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Risk Factors and an Interpretable Machine Learning Model for Predicting Spinal Epidural Lipomatosis: A Multicenter

Donghui Cao1,2, Xiaoyong Chen1,3, Xusheng Li1

  • 1Department of Spinal Orthopedics, General Hospital of Ningxia Medical University, Ningxia Hui Autonomous Region, Yinchuan City, China.

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|January 19, 2026
PubMed
Summary
This summary is machine-generated.

Researchers identified key risk factors for Spinal Epidural Lipomatosis (SEL), developing an interpretable machine learning model to aid early detection. This tool helps stratify risk for better patient management of this underdiagnosed condition.

Keywords:
XGBoostclinical decision supportlumbar spinal stenosismachine learningnomogrampredictive modelrisk factorsspinal epidural lipomatosis

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Area of Science:

  • Spinal health and neurosurgery
  • Medical imaging and diagnostics
  • Machine learning in healthcare

Background:

  • Spinal Epidural Lipomatosis (SEL) is a significant, underdiagnosed cause of lumbar spinal stenosis.
  • Current tools for early SEL identification and risk stratification are insufficient.
  • Developing predictive models is crucial for timely diagnosis and management.

Purpose of the Study:

  • To identify independent risk factors for Spinal Epidural Lipomatosis (SEL).
  • To develop and validate an interpretable machine learning-based predictive model for SEL.
  • To create a clinically accessible tool for risk stratification.

Main Methods:

  • Retrospective multicenter study involving 774 patients with low back and leg pain.
  • LASSO regression for variable selection and development of a predictive nomogram.
  • Four machine learning models were constructed and evaluated using AUC, calibration, and decision curve analysis.

Main Results:

  • Seven independent predictors for SEL were identified: elevated random blood glucose, blood type B, atherosclerosis index, body mass index, uric acid, obstructive sleep apnea, and age.
  • The XGBoost model showed superior predictive performance (AUC: 0.726) in the validation set.
  • Interpretability analysis highlighted glucose, age, and uric acid as key contributors to risk prediction.

Conclusions:

  • An interpretable prediction model integrating clinical factors and an XGBoost algorithm was developed and validated.
  • The model provides an actionable nomogram to assist clinicians in early SEL detection.
  • This tool supports risk-stratified management and targeted interventions for underdiagnosed SEL.