Objective To explore the preoperative non-invasive prediction of early postoperative glioma recurrence using multi-modal conventional MRI radiomics.Methods A retrospective analysis of the clinical and MRI data of 83 glioma patients who met the inclusion criteria was conducted.The Kruskal-Wallis test was used to compare clinical factors between recurrent and non-recurrent groups.The automated segmentation of the entire tumor lesion for glioma patients was accomplished with VB-Net algorithm,a deep learning approach developed by United Imaging Healthcare;and the extraction of radiomics features from preoperative T1CE and T2WI images was carried out on URP platform.The optimal feature combination was determined using the maximum relevance and minimum redundancy and least absolute shrinkage and selection operator methods.Logistic regression and five-fold cross-validation were employed to analyze radiomics features and construct 4 prediction models,namely T2WI model,T1CE model,T2WI+T1CE model,and imaging-clinical fusion model.The diagnostic performances of these models were evaluated and compared using the area under the receiver operating characteristic curve(AUC)and the Delong test.In addition,the model sensitivity and specificity were calculated.Results Postoperatively,there were 40 recurrent cases and 43 non-recurrent cases.The clinical factors such as glioma grade showed statistical significance between two groups(P<0.05),while gender and age did not show significant statistical differences(P>0.05).For the single-sequence radiomics models,T1CE model(AUC:0.804)outperformed T2WI model(AUC:0.702).The multi-modal combined model exhibited a higher AUC than the single-sequence prediction models,with an AUC of 0.849,a sensitivity of 72.5%,and a specificity of 79.1%.The imaging-clinical fusion model whose predictive efficiency was close to that of multi-modal combined model(P=0.303)also performed well in forecasting postoperative glioma recurrence,with an AUC of 0.839,a sensitivity of 72.5%,and a specificity of 79.1%.Conclusion The multi-modal conventional MRI radiomics model can better predict early postoperative glioma recurrence.The imaging-clinical fusion model that includes glioma grade does not have the diagnostic performance superior to that of radiomics model.

Objective To explore the preoperative non-invasive prediction of early postoperative glioma recurrence using multi-modal conventional MRI radiomics.Methods A retrospective analysis of the clinical and MRI data of 83 glioma patients who met the inclusion criteria was conducted.The Kruskal-Wallis test was used to compare clinical factors between recurrent and non-recurrent groups.The automated segmentation of the entire tumor lesion for glioma patients was accomplished with VB-Net algorithm,a deep learning approach developed by United Imaging Healthcare;and the extraction of radiomics features from preoperative T1CE and T2WI images was carried out on URP platform.The optimal feature combination was determined using the maximum relevance and minimum redundancy and least absolute shrinkage and selection operator methods.Logistic regression and five-fold cross-validation were employed to analyze radiomics features and construct 4 prediction models,namely T2WI model,T1CE model,T2WI+T1CE model,and imaging-clinical fusion model.The diagnostic performances of these models were evaluated and compared using the area under the receiver operating characteristic curve(AUC)and the Delong test.In addition,the model sensitivity and specificity were calculated.Results Postoperatively,there were 40 recurrent cases and 43 non-recurrent cases.The clinical factors such as glioma grade showed statistical significance between two groups(P<0.05),while gender and age did not show significant statistical differences(P>0.05).For the single-sequence radiomics models,T1CE model(AUC:0.804)outperformed T2WI model(AUC:0.702).The multi-modal combined model exhibited a higher AUC than the single-sequence prediction models,with an AUC of 0.849,a sensitivity of 72.5%,and a specificity of 79.1%.The imaging-clinical fusion model whose predictive efficiency was close to that of multi-modal combined model(P=0.303)also performed well in forecasting postoperative glioma recurrence,with an AUC of 0.839,a sensitivity of 72.5%,and a specificity of 79.1%.Conclusion The multi-modal conventional MRI radiomics model can better predict early postoperative glioma recurrence.The imaging-clinical fusion model that includes glioma grade does not have the diagnostic performance superior to that of radiomics model.

