Systematic review of radiological cervical foraminal grading systems

Meacock, James; Schramm, Moritz; Selvanathan, Senthil; Currie, Stuart; Stocken, Deborah; Jayne, David; Thomson, Simon

doi:10.1007/s00234-020-02596-5

Cited by 16 publications

(11 citation statements)

References 61 publications

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“…Cervical spine X-ray imaging data from various positions, including anterior-posterior, lateral, left oblique, and right oblique, were consolidated. Identifying image features from different positions is crucial for model training, as each image feature has a primary observation position [ 24 ]. Initially, CS X-ray projection positions are identified to ensure the deep learning model learns the most important CS image features from these specific positions, aiding in precise feature localization and thus enhancing CS diagnosis accuracy.…”

Section: Discussionmentioning

confidence: 99%

Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images

Xie,

Nie,

Lundgren

et al. 2024

Sensors

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The increase in Cervical Spondylosis cases and the expansion of the affected demographic to younger patients have escalated the demand for X-ray screening. Challenges include variability in imaging technology, differences in equipment specifications, and the diverse experience levels of clinicians, which collectively hinder diagnostic accuracy. In response, a deep learning approach utilizing a ResNet-34 convolutional neural network has been developed. This model, trained on a comprehensive dataset of 1235 cervical spine X-ray images representing a wide range of projection angles, aims to mitigate these issues by providing a robust tool for diagnosis. Validation of the model was performed on an independent set of 136 X-ray images, also varied in projection angles, to ensure its efficacy across diverse clinical scenarios. The model achieved a classification accuracy of 89.7%, significantly outperforming the traditional manual diagnostic approach, which has an accuracy of 68.3%. This advancement demonstrates the viability of deep learning models to not only complement but enhance the diagnostic capabilities of clinicians in identifying Cervical Spondylosis, offering a promising avenue for improving diagnostic accuracy and efficiency in clinical settings.

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Section: Discussionmentioning

confidence: 99%

Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images

Xie,

Nie,

Lundgren

et al. 2024

Sensors

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“…Foramens at each level were classified as either having foraminal stenosis or having uncinate process degeneration. Foraminal stenosis was evaluated using oblique sagittal T2 MRI findings, according to the classification suggested by Kim et al [ 18 ], Park et al [ 19 ], and Meacock et al [ 20 ]. Foramens with >50% perineural fat obliteration around the nerve root circumferences were considered to have foraminal stenosis [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

Preoperative Radiographic Simulation for Partial Uncinate Process Resection during Anterior Cervical Discectomy and Fusion to Achieve Adequate Foraminal Decompression and Prevention of Vertebral Artery Injury

Yang,

Kim,

Lee

et al. 2023

Asian Spine J

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Study Design: Retrospective radiographic study.Purpose: This study aims to demonstrate the proper resection trajectory of a partial posterior uncinate process resection combined with anterior cervical discectomy and fusion (ACDF) and evaluate whether foraminal stenosis or uncinate process degeneration increases the risk of vertebral artery (VA) injury.Overview of Literature: Appropriate resection trajectory that could result in sufficient decompression and avoid vertebral artery injury is yet unknown.Methods: We retrospectively reviewed patients who underwent cervical magnetic resonance imaging and computed tomography angiography for preoperative ACDF evaluation. The segments were classified according to the presence of foraminal stenosis. The height, thickness, anteroposterior length, horizontal distance from the uncinate process to the VA, and vertical distance from the uncinate process baseline to the VA of the uncinate process were measured. The distance between the uncinate anterior margin and the resection trajectory (UAM-to-RT) was measured.Results: There were no VA injuries or root injuries among the 101 patients who underwent ACDF (163 segments, mean age of 56.3±12.2). Uncinate anteroposterior length was considerably longer in foramens with foraminal stenosis, whereas uncinate process height, thickness, and distance between the uncinate process and VA were not significantly associated with foraminal stenosis. There were no significant differences in radiographic parameters based on uncinate degeneration. The UAM-to-RT distances for adequate decompression were 1.6±1.4 mm (range, 0–4.8 mm), 3.4±1.7 mm (range, 0–7.1 mm), 4.0±1.7 mm (range, 0–9.0 mm), and 4.5±1.2 mm (range, 2.5–7.5 mm) for C3–C4, C4–C5, C5–C6, and C6–C7, respectively.Conclusions: More than half of the uncinate process in the anteroposterior plane should be removed for adequate neural foramen decompression. Foraminal stenosis or uncinate degeneration did not alter the relative anatomy of the uncinate process and the VA and did not impact VA injury risk.

