Quantification of cauda equina nerve root dispersion through radiomics features in weight-bearing MRI in normal subjects and spinal canal stenosis patients

• Published in: European radiology Vol. 34; no. 7; pp. 4585 - 4595
• Main Authors: Levi, Riccardo, Battaglia, Massimiliano, Garoli, Federico, Cappellini, Luca Alessandro, De Robertis, Mario, Anselmi, Leonardo, Savini, Giovanni, Riva, Marco, Fornari, Maurizio, Grimaldi, Marco, Politi, Letterio S.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2024; Springer Nature B.V
• Subjects: Adult; Aged; Artificial neural networks; Back pain; Canals (anatomy); Cauda Equina - diagnostic imaging; Correlation; Diagnostic Radiology; Entropy; Female; Humans; Image processing; Image segmentation; Imaging; Internal Medicine; Interventional Radiology; Lumbar Vertebrae - diagnostic imaging; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medicine; Medicine & Public Health; Middle Aged; Nerves; Neural networks; Neuro; Neuroradiology; Nonuniformity; Pain; Radiology; Radiomics; Retrospective Studies; Root distribution; Spatial distribution; Spinal cord; Spinal Nerve Roots - diagnostic imaging; Spinal stenosis; Spinal Stenosis - diagnostic imaging; Ultrasound; Vertebrae; Weight; Weight-Bearing; Cauda equina; Magnetic resonance; Weight-bearing; Spinal stenosis; Radiomics
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-10467-9

Objective To quantify the distribution of cauda equina nerve roots in supine and upright positions using manual measurements and radiomics features both in normal subjects and in lumbar spinal canal stenosis (LSCS) patients. Methods We retrospectively recruited patients who underwent weight-bearing MRI in supine and upright positions for back pain. 3D T2-weighted isotropic acquisition (3D-HYCE) sequences were used to develop a 3D convolutional neural network for identification and segmentation of lumbar vertebrae. Para-axial reformatted images perpendicular to the spinal canal and parallel to each vertebral endplate were automatically extracted. From each level, we computed the maximum antero-posterior (AP) and latero-lateral (LL) dispersion of nerve roots; further, radiomics features were extracted to quantify standardized metrics of nerve root distribution. Results We included 16 patients with LSCS and 20 normal subjects. In normal subjects, nerve root AP dispersion significantly increased from supine to upright position ( p  < 0.001, L2–L5 levels), and radiomics features showed an increase in non-uniformity. In LSCS subjects, in the upright position AP dispersion of nerve roots and entropy-related features increased caudally to the stenosis level ( p  < 0.001) and decreased cranially ( p  < 0.001). Moreover, entropy-related radiomics features negatively correlated with pre-operative Pain Numerical Rating Scale. Comparison between normal subjects and LSCS patients showed a difference in AP dispersion and increase of variance cranially to the stenosis level ( p  < 0.001) in the upright position. Conclusions Nerve root distribution inside the dural sac changed between supine and upright positions, and radiomics features were able to quantify the differences between normal and LSCS subjects. Clinical relevance statement The distribution of cauda equina nerve roots and the redundant nerve root sign significantly varies between supine and upright positions in normal subjects and spinal canal stenosis patients, respectively. Radiomics features quantify nerve root dispersion and correlates with pain severity. Key Points • Weight-bearing MRI depicts spatial distribution of the cauda equina in both supine and upright positions in normal subjects and spinal stenosis patients. • Radiomics features can quantify the effects of spinal stenosis on the dispersion of the cauda equina in the dural sac. • In the orthostatic position, dispersion of nerve roots is different in lumbar spinal stenosis patients compared to that in normal subjects; entropy-related features negatively correlated with pre-operative Pain Numerical Rating Scale.