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

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

BackgroundTraditional 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-cente...

Full description

Bibliographic Details
Main Authors: Jing Wang, Weiwei Gao, Min Lu, Xiaohua Yao, Debin Yang
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-11-01
Series:Frontiers in Oncology
Subjects:
breast cancer
Ki-67 levels
radiomics
peritumoral ultrasound segmentation
machine learning
support vector machine
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2023.1290313/full

Internet

https://www.frontiersin.org/articles/10.3389/fonc.2023.1290313/full

Similar Items