Objective To explore the value of the Transformer model based on multi-sequence MRI to predict isocitrate dehydrogenase(IDH)mutation status in patients with glioma.Methods The multi-sequence MRI data of 500 glioma patients(103 mutation-type and 397 wild-type)were analyzed retrospectively from the publicly available dataset Cancer Imaging Archive.The prediction model was constructed through the Transformer deep learning algorithm.Area under the curve(AUC)of the receiver operating characteristic(ROC)curve was used to evaluate the predictive performance,and the five-fold crossover was used for validation of the predictive model.Results The clinical,multi-sequence MRI,and combined clinical+multi-sequence MRI models based on the Transformer deep learning algorithm could be used to predict the IDH mutation status of patients with glioma,and the combined clinical+multi-sequence MRI model had the highest diagnostic efficacy compared with the former two,with an AUC of 0.904[95％confidence interval(CI)0.875-0.928],and the sensitivity and specificity of 86.41％and 86.40％,respectively.DeLong's test showed that the difference in AUC between the combined clinical+multi-sequence MRI model and the clinical model was statistically significant(Z=3.327,P＜0.001).Conclusion The Transformer model based on multi-sequence MRI can effectively identify patients with IDH mutation-type glioma and IDH wild-type glioma.

Objective To explore the value of the Transformer model based on multi-sequence MRI to predict isocitrate dehydrogenase(IDH)mutation status in patients with glioma.Methods The multi-sequence MRI data of 500 glioma patients(103 mutation-type and 397 wild-type)were analyzed retrospectively from the publicly available dataset Cancer Imaging Archive.The prediction model was constructed through the Transformer deep learning algorithm.Area under the curve(AUC)of the receiver operating characteristic(ROC)curve was used to evaluate the predictive performance,and the five-fold crossover was used for validation of the predictive model.Results The clinical,multi-sequence MRI,and combined clinical+multi-sequence MRI models based on the Transformer deep learning algorithm could be used to predict the IDH mutation status of patients with glioma,and the combined clinical+multi-sequence MRI model had the highest diagnostic efficacy compared with the former two,with an AUC of 0.904[95％confidence interval(CI)0.875-0.928],and the sensitivity and specificity of 86.41％and 86.40％,respectively.DeLong's test showed that the difference in AUC between the combined clinical+multi-sequence MRI model and the clinical model was statistically significant(Z=3.327,P＜0.001).Conclusion The Transformer model based on multi-sequence MRI can effectively identify patients with IDH mutation-type glioma and IDH wild-type glioma.

Objectives: Bipolar hemiarthroplasty is commonly performed to treat displaced femoral neck fractures in osteo porotic patients. This study aimed to assess the occurrence and outcomes of unplanned return visits to the emergency department (ED) within 90 days following bipolar hemiarthroplasty for displaced femoral neck fractures. Methods: The clinical data of 1322 consecutive patients who underwent bipolar hemiarthroplasty for osteoporotic femoral neck fractures at a tertiary medical center were analyzed. Data from the patients’ electronic medical records, including demographic information, comorbidities, and operative details, were collected. The risk factors and mortality rates were analyzed. Results: Within 90 days after surgery, 19.9% of patients returned to the ED. Surgery-related reasons accounted for 20.2% of the patient’s returns. Older age, a high Charlson comorbidity index score, chronic kidney disease, and a history of cancer were identified as significant risk factors for unplanned ED visits. Patients with uncemented implants had a significantly greater risk of returning to the ED due to periprosthetic fractures than did those with cemented implants (P = 0.04). Patients who returned to the ED within 90 days had an almost fivefold greater 1-year mortality rate (15.2% vs 3.1%, P < 0.001) and a greater overall mortality rate (26.2% vs 10.5%, P < 0.001). Conclusions This study highlights the importance of identifying risk factors for unplanned ED visits after bipolar hemiarthroplasty, which may contribute to a better prognosis. Consideration should be given to the use of cemented implants for hemiarthroplasty, as uncemented implants are associated with a greater risk of peri prosthetic fractures.

