新发传染病电子杂志2026，Vol.11Issue(2)：17-23,7.DOI:10.19871/j.cnki.xfcrbzz.2026.02.004
CT及MRI T2WI影像组学联合临床特征在布鲁氏菌性脊柱炎和化脓性脊柱炎鉴别诊断中的应用价值
Application value of CT and MRI T2WI radiomics combined with clinical features in differential diagnosis of brucellar spondylitis and pyogenic spondylitis
摘要
Abstract
Objective To evaluate the diagnostic value of Logistic regression(LR)and support vector machines(SVM)models,constructed based on radiomic features from CT and MRI T2-weighted imaging(T2WI)combined with clinical features in the differential diagnosis between brucella spondylitis(BS)and pyogenic spondylitis(PS).Method Patients diagnosed with BS or PS by etiology or pathology who underwent both CT and MRI T2WI examinations at Inner Mongolia Autonomous Region People's Hospital from January 2017 to August 2025 were retrospectively collected.Patients were randomly divided into a training set(n=70,37 BS cases,33 PS cases)and a test set(n=30,16 BS cases,14 PS cases)at a ratio of 7:3.Univariate and multivariate LR were used to screen clinical risk factors,and a clinical model was established accordingly.The involved vertebral bodies were delineated on CT and MRI T2WI images for radiomic features extraction.Key features were selected from the training set by statistical testing,least absolute shrinkage and selection operator regression,and cross-validation to calculate radiomic scores(radscore,RS),thus establishing a dual-modal radiomic model of CT-MRI.Clinical features were fused with RS,and combined clinical-radiomic models were established using LR and SVM,respectively.Receiver operating characteristic curves of each model were plotted,and the diagnostic efficacy was evaluated by indicators such as the area under the curve(AUC).The Delong test was used to compare the differences in AUC values among the models.Result bony destruction of spinal appendages and paraspinal abscess were key clinical features for differentiating BS from PS.The clinical feature model showed AUCs of 0.718(training set)and 0.612(test set).A total of 1409 radiomic features were extracted from CT and MRI T2WI images respectively;after feature selection,10 key CT features and 5 key MRI T2WI features were retained,and the radiomic model achieved AUCs of 0.897(training set)and 0.732(test set).For the combined clinical-radiomic models,the LR model had AUCs of 0.901(training set)and 0.781(test set),while the SVM model had AUCs of 0.902(training set)and 0.777(test set).Delong test indicated no statistically significant differences in diagnostic efficacy between the LR model and SVM model(P＞0.05).Conclusion LR and SVM models based on CT and MRI T2WI radiomics combined with clinical features show favorable differential diagnostic performance for BS and PS,and can provide a non-invasive reference for clinical differentiation.关键词
布鲁氏菌性脊柱炎/化脓性脊柱炎/影像组学/机器学习/联合模型Key words
Brucella spondylitis/Pyogenic spondylitis/Radiomics/Machine learning/Combined model分类
医药卫生引用本文复制引用
邢佳凯,刘宇,闫昕,李俊林,赵建华..CT及MRI T2WI影像组学联合临床特征在布鲁氏菌性脊柱炎和化脓性脊柱炎鉴别诊断中的应用价值[J].新发传染病电子杂志,2026,11(2):17-23,7.基金项目
1.内蒙古自治区自然科学基金联合项目(2025LHMS08027) （2025LHMS08027）
2.内蒙古医学科学院2025年公立医院科研联合基金科技项目(2025GLLH0066) （2025GLLH0066）
3.内蒙古医学科学院公立医院科研联合基金科技项目(2023GLLH0060) （2023GLLH0060）
