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Machine Learning Model for Recurrent Lumbar Disc Herniation after Lumbar Discectomy.
Seung Yeop Kang1, Soo Jin Lee2, Suhyeon Kim2
1Ajou University School of Medicine, Suwon, Korea.
Journal of Korean Neurosurgical Society
|October 22, 2025
Summary
Recurrent lumbar disc herniation (RLDH) can be predicted by paraspinal muscle volume, specifically the quadratus lumborum. Machine learning models, like XGBoost, can identify these key features for better surgical decision-making.
Area of Science:
- Neurosurgery
- Orthopedics
- Radiology
Background:
- Recurrent lumbar disc herniation (RLDH) affects 5%-15% of patients post-discectomy.
- Established risk factors for RLDH include male gender, diabetes, smoking, and obesity.
- The role of paraspinal muscles in RLDH remains unclear.
Purpose of the Study:
- To identify key risk factors for RLDH using machine learning.
- To investigate the association between paraspinal muscle volume and RLDH.
- To develop a predictive model for RLDH.
Main Methods:
- Retrospective analysis of 126 patients undergoing lumbar discectomy.
- Inclusion of demographic, clinical, surgical, and MRI variables.
- Classification of patients into RLDH and non-RLDH groups based on follow-up MRI.
Main Results:
- Quadratus lumborum volume identified as a significant risk factor for RLDH (OR 7.894, P=0.001).
- XGBoost model achieved 0.794 accuracy and 0.811 AUC for RLDH prediction.
- Key predictive features included quadratus lumborum volume, psoas major, and vertebral weight.
Conclusions:
- Paraspinal muscle volume, particularly quadratus lumborum, is a crucial predictor of RLDH.
- Machine learning models can effectively predict RLDH.
- The developed prediction model can aid surgeons in surgical planning and follow-up strategies.
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