Objective:To evaluate the diagnostic value of MRI findings in differentiating between tophus and giant cell tumors of the tendon sheath (GCTTS) in the knee.Methods:The study was a case-control study. The clinical and MRI data of patients diagnosed with knee tophus or GCTTS was retrospectively analyzed at the Affiliated Hospital of Qingdao University from September 2018 to September 2024. The study included 23 cases of tophus and 22 cases of GCTTS. MRI sequences, including T 1WI, fat-suppressed T 2WI, and proton density weighted imaging, were evaluated. Parameters including lesion signal intensity and homogeneity, margin, maximum diameter, location (inside or outside the synovial cavity), ligament/tendon involvement, ligament/tendon parenchymal changes, adjacent bone erosion, bone marrow edema, synovial hyperplasia, joint effusion, and periarticular soft tissue swelling were recorded. Independent sample t-tests, χ2 tests, or Fisher exact tests were used to compare MRI findings between the two groups. Multivariate logistic regression was performed to identify independent predictive factors. Results:Significant differences in terms of maximum diameter, location (inside or outside the synovial cavity), ligament/tendon involvement, ligament/tendon parenchymal changes, adjacent bone erosion, bone marrow edema, and periarticular soft tissue swelling between the two groups were found (all P<0.05). No significant differences for other parameters were observed (all P>0.05). Lesion location and ligament/tendon parenchymal involvement demonstrated the highest sensitivity and specificity for distinguishing the two diseases. The sensitivity and specificity values for lesion location were 0.78 and 0.95. The sensitivity and specificity values for ligament/tendon involvement were 0.78 and 1.00. Multivariate logistic regression identified lesion location (inside or outside the synovial cavity) as an independent predictor for differentiating tophus from GCTTS ( OR=31.48, 95% CI 1.58-625.69, P=0.024). Conclusion:The location of the lesion (inside or outside the synovial cavity) and involvement of ligament/tendon parenchyma are critical factors in differentiating knee tophus from GCTTS. Additionally, lesion location serves as an independent predictor for distinguishing between the two conditions.

Objective:To explore the diagnostic value of routine magnetic resonance imaging (MRI) findings for early detection of dural arteriovenous fistulas (DAVF).Methods:A retrospective collection of 53 patients with DAVF confirmed by digital subtraction angiography (DSA) at the Affiliated Hospital of Qingdao University from September 2018 to June 2023 was conducted. All patients underwent routine non-enhanced and enhanced MRI scans before treatment, with 30 patients also receiving magnetic resonance angiography (MRA) examination. Medical records were reviewed, and basic patient information, clinical symptoms, and imaging markers [pial venous engorgement (PVE), cerebral hemorrhage, subarachnoid hemorrhage, vasogenic edema, venous infarction, medullary veins engorgement (MVE), parenchymal enhancement, encephalopathy mimics] were recorded. The Cognard grading was carried out in accordance with the manifestations shown by DSA.Results:Seventy-seven percent (41/53) of patients exhibited PVE on the brain surface, with 95% (39/41) of these cases showing localized PVE on one hemisphere or even within a single brain lobe. Approximately 64% (34/53) of these PVEs were detectable on conventional T 2-weighted imaging. Among the 30 patients who underwent MRA, only 50% (15/30) showed evidence of PVE on both T 2WI and MRA, while an additional 23% (7/30) had PVE only on MRA. About 38% (20/53) of patients presented with isolated vasogenic edema, with 70% (14/20) of these cases demonstrating PVE on T 2WI. Twenty-six percent (14/53) of patients had intracranial hemorrhage, and 11 of these patients demonstrated positive signs of PVE. Parenchymal enhancement was primarily observed in subarachnoid structures in 11% (6/53) of patients, with 5/6 showing PVE on the brain surface or spinal cord surface. Venous infarction (4%, 2/53) and MVE (4%, 2/53) were more commonly seen in high Cognard grade DAVF, whereas encephalopathy mimics (4%, 2/53) were frequently encountered in low Cognard grade DAVF. Conclusions:PVE on the brain surface is a direct sign for the diagnosis of DAVF on routine MRI, yet it is often subtle. Familiarity with common indirect signs is of great importance for the early diagnosis of DAVF.

