Objective:To analyze the changes in the prevalence characteristics of multimorbidity among adolescents aged 13-18 in Inner Mongolia Autonomous Region from 2019 to 2022 and to explore the association between multimorbidity and moderate to high-intensity physical activity among them.Methods:A stratified random cluster sampling method was used to select students aged 13-18 in Inner Mongolia Autonomous Region every September from 2019 to 2022. Physical examinations, demographic characteristics, and depression-related surveys were conducted to analyze the multimorbidity of overweight, obesity, high blood pressure, myopia, spinal curvature abnormality, and depression. A logistic regression model was used to analyze the association between multimorbidity and moderate to high-intensity physical activity.Results:From 2019 to 2022, 70 972, 62 923, 80 254, and 78 288 study subjects were included, with the rates of multimorbidity being 56.4%, 55.4%, 57.2%, and 55.8%, respectively. The rates of multimorbidity remained relatively stable from 2019 to 2022 ( χ2=0.06, P=0.950). The incidence of multimorbidity among girls was significantly higher than that among boys ( P<0.001). The incidence of multimorbidity among urban students was significantly higher than that among suburban students ( P<0.001). The incidence of multimorbidity among high school students was higher than that among middle school students ( P<0.001). The top three multimorbidity combinations were myopia and overweight/obesity (26.4%), myopia and high blood pressure (24.4%), and myopia and depression (19.8%), while the least common combination was depression and spinal curvature abnormality (1.1%). The multimorbidity patterns showed no significant differences between years ( χ2=0.03, P=0.999). The multimorbidity status was significantly associated with the status of meeting the standard of moderate to high-intensity physical activity ( OR=0.83, 95% CI: 0.80-0.86). The association was stronger in boys ( OR=0.77, 95% CI: 0.73-0.81) compared with girls ( OR=0.90, 95% CI: 0.85-0.96), with a significant interaction term ( P<0.001). Conclusion:From 2019 to 2022, the incidence of multimorbidity among adolescents aged 13 to 18 in Inner Mongolia Autonomous Region is relatively high, mainly due to the co-occurrence of myopia and other health problems. Adequate physical activity is an important factor in reducing multimorbidity.

Objective:To analyze the changes in the prevalence characteristics of multimorbidity among adolescents aged 13-18 in Inner Mongolia Autonomous Region from 2019 to 2022 and to explore the association between multimorbidity and moderate to high-intensity physical activity among them.Methods:A stratified random cluster sampling method was used to select students aged 13-18 in Inner Mongolia Autonomous Region every September from 2019 to 2022. Physical examinations, demographic characteristics, and depression-related surveys were conducted to analyze the multimorbidity of overweight, obesity, high blood pressure, myopia, spinal curvature abnormality, and depression. A logistic regression model was used to analyze the association between multimorbidity and moderate to high-intensity physical activity.Results:From 2019 to 2022, 70 972, 62 923, 80 254, and 78 288 study subjects were included, with the rates of multimorbidity being 56.4%, 55.4%, 57.2%, and 55.8%, respectively. The rates of multimorbidity remained relatively stable from 2019 to 2022 ( χ2=0.06, P=0.950). The incidence of multimorbidity among girls was significantly higher than that among boys ( P<0.001). The incidence of multimorbidity among urban students was significantly higher than that among suburban students ( P<0.001). The incidence of multimorbidity among high school students was higher than that among middle school students ( P<0.001). The top three multimorbidity combinations were myopia and overweight/obesity (26.4%), myopia and high blood pressure (24.4%), and myopia and depression (19.8%), while the least common combination was depression and spinal curvature abnormality (1.1%). The multimorbidity patterns showed no significant differences between years ( χ2=0.03, P=0.999). The multimorbidity status was significantly associated with the status of meeting the standard of moderate to high-intensity physical activity ( OR=0.83, 95% CI: 0.80-0.86). The association was stronger in boys ( OR=0.77, 95% CI: 0.73-0.81) compared with girls ( OR=0.90, 95% CI: 0.85-0.96), with a significant interaction term ( P<0.001). Conclusion:From 2019 to 2022, the incidence of multimorbidity among adolescents aged 13 to 18 in Inner Mongolia Autonomous Region is relatively high, mainly due to the co-occurrence of myopia and other health problems. Adequate physical activity is an important factor in reducing multimorbidity.

