Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer

Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer

Abstract Objective This study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopathological features in the survival analysis of triple-negative breast cancer (TNBC) patients. Methods and materials All patients, including retrosp...

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Main Authors: Wenwen, Zekun Jiang, Jingyan Liu, Dingbang Liu, Yiyue Li, Yushuang He, Haina Zhao, Lin Ma, Yixin Zhu, Qiongxian Long, Jun Gao, Honghao Luo, Heng Jiang, Kang Li, Xiaorong Zhong, Yulan Peng
Format: Article
Language:English
Published: BMC 2025-02-01
Series:BMC Cancer
Subjects:
Triple negative breast cancer
Ultrasound
Radiomics
Prognosis
Machine learning
Online Access:https://doi.org/10.1186/s12885-025-13635-w
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https://doi.org/10.1186/s12885-025-13635-w

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