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.

Objective:To explore the diagnostic value of routine magnetic resonance imaging (MRI) findings for early detection of dural arteriovenous fistulas (DAVF).Methods:A retrospective collection of 53 patients with DAVF confirmed by digital subtraction angiography (DSA) at the Affiliated Hospital of Qingdao University from September 2018 to June 2023 was conducted. All patients underwent routine non-enhanced and enhanced MRI scans before treatment, with 30 patients also receiving magnetic resonance angiography (MRA) examination. Medical records were reviewed, and basic patient information, clinical symptoms, and imaging markers [pial venous engorgement (PVE), cerebral hemorrhage, subarachnoid hemorrhage, vasogenic edema, venous infarction, medullary veins engorgement (MVE), parenchymal enhancement, encephalopathy mimics] were recorded. The Cognard grading was carried out in accordance with the manifestations shown by DSA.Results:Seventy-seven percent (41/53) of patients exhibited PVE on the brain surface, with 95% (39/41) of these cases showing localized PVE on one hemisphere or even within a single brain lobe. Approximately 64% (34/53) of these PVEs were detectable on conventional T 2-weighted imaging. Among the 30 patients who underwent MRA, only 50% (15/30) showed evidence of PVE on both T 2WI and MRA, while an additional 23% (7/30) had PVE only on MRA. About 38% (20/53) of patients presented with isolated vasogenic edema, with 70% (14/20) of these cases demonstrating PVE on T 2WI. Twenty-six percent (14/53) of patients had intracranial hemorrhage, and 11 of these patients demonstrated positive signs of PVE. Parenchymal enhancement was primarily observed in subarachnoid structures in 11% (6/53) of patients, with 5/6 showing PVE on the brain surface or spinal cord surface. Venous infarction (4%, 2/53) and MVE (4%, 2/53) were more commonly seen in high Cognard grade DAVF, whereas encephalopathy mimics (4%, 2/53) were frequently encountered in low Cognard grade DAVF. Conclusions:PVE on the brain surface is a direct sign for the diagnosis of DAVF on routine MRI, yet it is often subtle. Familiarity with common indirect signs is of great importance for the early diagnosis of DAVF.

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.

Objective:To evaluate the diagnostic value of MRI findings in differentiating between tophus and giant cell tumors of the tendon sheath (GCTTS) in the knee.Methods:The study was a case-control study. The clinical and MRI data of patients diagnosed with knee tophus or GCTTS was retrospectively analyzed at the Affiliated Hospital of Qingdao University from September 2018 to September 2024. The study included 23 cases of tophus and 22 cases of GCTTS. MRI sequences, including T 1WI, fat-suppressed T 2WI, and proton density weighted imaging, were evaluated. Parameters including lesion signal intensity and homogeneity, margin, maximum diameter, location (inside or outside the synovial cavity), ligament/tendon involvement, ligament/tendon parenchymal changes, adjacent bone erosion, bone marrow edema, synovial hyperplasia, joint effusion, and periarticular soft tissue swelling were recorded. Independent sample t-tests, χ2 tests, or Fisher exact tests were used to compare MRI findings between the two groups. Multivariate logistic regression was performed to identify independent predictive factors. Results:Significant differences in terms of maximum diameter, location (inside or outside the synovial cavity), ligament/tendon involvement, ligament/tendon parenchymal changes, adjacent bone erosion, bone marrow edema, and periarticular soft tissue swelling between the two groups were found (all P<0.05). No significant differences for other parameters were observed (all P>0.05). Lesion location and ligament/tendon parenchymal involvement demonstrated the highest sensitivity and specificity for distinguishing the two diseases. The sensitivity and specificity values for lesion location were 0.78 and 0.95. The sensitivity and specificity values for ligament/tendon involvement were 0.78 and 1.00. Multivariate logistic regression identified lesion location (inside or outside the synovial cavity) as an independent predictor for differentiating tophus from GCTTS ( OR=31.48, 95% CI 1.58-625.69, P=0.024). Conclusion:The location of the lesion (inside or outside the synovial cavity) and involvement of ligament/tendon parenchyma are critical factors in differentiating knee tophus from GCTTS. Additionally, lesion location serves as an independent predictor for distinguishing between the two conditions.

