Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer

Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer

BackgroundThis study explores the clinical value of a machine learning (ML) model based on ultrasound radiomics features of primary foci, combined with clinicopathologic factors to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) for patients with breast cancer (BC)...

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Bibliographic Details
Main Authors: Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Oncology
Subjects:
breast cancer
NAC
ultrasound radiomics features
pCR
GBM
SHAP
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2024.1485681/full
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https://www.frontiersin.org/articles/10.3389/fonc.2024.1485681/full

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