Objective:To investigate the performance of a predictive model based on fat suppression (FS)-T2WI sequence combined with machine learning in the differential diagnosis of brucellar spondylitis (BS) and tuberculous spondylitis (TS).Methods:The clinical and imaging data of 74 patients with BS and 81 patients with TS diagnosed clinically or pathologically in the First Affiliated Hospital of Xinjiang Medical University from January 2017 to January 2022 were retrospectively analyzed, and all patients underwent spinal magnetic resonance imaging (MRI) examination before treatment. Patients were randomly divided into a training group ( n = 123) and a testing group ( n = 32) in an 8 ∶ 2 allocation ratio, and radiomics feature extraction and dimensionality reduction analysis were performed on FS-T2WI sequence images. Four machine learning algorithms, including K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF) and logistic regression (LR), were used to construct a radiomics model, and receiver operating characteristic (ROC) curve was used to analyze the differential diagnostic performance of each model for BS and TS. Results:A total of 1 409 radiomics features were extracted, and 7 related features were screened and included for identification of BS and TS, among which the Maximum2DDiameterColumn feature value showed a strong correlation, and there was a statistically significant difference between BS and TS patients ( P < 0.001). In the testing group, the area under the ROC curve (AUC) value of the SVM model for identifying BS and TS was 0.886, with a sensitivity of 0.53, a specificity of 0.88, and a diagnostic accuracy of 0.81; in the training group, the AUC value of the SVM model for identifying BS and TS was 0.811, the sensitivity was 0.68, the specificity was 0.72, and the diagnostic accuracy of the model was 0.78. Conclusion:The prediction model based on FS-T2WI sequence combined with machine learning can be used to identify BS and TS, and the diagnostic performance of SVM model is prominent and stable.

Objective:To study the magnetic resonance imaging (MRI) and clinical manifestations of intraspinal echinococcosis.Methods:The general conditions, MRI and clinical manifestations of 23 patients with intraspinal echinococcosis diagnosed by pathology at the First Affiliated Hospital of Xinjiang Medical University from September 2011 to May 2023 were retrospectively analyzed.Results:There were 10 males and 13 females of the 23 patients with intraspinal echinococcosis. The age of the patients was (44.1 ± 13.9) years old, with a median age of 41 years old and a range of 25 to 72 years old. Eleven patients (47.8%) had a history of echinococcosis in the spine or other parts of the body. Among the 23 patients with intraspinal echinococcosis, 12 cases (52.2%) involved thoracic segment, 6 cases (26.1%) involved lumbar segment, 1 case (4.3%) involved sacral segment, 1 case (4.3%) involved thoracolumbar segment, 2 cases (8.7%) involved lumbosacral segment, and 1 case (4.3%) involved cervical and lumbar segment. There were 2 cases (8.7%) involving the intramedullary, 9 cases (39.1%) involving the extramedullary subdural, and 12 cases (52.2%) involving the extramedullary epidural. At the same time, 18 cases (78.3%) involved adjacent vertebral bodies, accessories or surrounding soft tissues. Intramedullary cystic echinococcosis was characterized by multiple nodules at the lower end of the spinal cord and the cauda equina nerve on MRI, with equal or low signal on T1WI, slightly high signals on T2WI and short time of inversion recovery (STIR), accompanied by small vesicles with high signal on T2WI. Intramedullary alveolar echinococcosis was characterized by nodular T1WI signals, slightly lower signals on T2WI and STIR, and circular enhancement on enhanced scan. Extramedullary subdural echinococcosis was mostly manifested as oval small vesicles with low signal on T1WI and high signal on T2WI, with a grape string-like appearance, and the capsular wall with low signal on T2WI could be seen at the edge. Extramedullary epidural echinococcosis was manifested as slightly low signal on T1WI, high signals on T2WI and STIR, accompanied by single or multiple small vesicles with high signal on T2WI, and compression of the dural sac. The clinical manifestations were chest and back, lumbosacral pain in 21 cases (91.3%), and lower limb dysfunction in 6 cases (26.1%).Conclusions:Intraspinal echinococcosis is relatively rare compared with other sites. When MRI features are clear, typical clinical manifestations are present, or there is a history of echinococcosis in other sites, intraspinal echinococcosis should be considered.