Objective To investigate the value of multimodal MRI radiomics in predicting muscle-invasive bladder cancer.Methods A total of 178 patients with pathology diagnosis of bladder cancer were retrospectively collected,including 31 cases of muscle invasive bladder cancer(MIBC)and 147 cases of non-muscle invasive bladder cancer(NMIBC).Patients were randomly divided into training group and testing group at a ratio of 7︰3.The range of bladder tumors in T2WI,diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)images were segmented as volume of interest(VOI)by using ITK-SNAP software.Radiomics features were extracted through A.K software.The optimal radiomics features were obtained through radiomics algorithm and least absolute shrinkage and selection operator(LASSO)method.Finally,the logistic regression analysis method and random forest model method were used to construct prediction models.The performance of prediction models was evaluated by the receiver operating characteristic(ROC)curve.Results This study constructed four groups of models containing T2WI prediction model,DWI prediction model,ADC prediction model,and T2WI+DWI+ADC prediction model.The area under the curve(AUC)of T2WI,DWI,and ADC prediction models for identifying MIBC and NMIBC were separately 0.920,0.914,and 0.954 in the training group while those were respectively 0.881,0.773,and 0.871 in the testing group.There was no statistical significance between T2WI,DWI,and ADC prediction models.In training and testing groups,the AUC of T2WI+DWI+ADC prediction model were respectively 0.959 and 0.909,which were higher than the single sequence prediction model.The sensitivity and specificity of the training group were 0.905 and 0.853 and the sensitivity and specificity of the testing group were 0.778 and 0.795.Conclusion MRI radiomics prediction model can effectively differentiate MIBC and NMIBC.The T2WI+DWI+ADC prediction model shows better prediction efficiency.

Objective To explore the comparative study of constructing prediction models for muscle invasive of bladder cancer based on different machine learning algorithms combined with MRI radiomic.Methods A total of 187 bladder cancer patients who underwent MRI examination and were confirmed by pathology were retrospectively selected.Patients were randomly divided into a training set and a test set in a 7∶3 ratio.The patients were divided into muscle invasive bladder cancer(MIBC)group and non-muscle invasive bladder cancer(NMIBC)group according to the surgical pathology results.Tumor volume of interest(VOI)was outlined on the images of T2 WI,diffusion weighted imaging(DWI),and apparent diffusion coefficient(ADC),and the radiomic features were extracted by A.K software,and dimensionality reduction was performed using the maximum relevance minimum redundancy(mRMR)algorithm combined with least absolute shrinkage and selection operator(LASSO).Six machine learning algorithms,including K-nearest neighbor(KNN),decision tree(DT),support vector machine(SVM),logistic regression(LR),random forest(RF),and explainable boosting machine(EBM)were used to construct the radiomic model and calculate the corresponding area under the curve(AUC),accuracy,sensitivity,and specificity,respectively.Results Six machine learning algorithms,including KNN,DT,SVM,LR,RF,and EBM were used to construct the radiomic model,and the AUC values for predicting MIBC in the training set were 0.863,0.838,0.853,0.866,0.977,0.997,and in the test set were 0.748,0.833,0.860,0.868,0.870,0.900.Among them,the MRI radiomic model constructed based on EBM had the highest predictive efficacy for MIBC,with AUC values,accuracy,sensitivity and specificity of 0.997,0.977,0.957 and 0.981 in the training set,and 0.900,0.877,0.800,and 0.894 in the test set,respectively.Conclusion Multiple machine learning algorithms combined with MRI radiomic to construct models have good predictive efficacy for MIBC,and the model constructed based on EBM shows the highest predictive value.

Objective:Based on the Ancient and Modern Medical Record Cloud Platform, we aimed to analyze the rules of TCM compound patents for the treatment of acute pancreatitis.Methods:Compound patents for acute pancreatitis were retrieved from the National Patent Database. After the steps of data screening, data entry, and data specification, a database of compound patents treated for acute pancreatitis was established. The frequency analysis, attribute analysis, association analysis, cluster analysis, and complex network analysis were performed by using the Ancient and modern medical record cloud platform.Results:A total of 87 compound patents were obtained, comprising 213 herbs, of which the core drugs were Rhei radix et rhizoma, Bupleuri radix, Aurantii fructus immaturus, Glycyrrhizae radix et rhizoma, Magnoliae officinalis cortex, Corydalis rhizoma, Scutellariae radix, Aucklandiae radix, Natrii sulfas, Coptidis rhizoma. The drugs were mainly warm, cold and slightly cold, and the drugs taste mostly bitter and spicy, and the drugs mainly belonged to the spleen meridian and liver meridian. The cluster analysis results contained 5 categories. The associations of drugs included Bupleuri radix - Rhei radix et rhizoma, Aurantii fructus immaturus - Rhei radix et rhizoma, Magnoliae officinalis cortex - Rhei radix et rhizoma, for which complex network analysis yielded a core composition of Rhei radix et rhizoma, Bupleuri radix, Glycyrrhizae radix et rhizoma, Natrii sulfas, Aurantii fructus immaturus, Corydalis rhizoma, Scutellariae radix, Magnoliae officinalis cortex. Conclusion:The eliminating stasis by purging for acute pancreatitis is dominated by Rhei radix et rhizoma, channeling Fu Qi method is based on Aurantii fructus immaturus and Bupleuri radix, and eliminating stasis by purging combined with channeling Fu Qi methods can be used with Magnoliae officinalis cortex, Natrii sulfas, etc.

