Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer
The combined nomograms based on pre-intervention ultrasound radiomics and clinicopathological features demonstrated exemplary performance in survival analysis. The new models may allow us to non-invasively classify TNBC patients with various disease outcome.
Authors
- Kang Li (ORCID: https://orcid.org/0000-0003-2365-7438)
- Yulan Peng
- Yiyue Li (ORCID: https://orcid.org/0000-0001-5435-1699)
- Heng Jiang (ORCID: https://orcid.org/0000-0001-8394-6077)
- Yixin Zhu (ORCID: https://orcid.org/0000-0002-8748-2991)
- Xiaorong Zhong (ORCID: https://orcid.org/0000-0003-1812-7213)
- Zekun Jiang (ORCID: https://orcid.org/0000-0002-3178-7761)
- Qiongxian Long
- Haina Zhao
- Lin Ma (ORCID: https://orcid.org/0000-0002-7926-4732)
- Jun Gao (ORCID: https://orcid.org/0000-0002-9681-787X)
- Yushuang He
- Wenwen
- Jingyan Liu
- Honghao Luo
- Dingbang Liu
Institutions
- North Sichuan Medical University (CN)
- Sichuan University (CN)
- Sichuan Agricultural University (CN)
- West China Hospital of Sichuan University (CN)
- Sichuan Cancer Hospital (CN)
- Peking University Shenzhen Hospital (CN)
- Nanchong Central Hospital (CN)
- University of Nebraska Medical Center (US)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1186/s12885-025-13635-w
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- Sichuan University
- Chengdu Science and Technology Bureau
- National Key Research and Development Program of China
- West China Hospital, Sichuan University