Objective To observe the value of synthetic MRI(SynMRI)and diffusion tensor imaging(DTI)for evaluating grade and isocitrate dehydrogenase-1(IDH-1)status of adult gliomas.Methods Totally 115 patients with adult glioma were retrospectively enrolled and divided into adult low-grade glioma(aLGG)group(n=44)and adult high-grade glioma(aHGG)group(n=71)according to WHO classification.There were 30 cases of IDH-1 mutant type,2 cases of wild type and 12 cases of undetermined gliomas in aLGG group,whereas 26 cases of IDH-1 mutant type,24 cases of wild type and 21 cases undetermined gliomas in aHGG group,respectively.SynMRI and DTI parameters,including T1 value,T2 value,proton density(PD),apparent diffusion coefficient(ADC)and fractional anisotropy(FA)were compared between groups,and the efficacy of each parameter for distinguishing aLGG and aHGG,as well as IDH-1 mutant type and wild type was analyzed.Results Significant differences of T1 value,T2 value,ADC and FA were found between groups,also between IDH-1 mutant type and wild type gliomas within aHGG group(all P＜0.05).The area under the curve(AUC)of the above parameters for distinguishing aLGG and aHGG was 0.731,0.686,0.930 and 0.710,respectively,while for distinguishing IDH-1 mutant type and wild type in aHGG group was 0.775,0.729,0.817 and 0.705,respectively,among which ADC had the highest AUC(all P＜0.05).Conclusion SynMRI and DTI parameters such as T1 value,T2 value,ADC and FA were helpful for distinguishing aLGG and aHGG,IDH-1 mutant type and wild type,among which ADC had the highest efficacy.

Objective:To investigate the value of a deep learning model based on diffusion-weighted imaging (DWI) in quick identification of the TOAST etiology classification in patients with acute ischemic stroke (AIS).Methods:In this cross-sectional study, imaging and clinical data of 504 patients with AIS admitted to the First Affiliated Hospital of Xinjiang Medical University from March 2023 to February 2024 were retrospectively reviewed. Using the TOAST etiology classification, there were 252 large artery atherosclerosis type and 252 small-artery occlusion type. The 504 cases were divided into a training set ( n=302), a validation set ( n=101) and a test set ( n=101) using stratified randomization in the ratio of 6∶2∶2. All cases had DWI data. A DenseNet network framework was used to construct DenseNet models by optimizing the model configurations of different layers. Three DenseNet models with different layers (121, 169, 201) were constructed, named DenseNet169 model, DenseNet121 model, and DenseNet201 model. The data enhancement, Adam optimizer and cross-entropy loss function methods were used to improve the convergence speed and robustness of the model, and to balance the positive and negative sample imbalance problem. Independent sample t-test or χ2 was used to compare the clinical data of patients with large artery atherosclerosis type and small-artery occlusion type AIS. Receiver operating characteristic curves and area under the curve (AUC) were performed to evaluate the efficacy of each model in identification of patients with large artery atherosclerosis type and small-artery occlusion type AIS. Results:There were statistically significant differences in age, National Institutes of Health Stroke Scale score at admission, and stenosis or occlusion of large vessels between patients with large artery atherosclerosis type and small-artery occlusion (all P<0.05). In the test set, the AUC, sensitivity, accuracy, and F1 score values of the DenseNet201 model for discriminating patients with large artery atherosclerosis type AIS and small-artery occlusion type AIS (0.826, 0.902, 0.743, 0.780, respectively) were higher than those of DenseNet121 (0.801, 0.647, 0.723, 0.702, respectively) and DenseNet169 model (0.778, 0.882, 0.733, 0.769). Conclusions:The deep learning models based DWI constructed in this study can help with the TOAST etiology classification of AIS cases. DenseNet201 model shows the best and stable performance in the deep learning-based classification.