Objective To observe the value of multiparametric MRI-based radiomics model and deep learning(DL)model for distinguishing benign and malignant myxoid soft tissue tumors(MSTT).Methods A total of 141 MSTT patients confirmed with pathology were retrospectively collected.The patients were randomly divided into training set(n=98,including 51 cases of malignant MSTT and 47 cases of benign MSTT)and test set(n=43,including 22 cases of malignant MSTT and 21 cases of benign MSTT)at the ratio of 7∶3.Based on T1WI and fat suppression(FS)-T2WI in training set,radiomics features and DL features were extracted and selected,then a radiomics model and a DL model were constructed,respectively.Receiver operating characteristic(ROC)curves,calibration curves and decision curves were drawn,and the discrimination,calibration and net benefit of these 2 models were compared.Results In training set,the radiomics model for differentiating benign and malignant MSTT was constructed according to 9 optimal radiomics features,including 2 first order features,1 shape feature,3 gray level co-occurrence matrix features,1 gray level dependence matrix feature and 2 gray level size zone matrix features,while DL model was built based on 7 optimal DL features.In test set,the area under the ROC curve of radiomics model and DL model was 0.758 and 0.911,respectively,the latter was higher than the former(P=0.017).Both models had good calibration,and DL model had higher overall net benefit.Conclusion Compared with radiomics model,DL model based on MRI had better ability to differentiating benign and malignant MSTT,also higher overall net benefit.

Objective:To investigate the distribution and morphological characteristics of brain magnetic resonance imaging (MRI) lesions in patients with myelin oligodendrocyte glycoprotein (MOG) antibody related demyelinating diseases and aquaporin-4 (AQP4) antibody positive neuromyelitis optica spectrum disorders (NMOSD) and their clinical value in early diagnosis.Methods:A total of 35 patients with MOG antibody related demyelinating diseases [20 males and 15 females; aged 31 (25, 43) years] and 36 patients with AQP4 antibody positive NMOSD [3 males and 33 females; aged 42 (29, 54) years] were collected retrospectively from September 2018 to June 2021 in Chenzhou First People′s Hospital and the Affiliated Hospital of Qingdao University which were classified as MOG group and AQP4 positive group respectively. All patients underwent routine cranial MRI scanning before treatment and the location, shape and quantity of intracranial lesions were recorded. Wilcoxon rank sum test was used to compare the number of different types of lesions between the two groups. Logistic regression analysis was used to evaluate the significance of different lesions for the two diseases.Results:There were 7 types of lesions with significant differences in different parts and shapes. Stepwise Logistic regression showed that cortical and juxtacortical lesions ( OR=21.91, 95% CI 3.09-61.69, P<0.05) and infratentorial peripheral white matter lesions ( OR=10.48, 95% CI 2.00-18.89, P<0.05) were the most important risk factors in the MOG group. The incidence of cortical and juxtacortical lesions in the MOG group was 51.4% (18/35), which was higher than that in the AQP4 positive group (2.8%, 1/36; χ2=19.02, P<0.01). The incidence of infratentorial peripheral white matter lesions in the MOG group was 31.4% (11/35), which was higher than that in the AQP4 positive group (5.6%, 2/36; χ2=6.31, P<0.05). Receiver operating characteristic (ROC) curve showed that peripheral lesions [including 6 types of lesions such as supratentorial soft meningitis, cortical encephalitis, cortical and juxtacortical lesions, infratentorial soft meningitis, infratentorial soft meningeal demyelination and infratentorial peripheral lesions, area under curve (AUC)=0.93] were more important than cortical and juxtacortical lesions (AUC=0.75) and central lesions (supratentorial paraventricular white matter lesions, diencephalon, infratentorial paraventricular lesions,AUC=0.64), which had higher diagnostic efficiency. Conclusions:The incidence of intracranial lesions in MOG antibody related demyelinating disease was higher than that in AQP4 positive NMOSD, and the distribution and morphology of intracranial MRI lesions in the two diseases had their characteristic manifestations. Identifying the distribution patterns of peripheral lesions (distributed along pia mater) and central lesions (distributed along ependyma) had a certain reference significance for distinguishing the two groups of diseases.