Objective To compare the segmenting efficacy of automatic segmentation models for advanced gastric cancer(AGC)on CT virtual monoenergetic images(VMI),non-linear blending images(NLBI)and mixed-energy images(MEI)based on 3D-nnU-Net.Methods Totally 216 cases of AGC were retrospectively enrolled,among them 185 cases were used to construct,train and validate models and divided into training set(n=154)and test set(n=31)at the ratio of 5∶1,while the other 31 cases were used as validation set to evaluate the generalization of the models.The 70 keV energy level VMI(VMI70 keV),NLBI and MEI were reconstructed with whole-abdominal dual-energy mode venous CT,and automatic segmentation models of AGC,including VMI70 keV,NLBI and MEI models were constructed using 3D-nnU-Net,respectively.Taken manually segmented results as golden standards,the efficacy of each model for segmenting all lesions and T2 stage lesions in test set and validation set were evaluated using Dice similarity coefficient(DSC),intersection over union(IoU)and average symmetric surface distance(ASSD).Results For all lesions in test and validation sets,DSC of 3 models were all＞0.80.DSC and IoU of VMI70 keV and NLBI models were both higher,while their ASSD was lower than those of MEI model(all P＜0.05).For T2 stage AGC in both test set and validation set(each n=5),DSC of MEI model was lower than that of VMI70 keV and NLBI models(both P＜0.05),while IoU of MEI model was lower than that of VMI70 keV model(P＜0.05),and its ASSD was higher than that of NLBI model(P＜0.05).Conclusion All 3D-nnU-Net-based VMI70 keV,NLBI and MEI models could effectively segment AGC on dual-energy CT images,and the segmentation efficacy of the former two were better.

Objective To compare the segmenting efficacy of automatic segmentation models for advanced gastric cancer(AGC)on CT virtual monoenergetic images(VMI),non-linear blending images(NLBI)and mixed-energy images(MEI)based on 3D-nnU-Net.Methods Totally 216 cases of AGC were retrospectively enrolled,among them 185 cases were used to construct,train and validate models and divided into training set(n=154)and test set(n=31)at the ratio of 5∶1,while the other 31 cases were used as validation set to evaluate the generalization of the models.The 70 keV energy level VMI(VMI70 keV),NLBI and MEI were reconstructed with whole-abdominal dual-energy mode venous CT,and automatic segmentation models of AGC,including VMI70 keV,NLBI and MEI models were constructed using 3D-nnU-Net,respectively.Taken manually segmented results as golden standards,the efficacy of each model for segmenting all lesions and T2 stage lesions in test set and validation set were evaluated using Dice similarity coefficient(DSC),intersection over union(IoU)and average symmetric surface distance(ASSD).Results For all lesions in test and validation sets,DSC of 3 models were all＞0.80.DSC and IoU of VMI70 keV and NLBI models were both higher,while their ASSD was lower than those of MEI model(all P＜0.05).For T2 stage AGC in both test set and validation set(each n=5),DSC of MEI model was lower than that of VMI70 keV and NLBI models(both P＜0.05),while IoU of MEI model was lower than that of VMI70 keV model(P＜0.05),and its ASSD was higher than that of NLBI model(P＜0.05).Conclusion All 3D-nnU-Net-based VMI70 keV,NLBI and MEI models could effectively segment AGC on dual-energy CT images,and the segmentation efficacy of the former two were better.