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“…Radiology reports of mild, moderate, or severe stenosis accompany typical MRI reports evaluating foraminal stenosis. While insurers use these to support an indication for surgical treatment, these reports are unreliable [ 10 ], as up to 10% of cases in symptomatic patients may be reported as normal [ 13 ]. Instead, the severity of stenosis was classified into one of three grades as described by Kim et al [ 9 ], a validated neuroforaminal stenosis grading system.…”

Section: Methodsmentioning

confidence: 99%

“…Magnetic resonance imaging (MRI) is the modality of choice before surgery for determining the degree of neural element compression and the site of nerve impingement [ 9 , 10 ]. Although the clinical utility of MRI is widely established, literature on its capacity to predict clinical outcomes based on the degree of stenosis is limited.…”

Section: Introductionmentioning

confidence: 99%

How Does the Severity of Neuroforaminal Compression in Cervical Radiculopathy Affect Outcomes of Anterior Cervical Discectomy and Fusion

Lambrechts,

Issa,

Lee

et al. 2023

Asian Spine J

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Study Design: This study is a retrospective cohort study.Purpose: This study aims to determine whether preoperative neuroforaminal stenosis (FS) severity is associated with motor function patient-reported outcome measures (PROMs) following anterior cervical discectomy and fusion (ACDF).Overview of Literature: Cervical FS can significantly contribute to patient symptoms. While magnetic resonance imaging (MRI) has been used to classify FS, there has been limited research into the impact of FS severity on patient outcomes.Methods: Patients undergoing primary, elective 1–3 level ACDF for radiculopathy at a single academic center between 2015 and 2021 were identified retrospectively. Cervical FS was evaluated using axial T2-weighted MRI images via a validated grading scale. The maximum degree of stenosis was used for multilevel disease. Motor symptoms were classified using encounters at their final preoperative and first postoperative visits, with examinations ≤3/5 indicating weakness. PROMs were obtained preoperatively and at 1-year follow-up. Bivariate analysis was used to compare outcomes based on stenosis severity, followed by multivariable analysis.Results: This study included 354 patients, 157 with moderate stenosis and 197 with severe stenosis. Overall, 58 patients (16.4%) presented with upper extremity weakness ≤3/5. A similar number of patients in both groups presented with baseline motor weakness (13.5% vs. 16.55, p =0.431). Postoperatively, 97.1% and 87.0% of patients with severe and moderate FS, respectively, experienced full motor recovery (p =0.134). At 1-year, patients with severe neuroforaminal stenosis presented with significantly worse 12-item Short Form Survey Physical Component Score (PCS-12) (33.3 vs. 37.3, p =0.049) but demonstrated a greater magnitude of improvement (Δ PCS-12: 5.43 vs. 0.87, p =0.048). Worse stenosis was independently associated with greater ΔPCS-12 at 1-year (β =5.59, p =0.022).Conclusions: Patients with severe FS presented with worse preoperative physical health. While ACDF improved outcomes and conferred similar motor recovery in all patients, those with severe FS reported much better improvement in physical function.

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Systematic review of radiological cervical foraminal grading systems

Cited by 16 publications

References 61 publications

Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images

Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images

Preoperative Radiographic Simulation for Partial Uncinate Process Resection during Anterior Cervical Discectomy and Fusion to Achieve Adequate Foraminal Decompression and Prevention of Vertebral Artery Injury

How Does the Severity of Neuroforaminal Compression in Cervical Radiculopathy Affect Outcomes of Anterior Cervical Discectomy and Fusion

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