Objective To observe the value of contrast-enhanced CT radiomics combined with clinical and hematology indicators for predicting lymph node(LN)metastasis(LNM)of esophageal squamous cell carcinoma(ESCC).Methods Totally 218 ESCC patients were retrospectively enrolled.Stage pN1 and pN2 were clustering as LNM(n=90),while stage pN0 were taken as non-LNM(n=128).The patients were divided into training set(n=174)and test set(n=44)at the ratio of 8∶2.In training set,clinical and LN imaging features which could be used to independently judge LNM were screened and a clinical-imaging model was constructed.The hematological indicators that might be associated with ESCC LNM were screened,and a hematological model was constructed.Radiomics features in LN ROI and ESCC volume of interest(VOI)were extracted based on venous-phase contrast-enhanced CT images,and those might be associated with LNM were screened,and a radiomics model was constructed.Finally a combined model was constructed based on all the above features.The efficacy of each model for diagnosing LNM was evaluated with the area under the curve(AUC)of receiver operating characteristic curves,and the clinical net benefit was evaluated using decision curve analysis(DCA).Results Body mass index(BMI)and internal necrosis of target LN were both independent judging factors for ESCC LNM(both P＜0.05),and AUC of clinical-imaging model for diagnosing LNM in training and test sets was 0.747 and 0.687,respectively.Seven hematological indicators were included in hematological model,and AUC in training and test sets was 0.623 and 0.583,respectively.Ten LN radiomics features and 15 ESCC radiomics features were included in radiomics model,and AUC in training and test sets was 0.769 and 0.745,respectively.AUC of the combined model for diagnosing LNM in training and test sets was 0.822 and 0.739,respectively,better than other models in training set(all P＜0.05),but no significantly different in test set(all P＞0.05).DCA showed that combined model had higher net gain than the other models in 0.55-0.80 threshold probability interval.Conclusion Combined model based on venous-phase contrast-enhanced CT radiomics and clinical and hematology indicators could relatively effectively evaluate ESCC LNM,which might bring some promotions in clinical benefit.

Purpose To analyze the correlation between quantitative parameters of spectral CT and the mutation status of epidermal growth factor receptor(EGFR)and anaplastic lymphoma kinase(ALK)genes in solid lung adenocarcinoma,in order to predict the expression of gene mutation in solid lung adenocarcinoma by combining spectral parameters with clinical indicators.Materials and Methods The imaging and clinical data of 86 patients in the First Affiliated Hospital of Xinxiang Medical College pathologically confirmed with lung adenocarcinoma who underwent energy spectral CT plain and enhanced scans from May 2020 to May 2022 were retrospectively collected.According to the mutation status of EGFR and ALK genes,all patietns were divided into mutation group(43 cases)and wild group(43 cases).The single-energy CT values(including 40 keV,70 keV and 100 keV),slope of energy spectrum curve,effective atomic number during plain scan,iodine concentration in arterial and venous phases,water concentration and iodine concentration in arteries at the same level were obtained,and standardized iodine base value was also calculated.Statistically significant parameters were used to construct the prediction model,and the receiver operating characteristic curve was performed to evaluate the prediction efficiency of the model.Results There were statistically significant differences in gender and smoking status between the EGFR mutation group and the wild-type group(χ2=5.628,P=0.018;χ2=4.214,P=0.040).There were statistically significant differences in plain scan effective atomic number and energy spectrum slope,arterial phase 40 keV,70 keV and 100 keV single-energy CT values,40 keV single-energy CT values in the venous phase,the slope of the arteriovenous dual-phase energy spectrum and the standardized iodine base value between the two groups(t=2.067-4.394,all P<0.05).Logistic regression analysis showed that normalized iodine base value and energy spectrum slope in venous phase were the independent predictors of EGFR gene mutation status.There were no significant differences in quantitative parameters of plain scan and arterial phase energy spectrum CT between the ALK mutant group and the wild-type group(P>0.05).There was statistically significant difference in water base value of venous phase between the two groups(t=2.058,P=0.043).Conclusion The quantitative features of spectral CT may be correlated with the mutation status of EGFR and ALK genes in solid lung adenocarcinoma,and the Logistic regression model based on the combination of quantitative features of spectral CT and clinical features has certain value in predicting EGFR gene mutation.

Purpose To explore the ultrasound features and early diagnostic clues of fetal myocardial non-compaction.Materials and Methods The clinical data and echocardiographic data of four fetuses who underwent fetal echocardiography in Henan Provincial People's Hospital from January 2015 to February 2023 and were confirmed to have myocardial non-compaction by pathological finding or postnatal examination were collected,and analyzed.Results A total of four fetuses diagnosed as myocardial non-compaction by prenatal ultrasound:two involved the left ventricle with isolated lesions,and apical myocardial non-compaction was confirmed by postnatal echocardiography;two involved the biventricles,and both of which were pathologically confirmed after induction of labor.The prenatal ultrasound of fetal myocardial involvement in four cases showed that:(1)the affected myocardium showed a bilayer structure:the outer layer was compacted myocardium,which showed thin and compacted homogeneous hypoechoic;the inner layer was loose and thickened non-compacted myocardium with enhanced echogenicity;(2)color Doppler flow imaging:the non-compacted myocardium showed sieve mesh blood flow with ventricular communication.Some cases were associated with cardiac enlargement and arrhythmia.Conclusion Prenatal echocardiography can diagnose fetal myocardial non-compaction with a characteristic echographic presentation.Localized myocardial thickening and echogenic enhancement,cardiac enlargement and arrhythmia may be important clues to identify fetal myocardial non-compaction.