Using high-repeatable radiomic features improves the cross-institutional generalization of prognostic model in esophageal squamous cell cancer receiving definitive chemoradiotherapy
The repeatability of RFs impacts the generalizability of the radiomic model. The high-repeatable RFs safeguard the cross-institutional generalizability of the model. Smaller bin width helps improve the repeatability of RFs.
Authors
- Jiang Zhang (ORCID: https://orcid.org/0000-0003-1451-729X)
- Qifeng Wang (ORCID: https://orcid.org/0000-0002-3036-6084)
- Xinzhi Teng (ORCID: https://orcid.org/0000-0001-7515-8302)
- Jing Yan Cai (ORCID: https://orcid.org/0000-0001-6934-0108)
- Jie Gong (ORCID: https://orcid.org/0000-0002-3923-2488)
- Hongfei Sun (ORCID: https://orcid.org/0000-0002-3995-7251)
- Jie Li (ORCID: https://orcid.org/0000-0002-2540-7494)
- Lina Zhao (ORCID: https://orcid.org/0000-0002-0011-6471)
- Zhi Yang (ORCID: https://orcid.org/0009-0003-1586-3001)
Institutions
- Hong Kong Polytechnic University (HK)
- University of Electronic Science and Technology of China (CN)
- Sichuan Cancer Hospital (CN)
- Xijing Hospital (CN)
- Air Force Medical University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1186/s13244-024-01816-3
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- National Natural Science Foundation of China