MRI machine learning model predicts nerve root sedimentation in lumbar stenosis: a prospective study
Year: 2025
Type: article
Study design: Observational Study
Abstract: Objectives: To analyze MRI characteristics of the nerve root sedimentation sign (SedSign) in lumbar spinal canal stenosis (LSS) and to establish a risk model predicting its occurrence. Methods: A total of 1,138 narrow layers were divided into SedSign-positive (426 layers) and SedSign-negative (712 layers) groups. Key data included spinal canal diameters, dural sac dimensions, ligamentum flavum (LF) and epidural fat (EF) thickness, SedSign presence, lumbar disc herniation (LDH), high-intensity zone (HIZ), and EF classification. Comparisons used t tests or Mann-Whitney U tests. Recursive feature elimination with cross-validation (RFECV) was used to select predictive features, and models were established via random forest (RF), K-nearest neighbors (KNN), and extreme gradient boosting (XGBoost) algorithms and evaluated in terms of precision, recall, average F1 score, accuracy, and AUC. The optimal model was subject to SHAP analysis to explain the risk factors. Results: = 0.051). RFECV yielded eight features: age, sex, APDS, APDD, TDD, EF grade, LDH, and LFH. The RF model constructed using these features-designated as SedSign8-exhibited superior performance in predicting the risk of SedSign, with robust metrics across all evaluation dimensions: precision of 84.4%, recall of 73.6%, F1 score of 78.6%, accuracy of 83.6%, and an AUC of 0.901. Conclusion: Older patients, along with a greater degree of stenosis and changes in the dural sac and surrounding tissue structures, were identified as the main pathophysiological basis for the occurrence of the SedSign in LSS.
Source: Frontiers in Neurology
Authors: Qing Wang, Xianping Luo, Deng Li, Yi Zhai, Caiyun Ying
Language: English
Subfield: Pathology and Forensic Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4413255084