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. 2025 Jun 17;12(6):666.
doi: 10.3390/bioengineering12060666.

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

He Wang  1 , Kai Wang  1 , Yutian Wang  1 , Zhenlei Liu  1 , Lei Zhang  1 , Shanhang Jia  1 , Kun He  2   3 , Xiangyu Zhang  1 , Hao Wu  1
Affiliations

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

He Wang et al. Bioengineering (Basel). .

Abstract

(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.

Keywords: PET-MRI; cervical spondylotic myelopathy; machine learning; radiomics.

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Conflict of interest statement

The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Results of identifying compression levels: (a) AUC curve of 9 machine learning algorithms on the training dataset; (b) statistic comparison of 9 machine learning algorithms on the training dataset; (c) AUC curve of proposed machine learning algorithm and 3 screeners on the test dataset; (d) statistic comparison of proposed machine learning algorithm and 3 screeners on the test dataset; (e) SHAPLEY explanations of the proposed model.
Figure 2
Figure 2
Results of identifying decreased 18F-FDG uptake levels: (a) AUC curve of 9 machine learning algorithms on the training dataset; (b) statistic comparison of 9 machine learning algorithms on the training dataset; (c) AUC curve of proposed machine learning algorithm and 3 screeners on the test dataset; (d) statistic comparison of proposed machine learning algorithm and 3 screeners on the test dataset; (e) SHAPLEY explanations of the proposed model.
Figure 3
Figure 3
Prediction result of Example 1: (a) the PET/MR results of Example 1 are shown. Cervical spinal cord segments from C2 to C7 and five intervertebral disc segments were labeled. SUVmax was normal at the C3/4 level and SUVmax was decreased at the C5/6 level; (b) the LIME results of the proposed model are shown.
Figure 4
Figure 4
Prediction result of Example 2: (a) the PET/MR results of Example 2 are shown. Cervical spinal cord segments from C2 to C7 and five intervertebral disc segments were labeled. SUVmax decreased at the C5/6 level and SUVmax was normal at the C6/7 level; (b) the LIME results of the proposed model are shown. The proposed methods predicted that both SUVmax were normal at the two levels.
Figure 5
Figure 5
Proposed flowchart of the treatment of cervical spondylotic myelopathy.
See this image and copyright information in PMC

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