Nomogram model based on enhanced MRI radiomics,deep learning and clinical features for differentiating spinal tuberculosis and pyogenic spondylitis
10.13929/j.issn.1003-3289.2025.01.026
- VernacularTitle:基于增强MRI影像组学、深度学习及临床特征构建列线图模型鉴别脊柱结核与化脓性脊柱炎
- Author:
Xirui LI
1
;
Dezhi WANG
;
Xiaonan YANG
;
Jie LI
;
Dapeng HAO
;
Jiufa CUI
Author Information
1. 青岛大学附属医院放射科,山东青岛 266003
- Publication Type:Journal Article
- Keywords:
tuberculosis,spinal;
spondylitis;
magnetic resonance imaging;
deep learning;
radiomics
- From:
Chinese Journal of Medical Imaging Technology
2025;41(1):122-127
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the efficacy of nomogram model based on enhanced MRI radiomics,deep learning(DL)and clinical features for differentiating spinal tuberculosis and pyogenic spondylitis.Methods Totally 59 cases of spinal tuberculosis and 66 of pyogenic spondylitis were retrospectively enrolled.Radiomics,DL and clinical features relevant to differentiating spinal tuberculosis and pyogenic spondylitis were selected.Then a predictive model was constructed using logistic regression based on the selected optimal features,and a comprehensive nomogram model was developed through combination of the above features.The effectiveness of these models for distinguishing spinal tuberculosis from pyogenic spondylitis were visualized based on receiver operating characteristic curves,calidration curves and decision curves.Results The nomogram model demonstrated the highest area under the curve(AUC)in both training set and test set,with AUC of 0.997 and 0.920,respectively.In test set,DeLong test indicated that the difference of AUC between the nomogram model and clinical model was significant(P=0.002),while no significant difference was observed between the nomogram model and the other models(all P＞0.05).The nomogram model provided the highest overall net benefit and exhibited good calibration for distinguishing spinal tuberculosis from pyogenic spondylitis.Conclusion Nomogram model based on enhanced MRI radiomics,DL and clinical features demonstrated high efficacy for differentiating spinal tuberculosis from pyogenic spondylitis.
