Dataset Information

Diffusion Basis Spectrum Imaging Identifies Clinically Relevant Disease Phenotypes of Cervical Spondylotic Myelopathy.

ABSTRACT:

Study design

Prospective cohort study.

Objective

Apply a machine learning clustering algorithm to baseline imaging data to identify clinically relevant cervical spondylotic myelopathy (CSM) patient phenotypes.

Summary of background data

A major shortcoming in improving care for CSM patients is the lack of robust quantitative imaging tools to guide surgical decision-making. Advanced diffusion-weighted magnetic resonance imaging (MRI) techniques, such as diffusion basis spectrum imaging (DBSI), may help address this limitation by providing detailed evaluations of white matter injury in CSM.

Methods

Fifty CSM patients underwent comprehensive clinical assessments and diffusion-weighted MRI, followed by DBSI modeling. DBSI metrics included fractional anisotropy, axial and radial diffusivity, fiber fraction, extra-axonal fraction, restricted fraction, and nonrestricted fraction. Neurofunctional status was assessed by the modified Japanese Orthopedic Association, myelopathic disability index, and disabilities of the arm, shoulder, and hand. Quality-of-life was measured by the 36-Item Short Form Survey physical component summary and mental component summary. The neck disability index was used to measure self-reported neck pain. K-means clustering was applied to baseline DBSI measures to identify 3 clinically relevant CSM disease phenotypes. Baseline demographic, clinical, radiographic, and patient-reported outcome measures were compared among clusters using one-way analysis of variance (ANOVA).

Results

Twenty-three (55%) mild, 9 (21%) moderate, and 10 (24%) severe myelopathy patients were enrolled. Eight patients were excluded due to MRI data of insufficient quality. Of the remaining 42 patients, 3 groups were generated by k-means clustering. When compared with clusters 1 and 2, cluster 3 performed significantly worse on the modified Japanese Orthopedic Association and all patient-reported outcome measures (P<0.001), except the 36-Item Short Form Survey mental component summary (P>0.05). Cluster 3 also possessed the highest proportion of non-Caucasian patients (43%, P=0.04), the worst hand dynamometer measurements (P<0.05), and significantly higher intra-axonal axial diffusivity and extra-axonal fraction values (P<0.001).

Conclusions

Using baseline imaging data, we delineated a clinically meaningful CSM disease phenotype, characterized by worse neurofunctional status, quality-of-life, and pain, and more severe imaging markers of vasogenic edema.

Level of evidence

II.

SUBMITTER: Zhang JK

PROVIDER: S-EPMC10042585 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Diffusion Basis Spectrum Imaging Identifies Clinically Relevant Disease Phenotypes of Cervical Spondylotic Myelopathy.

Zhang Justin K JK Javeed Saad S Greenberg Jacob K JK Dibble Christopher F CF Song Sheng-Kwei SK Ray Wilson Z WZ

Clinical spine surgery 20230228 3

<h4>Study design</h4>Prospective cohort study.<h4>Objective</h4>Apply a machine learning clustering algorithm to baseline imaging data to identify clinically relevant cervical spondylotic myelopathy (CSM) patient phenotypes.<h4>Summary of background data</h4>A major shortcoming in improving care for CSM patients is the lack of robust quantitative imaging tools to guide surgical decision-making. Advanced diffusion-weighted magnetic resonance imaging (MRI) techniques, such as diffusion basis spect ...[more]

PMID: 36959182

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Project description:Study designProspective cohort study.ObjectiveThe aim was to assess the association between diffusion tensor imaging (DTI) and diffusion basis spectrum imaging (DBSI) measures and cervical spondylotic myelopathy (CSM) clinical assessments at baseline and two-year follow-up.Summary of background dataDespite advancements in diffusion-weighted imaging, few studies have examined associations between diffusion magnetic resonance imaging (MRI) markers and CSM-specific clinical domains at baseline and long-term follow-up.Materials and methodsA single-center prospective cohort study enrolled 50 CSM patients who underwent surgical decompression and 20 controls from 2018 to 2020. At initial evaluation, all patients underwent diffusion-weighted MRI acquisition, followed by DTI and DBSI analyses. Diffusion-weighted MRI metrics assessed white matter integrity by fractional anisotropy, axial diffusivity, radial diffusivity, and fiber fraction. To improve estimations of intra-axonal anisotropic diffusion, DBSI measures intra-/extra-axonal fraction and intra-axonal axial diffusivity. DBSI also evaluates extra-axonal isotropic diffusion by restricted and nonrestricted fraction. Clinical assessments were performed at baseline and two-year follow-up and included the modified Japanese Orthopedic Association (mJOA); 36-Item Short Form Survey physical component summary (SF-36 PCS); SF-36 mental component summary; neck disability index; myelopathy disability index; and disability of the arm, shoulder, and hand. Pearson correlation coefficients were computed to compare associations between DTI/DBSI and clinical measures. A False Discovery Rate correction was applied for multiple comparisons testing.ResultsAt baseline presentation, of 36 correlations analyzed between DTI metrics and CSM clinical measures, only DTI fractional anisotropy showed a positive correlation with SF-36 PCS ( r =0.36, P =0.02). In comparison, there were 30/81 (37%) significant correlations among DBSI and clinical measures. Increased DBSI axial diffusivity, intra-axonal axial diffusivity, intra-axonal fraction, restricted fraction, and extra-axonal anisotropic fraction were associated with worse clinical presentation (decreased mJOA; SF-36 PCS/mental component summary; and increased neck disability index; myelopathy disability index; disability of the arm, shoulder, and hand). At latest follow-up, increased preoperative DBSI intra-axonal axial diffusivity and extra-axonal anisotropic fraction were significantly correlated with improved mJOA.ConclusionsThis findings demonstrate that DBSI measures may reflect baseline disease burden and long-term prognosis of CSM as compared with DTI. With further validation, DBSI may serve as a noninvasive biomarker following decompressive surgery.Level of evidence3.

