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 investigate the value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.

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 investigate the value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.

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 expression levels of serum miR-145 and HMGB3 in patients with gallbladder cancer(GBC)and their correlation with disease progression,verify the effects of miR-145 on HMGB3 expression and the proliferation and ap-optosis of GBC cells,and evaluate its feasibility as a potential diagnostic and prognostic biomarker.Methods Ninety patients with gall-bladder cancer admitted to the First Affiliated Hospital of Hebei North University from September 2018 to June 2022 were selected as the disease group,and 40healthy subjects were selected as the normal control group.The expression levels of serum miR-145 and HMGB3 in two groups were detected.The relationship between different clinicopathological features and the expression levels of miR-145 and HMGB3 was compared.Receiver operating characteristic(ROC)curve was used to analyze the diagnostic value of serum miR-145 and HMGB3;Kaplan-Meier survival curve was used to evaluate the 3-year survival rate of patients with different miR-145 and HMGB3 ex-pression levels.The effects of miR-145 on proliferation and apoptosis of GBC cells were analyzed by CCK-8 assay and flow cytometry.The protein expression was detected by Western blot.The apoptosis rate of each group was detected by flow cytometry.The direct interac-tion between miR-145 and HMGB3 was verified by double luciferase reporter gene assay.Results The expression level of miR-145 in serum of patients with GBC was significantly decreased(P＜0.05),while the expression level of HMGB3 was significantly increased(P＜0.05).The expressions of serum miR-145 and HMGB3 were correlated with differentiation degree,lymph node metastasis and TNM stage(P＜0.05).ROC curve analysis showed that serum miR-145 and HMGB3had high diagnostic value.Kaplan-Meire survival curve analysis showed that the 3-year survival rate of miRNA-143high expression subgroup was higher than that of low expression sub-group(P＜0.05),and the 3-year survival rate of HMGB3 low expression subgroup was significantly higher than that of high expression subgroup(P＜0.05).Overexpression of miR-145 inhibited the proliferation of GBC cells,significantly increased the expression of Bax protein in BGC-SD cells(P＜0.05),decreased the expression of Bel-2 protein(P＜0.05),decreased the expression of HMGB3 pro-tein(P＜0.05),and increased the apoptosis rate of BGC-SD cells.Double luciferase reporter gene assay showed that HMGB3 was the target of miR-145.Conclusion The downregulation of miR-145 is significantly associated with the upregulation of HMGB3 in GBC,and is related to the severity of the disease.Overexpression of miR-145 can significantly inhibit the proliferation and promote apoptosis of GBC cells,and the mechanism of action may be achieved by directly targeting HMGB3.Therefore,miR-145 and HMGB3may serve as potential biomarkers for the diagnosis and prognosis of GBC,providing a new strategy for molecular targeted therapy of GBC.

Objective To investigate the expression levels of serum miR-145 and HMGB3 in patients with gallbladder cancer(GBC)and their correlation with disease progression,verify the effects of miR-145 on HMGB3 expression and the proliferation and ap-optosis of GBC cells,and evaluate its feasibility as a potential diagnostic and prognostic biomarker.Methods Ninety patients with gall-bladder cancer admitted to the First Affiliated Hospital of Hebei North University from September 2018 to June 2022 were selected as the disease group,and 40healthy subjects were selected as the normal control group.The expression levels of serum miR-145 and HMGB3 in two groups were detected.The relationship between different clinicopathological features and the expression levels of miR-145 and HMGB3 was compared.Receiver operating characteristic(ROC)curve was used to analyze the diagnostic value of serum miR-145 and HMGB3;Kaplan-Meier survival curve was used to evaluate the 3-year survival rate of patients with different miR-145 and HMGB3 ex-pression levels.The effects of miR-145 on proliferation and apoptosis of GBC cells were analyzed by CCK-8 assay and flow cytometry.The protein expression was detected by Western blot.The apoptosis rate of each group was detected by flow cytometry.The direct interac-tion between miR-145 and HMGB3 was verified by double luciferase reporter gene assay.Results The expression level of miR-145 in serum of patients with GBC was significantly decreased(P＜0.05),while the expression level of HMGB3 was significantly increased(P＜0.05).The expressions of serum miR-145 and HMGB3 were correlated with differentiation degree,lymph node metastasis and TNM stage(P＜0.05).ROC curve analysis showed that serum miR-145 and HMGB3had high diagnostic value.Kaplan-Meire survival curve analysis showed that the 3-year survival rate of miRNA-143high expression subgroup was higher than that of low expression sub-group(P＜0.05),and the 3-year survival rate of HMGB3 low expression subgroup was significantly higher than that of high expression subgroup(P＜0.05).Overexpression of miR-145 inhibited the proliferation of GBC cells,significantly increased the expression of Bax protein in BGC-SD cells(P＜0.05),decreased the expression of Bel-2 protein(P＜0.05),decreased the expression of HMGB3 pro-tein(P＜0.05),and increased the apoptosis rate of BGC-SD cells.Double luciferase reporter gene assay showed that HMGB3 was the target of miR-145.Conclusion The downregulation of miR-145 is significantly associated with the upregulation of HMGB3 in GBC,and is related to the severity of the disease.Overexpression of miR-145 can significantly inhibit the proliferation and promote apoptosis of GBC cells,and the mechanism of action may be achieved by directly targeting HMGB3.Therefore,miR-145 and HMGB3may serve as potential biomarkers for the diagnosis and prognosis of GBC,providing a new strategy for molecular targeted therapy of GBC.

Objective:To investigate the relationship between serum soluble receptor activator of nuclear factor-κB ligand (sRANKL), Omentin-1 levels and postmenopausal osteoporosis (PMOP) .Methods:A total of 310 menopausal patients admitted to Qingdao Municipal Hospital from Jun. 2017 to Jul. 2021 were selected, including 165 patients with PMOP and 145 women with simple menopause as the control group. Serum sRANKL and Omentin-1 levels were detected by ELISA. Bone mineral density and bone metabolism indexes [N-terminal propeptide of typeⅠprecollagen (PINP), bone alkaline phosphatase (BALP), β isomer of the C-terminal telopeptide of type Ⅰ collagen (β-CTX) and osteocalcin (OC) ] were compared between the two groups. The correlation between serum sRANKL and Omentin-1 levels and bone mineral density and bone metabolism indexes in PMOP patients was analyzed by Pearson. The predictive value of sRANKL and Omentin-1 to PMOP was analyzed by ROC curve. Logistic regression analysis of the influence of multiple factors on PMOP.Results:Compared with the control group (15.62±4.41) (42.56±8.53), the serum sRANKL level (26.63±8.12) was increased and Omentin-1 level (32.32±5.52) was decreased in PMOP group ( t=14.55, P<0.001; t=12.69, P<0.001). The serum sRANKL in PMOP group was positively correlated with PINP, β-CTX and OC, while the serum Omentin-1 level was negatively correlated with the above indexes by Pearson analysis. ROC curve showed that serum sRANKL and Omentin-1 had important reference significance in predicting PMOP. Logistic regression suggested that increased sRANKL and decreased Omentin-1 were risk factors for PMOP. Conclusion:Serum sRANKL and Omentin-1 in patients with PMOP are correlated with bone mineral density and bone metabolism, and have potential as diagnostic targets of PMOP.