Machine learning-based predictive model for high- grade cytokine release syndrome in chimeric antigen receptor T-cell therapy
Using machine learning, we identified key clinical biomarkers strongly associated with high-grade CRS. This tool efficiently predicts progression to high-grade CRS post-onset and shows significant potential for clinical deployment in CAR-T therapy.
Authors
- Zhuo Xiang (ORCID: https://orcid.org/0009-0002-5734-4704)
- Guilai Chen
- Kai Zhou
- Yulin Liu (ORCID: https://orcid.org/0000-0003-0273-0296)
- Qiang Wang (ORCID: https://orcid.org/0000-0003-2614-9201)
- Yin Li (ORCID: https://orcid.org/0000-0001-8257-2210)
- Qingqing Wang
- Jingwang Bi
- Xiaofeng Yu (ORCID: https://orcid.org/0000-0002-3029-4906)
- Jianxin Lv
- Tangnuran Halimulati
Institutions
- Army Medical University (CN)
- Second Hospital of Shandong University (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Southwest Hospital (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-11-20
- DOI
- https://doi.org/10.3389/fimmu.2025.1692892
- Primary Topic
- CAR-T cell therapy research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China