Objective:To explore the value of quantitative parameters of dynamic contrast-enhanced MRI(DCE-MRI) in evaluating the biological behavior of soft tissue tumors.Methods:The clinical data of 69 patients with soft tissue tumors confirmed by pathology in the Affiliated Hospital of Qingdao University from January 2017 to December 2019 were analyzed retrospectively, including 29 benign tumors and 40 malignant tumors. All patients were examined by routine MRI and DCE-MRI before the operation. The DCE-MRI parameters including volume transfer constant (K trans), rate constant (K ep) and extracellular space volume fraction (V e) were acquired by post-processing software analysis. Microvessel density (MVD) and Ki-67 labeling index (Ki-67 LI) were detected using immunohistoche mical method. Spearman correlation test was used to analyze the correlation between DCE-MRI quantitative parameters and MVD and Ki-67 LI.Independent sample t-test or Mann-Whitney U test was used to compare the difference of parameters between benign and malignant group, and the receiver operating characteristic (ROC) curves were performed to evaluate the diagnostic value. Results:There was positive correlation between K trans, K ep and MVD ( r=0.633, 0.727, P<0.0l), and positive correlation between K trans, K ep and Ki-67 LI ( r=0.557, 0.612, P<0.01). There was no correlation between V e and MVD, Ki-67 LI ( P>0.05). The K trans, K ep, MVD and Ki-67 LI in the malignant group were higher than those in the benign group, and the differences were significant ( P<0.05).There was no significant difference in V e value between malignant group and benign group. When K trans value of 0.169/min was used, the sensitivity, specificity and area under the ROC curve (AUC) for differentiating benign and malignant soft tissue tumors were 84.6%, 85.8% and 0.859, respectively. When K ep value of 0.367/min was used, the sensitivity, specificity and AUC were 92.3%, 83.3% and 0.846, respectively. Conclusion:The DCE-MRI quantitative parameters K trans and K ep can be used to evaluate the biological behavior of soft tissue tumors.

Purpose To investigate the diameter change of dorsal root ganglion (DRG) in lumbar disc herniation using three-dimensional MR neurography. Materials and Methods Sixty-ifve patients with lumbar disc herniation and 30 healthy volunteers were selected. Bilateral DRG diameter was measured using MR three-dimensional constructive interference steady state (3D-CISS) sequence at the level of L3-S1 in the control group and at the level of herniation disc in patient group including central and lateral subgroups. The relationship between the sagittal index and DRG diameter at the level of herniation disc was analyzed. Results In the control group, the DRG diameters increased from the level of L3 to S1. The DRG diameters of the central subgroup were bigger than those of the control group (t=-2.485--2.253, P<0.05). The DRG diameters of the lateral subgroup were bigger on the diseased side than the contralateral side (t=1.956-2.432, P<0.05). The DRG diameters of the contralateral side in lateral subgroup were slightly bigger than those of control group without statistical signiifcance (t=-1.248--0.981, P>0.05). The sagittal index was not correlated with DRG diameter. Conclusion 3D-CISS sequence clearly demonstrates morphological changes of lumbosacral nerve root and measures its diameter.