Objective:To investigate the value of CT radiomic model based on analysis of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer in differentiating stage T1-2 from stage T3-4 gastric cancer.Methods:This study was a case-control study. Totally 465 patients with gastric cancer treated in Affiliated People′s Hospital of Jiangsu University from December 2011 to December 2019 were retrospectively collected. According to postoperative pathology, they were divided into 2 groups, one with 150 cases of T1-2 tumors and another with 315 cases of T3-4 tumors. The cases were divided into a training set (326 cases) and a test set (139 cases) by stratified sampling method at 7∶3. There were 104 cases of T1-2 stage and 222 cases of T3-4 stage in the training set, 46 cases of T1-2 stage and 93 cases of T3-4 stage in the test set. The axial CT images in the venous phase during one week before surgery were selected to delineate the region of interest (ROI) at the primary lesion and the extramural gastric adipose tissue adjacent to the cancer areas. The radiomic features of the ROIs were extracted by Pyradiomics software. The least absolute shrinkage and selection operator was used to screen features related to T stage to establish the radiomic models of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer. Independent sample t test or χ2 test were used to compare the differences in clinical features between T1-2 and T3-4 patients in the training set, and the features with statistical significance were combined to establish a clinical model. Two radiomic signatures and clinical features were combined to construct a clinical-radiomics model and generate a nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate the efficacy of each model in differentiating stage T1-2 from stage T3-4 gastric cancer. The calibration curve was used to evaluate the consistency between the T stage predicted by the nomogram and the actual T stage of gastric cancer. And the decision curve analysis was used to evaluate the clinical net benefit of treatment guided by the nomogram and by the clinical model. Results:There were significant differences in CT-T stage and CT-N stage between T1-2 and T3-4 patients in the training set ( χ2=10.59, 15.92, P=0.014, 0.001) and the clinical model was established. After screening and dimensionality reduction, the 5 features from primary gastric cancer and the 6 features from the adipose tissue outside the gastric wall beside cancer established the radiomic models respectively. In the training set and the test set, the AUC values of the primary gastric cancer radiomic model were 0.864 (95% CI 0.820-0.908) and 0.836 (95% CI 0.762-0.910), and the adipose tissue outside the gastric wall beside cancer radiomic model were 0.782 (95% CI 0.731-0.833) and 0.784 (95% CI 0.702-0.866). The AUC values of the clinical model were 0.761 (95% CI 0.705-0.817) and 0.758 (95% CI 0.671-0.845), and the nomogram were 0.876 (95% CI 0.835-0.917) and 0.851 (95% CI 0.781-0.921). The calibration curve reflected that there was a high consistency between the T stage predicted by the nomogram and the actual T stage in the training set ( χ2=1.70, P=0.989). And the decision curve showed that at the risk threshold 0.01-0.74, a higher clinical net benefit could be obtained by using a nomogram to guide treatment. Conclusions:The CT radiomics features of primary gastric cancer lesions and the adipose tissue outside the gastric wall beside cancer can effectively distinguish T1-2 from T3-4 gastric cancer, and the combination of CT radiomic features and clinical features can further improve the prediction accuracy.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Objective: To describe the current status of the prevalence of co-morbid myopia and obesity among 7-18 years students in the Inner Mongolia Autonomous Region in 2021 and to analyze the moderating effect of lifestyle in this association ，so as to provide scientific basis for the establishment of the mechanism of Co-morbidity，Shared Etiology，and Shared Prevention of common diseases in children and adolescents. Methods: A total of 139 630 primary and secondary school students aged 7-18 years from Inner Mongolia Autonomous Region were selected by stratified random cluster sampling method in September,2021. Myopia was determined using distance visual acuity examination and refractive error examination, and obesity was determined according to the BMI classification criteria for overweight, obesity screening of Chinese school age children and adolescents. Used a questionnaire, healthy lifestyles were determined according to the American Heart Association s Healthy Lifestyle Score by totaling the six scores for smoking, alcohol consumption, diet, exercise, screen time, and sleep duration.The χ 2 test was used to compare the association between group differences in the co-morbidity rate of myopia and obesity. The multivariable Logistic regression model was used to explore the influencing factors of the co-morbidity of myopia and obesity, and the stratified analysis was used to analyze the moderating effect of lifestyles on the prevalence of the co-morbidity. Results: The prevalence of myopia and obesity co-morbidity among students aged 7-18 years old in the Inner Mongolia Autonmous Region in 2021 was 13.7%, higher among boys than girls ( 15.5 % vs. 11.8%), higher among those aged 10-12 years old than 7-9,13-15,and 16-18 years old (14.7%,13.7%, 13.3%, 12.0%), higher among other ethnic minorities than Han Chinese and Mongolians (15.3%, 14.0%, 12.5%), higher in urban areas than that in suburban areas(15.3%, 13.0%), and middle economic level tracts were higher than poor and good tracts (14.8%, 12.9 %, 12.6%) ( χ 2=392.37，115.73，62.80，119.02，121.60, P <0.05). Multivariable Logistic regression modeling showed that unhealthy lifestyles ( OR=1.24, 95%CI=1.19-1.29 ) and middle level of lifestyle score ( OR=1.15, 95%CI=1.10-1.19 ) students had higher prevalence of co-morbidity, and the results were statistically significant among both boys and girls, the age groups of 10- 12, 13-15, and 16-18 years old, as well as the Han and Mongolian ethnic groups (all P <0.05). Conclusion In 2021, the current situation of myopia and obesity co-morbidity and unhealthy lifestyles among primary and secondary school students in the Inner Mongolia Autonomous Region are not optimistic.

