Radiomics-based machine learning model to phenotype hip involvement in ankylosing spondylitis: a pilot study
We confirmed for the first time, the clinical actionability of profiling hip involvement in AS by radiomics method. Four distinct phenotypes of hip involvement in AS were identified and importantly, the high-risk phenotypes could predict structural damage of hip involvement in AS.
Authors
- Xin Lou (ORCID: https://orcid.org/0000-0003-2980-5171)
- Jian Zhu (ORCID: https://orcid.org/0000-0002-6244-9917)
- Xiaojian Ji (ORCID: https://orcid.org/0000-0003-4753-191X)
- Zhengyuan Hu (ORCID: https://orcid.org/0000-0001-5854-122X)
- Feng Huang (ORCID: https://orcid.org/0000-0002-3269-7530)
- Xingkang Liu (ORCID: https://orcid.org/0000-0002-9235-5778)
- Yan Wang (ORCID: https://orcid.org/0000-0002-8302-3704)
- Kunpeng Li (ORCID: https://orcid.org/0000-0003-3822-6198)
- Bo Xu (ORCID: https://orcid.org/0000-0002-1920-5829)
- Jianglin Zhang (ORCID: https://orcid.org/0000-0003-1097-6288)
- Jie Zhang (ORCID: https://orcid.org/0000-0003-2140-3231)
- Yan Li (ORCID: https://orcid.org/0000-0002-5587-2784)
Institutions
- Chinese PLA General Hospital (CN)
- Zhengzhou University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2024-08-29
- DOI
- https://doi.org/10.3389/fimmu.2024.1413560
- Citations
- 6
- Primary Topic
- Spondyloarthritis Studies and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 2.82
Funders
- National Natural Science Foundation of China
- General Hospital of People’s Liberation Army