Risk Factors and an Interpretable Machine Learning Model for Predicting Spinal Epidural Lipomatosis
We developed and validated an interpretable prediction model that integrates clinical risk factors with an XGBoost algorithm and provides an actionable nomogram. This tool demonstrates strong potential to assist clinicians in early SEL detection and risk-stratified management, potentially enabling more targeted interventions for this underdiagnosed condition.
Authors
- Yuan Tian (ORCID: https://orcid.org/0000-0002-2296-1246)
- Wenbo Gu (ORCID: https://orcid.org/0000-0003-4175-1110)
- Hua Zhang (ORCID: https://orcid.org/0000-0001-5045-8739)
- Hui Ma (ORCID: https://orcid.org/0000-0001-5801-549X)
- Xi Zhu (ORCID: https://orcid.org/0000-0001-9631-9873)
- Xiao Zhang
- Haifeng Yuan
- Donghui Cao
- Yu Yang
- Xiaoyong Chen
- Hongyang Zhao
- Xusheng Li
Institutions
- Qinghai University Affiliated Hospital (CN)
- Yinchuan First People's Hospital (CN)
- Jinchuan (China) (CN)
- Ningxia Hui Autonomous Region Peoples Hospital (CN)
- Ningxia Medical University (CN)
- Ningxia Medical University General Hospital (CN)
- Jingning County People's Hospital (CN)
- The Fourth People's Hospital of Ningxia Hui Autonomous Region (CN)
Publication Details
- Journal
- Spine
- Published
- 2026-01-12
- DOI
- https://doi.org/10.1097/brs.0000000000005614
- Primary Topic
- Spinal Hematomas and Complications
- Type
- article
- Field-Weighted Citation Impact
- 0.00