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MRI-Based Machine Learning and Radiomics Methods for Assessing Spinal Cord Function in Patients with Mild Cervical Spondylotic Myelopathy

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机构: [1]Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, 45 Changchun St, Xicheng District, Beijing 100053, China. [2]Department of Neurosurgery, China-Japan Friendship School of Clinical Medicine, Peking University, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing 100029, China. [3]Department of Neurosurgery, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing 100029, China.
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关键词: cervical spondylotic myelopathy PET-MRI radiomics machine learning

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(1) Background: Patients with mild cervical spondylotic myelopathy (CSM) who delay surgery risk progression. While PET evaluates spinal cord function, its cost and radiation limit its use. (2) Methods: In this prospective study, patients with mild cervical spondylosis underwent preoperative 18F-FDG PET-MRI. Narrowed spinal levels were classified based on whether SUVmax was decreased. Follow-up assessments were conducted. Two machine learning models using MRI T2-based radiomics were developed to identify stenotic levels and decreased SUVmax. (3) Results: Patients with normal SUVmax showed greater symptom improvement. The radiomics models performed well, with AUCs of 0.981/0.962 (training/testing) for stenosis detection and 0.830/0.812 for predicting SUVmax decline. The model outperformed clinicians in predicting SUVmax decline, improving the AUC by 10%. (4) Conclusion: Patients with preserved SUVmax have better outcomes. MRI-based radiomics shows potential for identifying stenosis and predicting spinal cord function changes for preoperative assessment, though larger studies are needed to validate its clinical utility.

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出版当年[2025]版:
大类 | 3 区 医学
小类 | 3 区 工程:生物医学
最新[2025]版:
大类 | 3 区 医学
小类 | 3 区 工程:生物医学
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第一作者机构: [1]Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, 45 Changchun St, Xicheng District, Beijing 100053, China.
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