Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy
The hybrid radiomics-based model demontrated the best performance of predicting 2-year OS and can differentiate the high-risk and low-risk patients.
Authors
- Ying Cao (ORCID: https://orcid.org/0000-0002-9346-2401)
- Yidong Wan (ORCID: https://orcid.org/0000-0003-0985-3299)
- Jianyang Wang (ORCID: https://orcid.org/0000-0001-9263-8914)
- Zefen Xiao (ORCID: https://orcid.org/0000-0003-0503-6814)
- Lijing Zuo
- Wenyang Liu (ORCID: https://orcid.org/0000-0002-6978-7169)
- Jima Lv
- Zongmei Zhou (ORCID: https://orcid.org/0000-0002-8336-2319)
- Dong Qu
- Qinfu Feng
- Lei Deng (ORCID: https://orcid.org/0000-0003-2647-4610)
- Tao Zhang (ORCID: https://orcid.org/0000-0001-5907-5312)
- Tianye Niu (ORCID: https://orcid.org/0000-0003-4181-3641)
- Nan Bi (ORCID: https://orcid.org/0000-0001-7201-2930)
- Xin Wang (ORCID: https://orcid.org/0000-0001-8051-1481)
- Wenqing Wang (ORCID: https://orcid.org/0000-0002-6710-4045)
- Nuo Yu (ORCID: https://orcid.org/0000-0001-8796-6112)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- National Cancer Center (US)
- Shenzhen Bay Laboratory (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-04-02
- DOI
- https://doi.org/10.1186/s12885-025-13996-2
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Beijing Xisike Clinical Oncology Research Foundation