Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features

BACKGROUND: Traditional immunohistochemistry assessment of Ki-67 in breast cancer (BC) via core needle biopsy is invasive, inaccurate, and nonrepeatable. While machine learning (ML) provides a promising alternative, its effectiveness depends on extensive data. Although the current mainstream MRI-cen...

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Detalles Bibliográficos
Autores principales: Wang, Jing, Gao, Weiwei, Lu, Min, Yao, Xiaohua, Yang, Debin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10691503/
https://www.ncbi.nlm.nih.gov/pubmed/38044998
http://dx.doi.org/10.3389/fonc.2023.1290313

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10691503/
https://www.ncbi.nlm.nih.gov/pubmed/38044998
http://dx.doi.org/10.3389/fonc.2023.1290313

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