Utilizing multimodal radiomics technology from cervical MRI for diagnosis of cervical spinal cord injury and spinal cord concussion

The primary aim of this study is to assess the viability of employing multimodal radiomics techniques for distinguishing between cervical spinal cord injury and spinal cord concussion in cervical magnetic resonance imaging. This is a multicenter study involving 288 patients from a major medical center as the training group, and 75 patients from two other medical centers as the testing group. Data regarding the presence of spinal cord injury symptoms and their recovery status within 72 h were documented. These patients underwent sagittal T1-weighted and T2-weighted imaging using cervical magnetic resonance imaging. Radiomics techniques are used to help diagnose whether these patients have cervical spinal cord injury or spinal cord concussion. 1197 radiomics features were extracted for each modality of each patient. The accuracy of T1 modal in testing group is 0.773, AUC is 0.799. The accuracy of T2 modal in testing group is 0.707, AUC is 0.813. The accuracy of T1 + T2 modal in testing group is 0.800, AUC is 0.840. Our research indicates that multimodal radiomics techniques utilizing cervical magnetic resonance imaging can effectively diagnose the presence of cervical spinal cord injury or spinal cord concussion.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2024-08-12
DOI
https://doi.org/10.1038/s41598-024-69784-8
Citations
5
Primary Topic
Spinal Fractures and Fixation Techniques
Type
article
Field-Weighted Citation Impact
2.86

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Utilizing multimodal radiomics technology from cervical MRI for diagnosis of cervical spinal cord injury and spinal cord concussion

Zhigang Pan, Weipeng Hu, Zekai Chen, Yuanzhe Li et al.
5 citations
Scientific Reports
Spinal Fractures and Fixation Techniques
2.86
article

Utilizing multimodal radiomics technology from cervical MRI for diagnosis of cervical spinal cord injury and spinal cord concussion

Zhigang Pan, Weipeng Hu, Zekai Chen, Yuanzhe Li, Kaibin Fang, Canfang Shen, Zhangsheng Dai, Zhongning Fang
article en
5 citations

Abstract

The primary aim of this study is to assess the viability of employing multimodal radiomics techniques for distinguishing between cervical spinal cord injury and spinal cord concussion in cervical magnetic resonance imaging. This is a multicenter study involving 288 patients from a major medical center as the training group, and 75 patients from two other medical centers as the testing group. Data regarding the presence of spinal cord injury symptoms and their recovery status within 72 h were documented. These patients underwent sagittal T1-weighted and T2-weighted imaging using cervical magnetic resonance imaging. Radiomics techniques are used to help diagnose whether these patients have cervical spinal cord injury or spinal cord concussion. 1197 radiomics features were extracted for each modality of each patient. The accuracy of T1 modal in testing group is 0.773, AUC is 0.799. The accuracy of T2 modal in testing group is 0.707, AUC is 0.813. The accuracy of T1 + T2 modal in testing group is 0.800, AUC is 0.840. Our research indicates that multimodal radiomics techniques utilizing cervical magnetic resonance imaging can effectively diagnose the presence of cervical spinal cord injury or spinal cord concussion.

Scientific ReportsVol. 14(1)
Fujian Medical University (CN), Second Affiliated Hospital of Fujian Medical University (CN)
Fujian Medical University
Good health and well-being
Openalex Percentile: Top 9%
Spinal Fractures and Fixation Techniques
2.86
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.