.nlm.nih.gov/pubmed/35184757
http://dx.doi.org/10.1186/s13058-022-01509-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8859891/
https://www.ncbi.nlm.nih.gov/pubmed/35184757
http://dx.doi.org/10.1186/s13058-022-01509-z

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