Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy
Peritumoral region radiomics features could predict the prognosis of proximal esophageal cancer. Dual-region radiomics features showed significantly better predictive performance. Radiomics features can provide insights into the lipid metabolism associated with radioresistance.
Authors
- Liting Qian (ORCID: https://orcid.org/0000-0001-9005-6005)
- Juan Bo (ORCID: https://orcid.org/0000-0003-0274-8154)
- Linrui Li (ORCID: https://orcid.org/0000-0002-6412-8455)
- Yuxin Zhang (ORCID: https://orcid.org/0000-0003-4698-5645)
- Zhihui Qin (ORCID: https://orcid.org/0000-0003-4145-5741)
- Jiangning Dong (ORCID: https://orcid.org/0000-0002-2954-076X)
- Jiaru Hu
Institutions
- University of Science and Technology of China (CN)
- Anhui Medical University (CN)
- Anhui Provincial Hospital (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1186/s13244-024-01853-y
- Citations
- 8
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.58
Funders
- Anhui Medical University