Project description:MethodsTwenty patients with CSM and 17 controls were recruited. Clinical scores of modified Japanese Orthopedic Association (mJOA) and Nurick were collected. MRI based compression grades such as cord distortion were assessed. Hand dysfunction was tested using a custom motorized apparatus. Subject's forearm was placed in a cast and positioned such that their metacarpophalangeal (MCP) joint was vertically aligned with the motor shaft. Surface electromyographic sensors were placed on flexor digitorum superficialis (FDS) and extensor digitorum communis muscles. Hyperreflexia was measured as the FDS muscle activation during reflex when the MCP joint was moved from flexion to extension at 300°/sec. Proprioception was quantified as the angle of detection in absence of visual or auditory cues (subjects were blindfolded and given noise-cancelling headphones). Strength was measured as the maximum isometric force at the MCP joint. 2-sample t-test (p<0.05) were performed to assess significant differences in reflexes, proprioception and strength among patients and controls (SPSS software version 24).ResultsPatients reported higher Nurick (1.90±1.0 vs 0±0, p<0.001) and lower mJOA scores (14.3±1.9 vs 18.0±0, p<0.001) as compared to controls. Similarly, patients with CSM had increased reflexes (peak FDS EMG) (0.073±0.096 vs. 0.014±0.010, p = 0.019). Patient proprioception was significantly worse; mean angle of detection was greater than twice as high in patients (9.6± 9.43°) compared to controls (4.0± 2.3°), p = 0.022. MRI based compression ratio (CR) was a significant predictor of hyperreflexia, CR<0.44 resulted in steep increase in reflex activity. Fifteen of the 20 patients who completed follow up testing at 6 months after surgery show substantial clinical improvement in reflexes and proprioceptive angle, while repeated testing in controls were unchanged.ConclusionIn conclusion, hyperreflexia and decline in proprioception are the main drivers of hand disability in patients with CSM. Of multiple scales, only a select few MRI scales (such as compression ratio) were predictive of increased reflexes. The study describes a pre-clinical testing apparatus to quantitatively and objectively assess primary presenting symptoms in CSM. This pilot apparatus has the potential to evaluate treatment efficacy through repeated testing. Objective testing of hand dysfunction can help inform the design of clinically feasible devices, guide MRI biomarker analysis, and improve our understanding of the progression of neurological injury in this patient population.

Project description:ObjectiveDiffusion basis spectrum imaging (DBSI) has shown promise in evaluating cervical spinal cord structural changes in patients with cervical spondylotic myelopathy (CSM). DBSI may also be valuable in the postoperative setting by serially tracking spinal cord microstructural changes following decompressive cervical spine surgery. Currently, there is a paucity of studies investigating this topic, likely because of challenges in resolving signal distortions from spinal instrumentation. Therefore, the objective of this study was to assess the feasibility of DBSI metrics extracted from the C3 spinal level to evaluate CSM patients postoperatively.MethodsFifty CSM patients and 20 healthy controls were enrolled in a single-center prospective study between 2018 and 2020. All patients and healthy controls underwent preoperative and postoperative diffusion-weighted MRI (dMRI) at a 2-year follow-up. All CSM patients underwent decompressive cervical surgery. The modified Japanese Orthopaedic Association (mJOA) score was used to categorize CSM patients as having mild, moderate, or severe myelopathy. DBSI metrics were extracted from the C3 spinal cord level to minimize image artifact and reduce partial volume effects. DBSI anisotropic tensors evaluated white matter tracts through fractional anisotropy, axial diffusivity, radial diffusivity, and fiber fraction. DBSI isotropic tensors assessed extra-axonal pathology through restricted and nonrestricted fractions.ResultsOf the 50 CSM patients, both baseline and postoperative dMR images with sufficient quality for analysis were obtained in 27 patients. These included 15 patients with mild CSM (mJOA scores 15-17), 7 with moderate CSM (scores 12-14), and 5 with severe CSM (scores 0-11), who were followed up for a mean of 23.5 (SD 4.1, range 11-31) months. All preoperative C3-level DBSI measures were significantly different between CSM patients and healthy controls (p < 0.05), except DBSI fractional anisotropy (p = 0.31). At the 2-year follow-up, the same significance pattern was found between CSM patients and healthy controls, except DBSI radial diffusivity was no longer statistically significant (p = 0.75). When assessing change (i.e., postoperative - preoperative values) in C3-level DBSI measures, CSM patients exhibited significant decreases in DBSI radial diffusivity (p = 0.02), suggesting improvement in myelin integrity (i.e., remyelination) at the 2-year follow-up. Among healthy controls, there was no significant difference in DBSI metrics over time.ConclusionsDBSI metrics derived from dMRI at the C3 spinal level can be used to provide meaningful insights into representations of the spinal cord microstructure of CSM patients at baseline and 2-year follow-up. DBSI may have the potential to characterize white matter tract recovery and inform outcomes following decompressive cervical surgery for CSM.

Dataset Information

Diffusion Basis Spectrum Imaging Identifies Clinically Relevant Disease Phenotypes of Cervical Spondylotic Myelopathy.

Study design

Objective

Summary of background data

Methods

Results

Conclusions

Level of evidence

Publications

Diffusion Basis Spectrum Imaging Identifies Clinically Relevant Disease Phenotypes of Cervical Spondylotic Myelopathy.

Similar Datasets

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