Multifidus Fat Infiltration in Patients with Persistent Spinal Pain Syndrome Type II Treated with Spinal Cord Stimulation: A Preliminary Report

脊髓刺激治疗持续性脊柱疼痛综合征II型患者的多裂肌脂肪浸润:初步报告

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Abstract

Background/Objectives: Functional spinal instability from multifidus dysfunction has been proposed as a mechanism for chronic postsurgical pain. Prior studies reported structural impairments in the lumbar multifidus in patients with chronic low back pain, including a reduced cross-sectional area, muscle thickness, and increased fat infiltration. This preliminary report examined the prevalence of multifidus fat infiltration after Spinal Cord Stimulation (SCS), an established pain management technique. It also assessed inter-rater reliability in evaluating fat infiltration using MRI. Methods: The medical imaging data from four patients with Persistent Spinal Pain Syndrome Type II (PSPS II) treated with SCS were collected. Two independent operators performed the manual segmentation of the multifidus muscle on axial MRI images of the lumbar spine. The fat-to-muscle ratio was quantified and rated using a four-point classification system, categorizing multifidus fat infiltration as normal, mild, moderate, or severe. To assess the reliability of the manual segmentations, inter-rater reliability was determined. Results: The median fat-to-muscle ratio at the levels L2-L3 was 46.12 (Q1-Q3: 44.88-47.35). At the levels L3-L4, L4-L5, and L5-S1, the median values were 50.45 (Q1-Q3: 45.57-52.98), 52.11 (Q1-Q3: 48.81-52.80), and 52.84 (Q1-Q3: 49.09-56.39), respectively. An ICC value of one (95% CI from 0.999 to 1, p < 0.001) was found for inter-rater agreement on the muscle volume of the multifidus muscle. Conclusions: All the patients had moderate-to-severe fat infiltration of the multifidus muscle at each lumbar spinal level. Although time-consuming, the manual segmentation of the multifidus muscle in patients treated with SCS was feasible and yielded excellent inter-rater reliability when determining muscle volume. Future endeavors should focus on the automation of segmentation and classification.

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