Objective To observe the value of MRI in evaluation on the morphology and function of iliococcygeal muscles in fertile and nulliparous women.Methods Totally 50 healthy fertile women (fertile group,further divided into cesarean section subgroup and spontaneous delivery subgroup according to the mode of delivery) and 17 nulliparous healthy women (nulliparous group) underwent MR scanning in both natural and increased abdominal pressure state.Iliococcygeus thickness (ICT),coronal iliococcygeal angle (cICA) and sagittal iliococcygeal angle (sICA) of different states were measured and compared between the groups.Results In the natural state,the right and bilateral average sICA in the fertile group were larger than those in nulliparous group (both P＜0.05),while no statistical difference of ICT,right,left and bilateral average cICA and left sICA were found between two groups (all P＞0.05);the bilateral average sICA in spontaneous delivery subgroup was larger than that in cesarean section subgroup (P＜0.05).In increased abdominal pressure state,left,right sICA and bilateral average sICA in fertile group were larger than those in nulliparous group (all P＜0.05),while there was no statistical difference of ICT and cICA between two groups (all P＞0.05);no statistical difference of ICT,cICA nor sICA was found between spontaneous delivery subgroup and cesarean section subgroup (all P＞0.05).Conclusion MRI can accurately evaluate morphological and functional changes of iliococcygeal muscle in females.

Objective To evaluate the application of exponential apparent diffusion coefficient (eADC)value in differentiating benign from malignant thyroid nodules.Methods Routine MR sequences and axial diffusion weighted imaging (DWI)sequences with different b-values(0, 300,500,800 s/mm2)were performed in 46 patients with 51 histopathologically confirmed thyroid nodules,including 35 malignant nodules and 1 6 benign nodules.The eADC values of each thyroid nodules’solid component with different b-values were measured and assessed by independent samples t test.Receiver operating characteristic (ROC)curves were drawn and used to determine the diagnostic threshold and assess the screen test.Results The eADC values of the malignant nodules were higher than that of benign nodules (P<0.05)in all of the three different b-values.The eADC values of the malignant nodules and the benign nodules were 0.618±0.080 and 0.492±0.071 (b=300 s/mm2),0.520±0.104 and 0.371±0.077 (b=500 s/mm2)and 0.407±0.114 and 0.286±0.097 (b=800 s/mm2)respectively. According to the ROC curve,the area under the curve(AUC)was 0.883,0.890 and 0.824 when the b-value was set as 300,500 and 800 s/mm2respectively.When the b-value was set as 500 s/mm2and the diagnostic threshold was 0.454,the sensitivity,specificity, positive predictive value,negative predictive value and Youden index were 74.3%,93.8%,96.3%,60.9% and 0.68,respectively.Conclusion The eADC value is helpful in differentiating benign from malignant thyroid nodules,and the best result can be obtained by using DWI with b-value of 500 s/mm2.