Objective:To analyze the potential mechanism of Qifang Weitong granules in the treatment of gastric cancer based on network pharmacology and molecular docking method.Methods:TCMSP, TCMID, and Swiss Target Prediction databases were used to screen out the chemical components and related targets of Qifang Weitong Granules. GeneCards and OMIM databases were used to screen out the gastric cancer targets to obtain common targets of this disease and Qifang Weitong Granules and upload them to STRING database to form a PPI network, and obtain the key targets and analyze the correlation between the key targets and gastric cancer in Oncomine tumor database. In addition, the regulatory network of gastric cancer and Qifang Weitong Granules was constructed by using Cytoscape software, and the CluoGO plug-in and R language of Cytoscape software were used to perform GO and KEGG enrichment analysis on the key targets. The possibility of the binding between the molecules of this medicine and targeted molecules is verified by molecular docking.Results:There were 168 medicinal chemical components obtained in Qifang Weitong Granules, 2 803 gastric cancer targets, and 49 common targets. In the regulatory network of gastric cancer and Qifang Weitong Granules, β-sitosterol, formononet, stigmasterol have higher values of chemical composition. The key targets in the PPI network are MAPK8, FOS, AR, etc. The GO enrichment analysis focused on the positive regulation of mitochondrial outer membrane permeability in the apoptosis signaling pathway, while the KEGG enrichment analysis is significantly enriched in apoptosis access. The result of molecular docking showed good binding and stable conformation.Conclusion:Qifang Weitong Granules can induce the expression of genes and proteins related to gastric cancer, show its effect by affecting the level of hormones, cell apoptosis and other biological processes, and activating the apoptosis signal pathway.

Objective:To evaluate the clinical efficacy of TCM Qingjie Huagong Decoction combined with routine internal medicine in the treatment of severe acute pancreatitis with cholelithiasis (bile duct stones) in the early stage.Methods:Thirty-two patients with severe acute pancreatitis combined with cholelithiasis in the first affiliated Hospital of GuangXi University of Traditional Chinese Medicine were selected and randomly divided into two groups with 16 in each, both groups were treated for 14 days. Serum amylase (AMS) was detected by iodine-starch colorimetry, GOT and GPT were detected by continuous monitoring method, and CRP, IL-6 and procalcitonin (PCT) were detected by immune transmission turbidimetry. Acute Physiological and Chronic Health Score Ⅱ (APACHE Ⅱ), CT Severity Index Score (CTSI) and Modified Marshall Score were used to evaluate the severity of SAP. The recovery time of body temperature, the relief time of abdominal distension pain, the recovery time of bowel sounds and the total hospital stay were observed and recorded to evaluate the clinical effect.Results:The total effective rate was 93.8% (15/16) in the treatment group and 75.0% (12/16) in the control group. There was significant difference between the two groups ( χ2=8.19, P=0.042). After treatment, the level of AMS, WBC, CRP, PCT, AST, ALT and IL-6 in the treatment group were lower than those in the control group ( t values were 14.3, 7.24, 9.63, 5.48, 7.05, 7.33, 28.34, respectively, all Ps<0.05); After treatment, the time for body temperature to return to normal [(2.91±0.12)d vs. (3.78±0.38)d, t=8.76], the time for relief of abdominal distension pain [(4.77±0.68)d vs. (7.13±1.55)d, t=9.52], the time for recovery of bowel sounds [(3.90±1.80)d vs. (4.89±1.38)d, t=2.98] and the total hospital stay [(22.60±2.80)d vs. (30.37±3.89)d, t=7.88] in the treatment group were all significantly shorter than those in the control group ( P<0.01); APACHE Ⅱ, CTSI and the Modified Marshall Score in the treatment group were lower than those in the control group ( t values were 11.82, 12.72, 7.71, respectively, all Ps<0.01). Conclusion:Qingjie Huagong Decoction combined with ERCP and conventional western medicine therapy can reduce the level of inflammation in patients with cholelithiasis in the early stage of SAP, relieve clinical symptoms and improve clinical efficacy.

Humans ; Pneumoconiosis ; diagnosis