Objective:To describe the distribution of fat-free mass (FFM) and fat-free mass index (FFMI) in children aged 3-17 years in China.Methods:Data were collected from National Nutrition and Health Systematic Survey in 0-18 years old children in China. By using multi-stage stratified randomized cluster sampling method, the project was conducted in 28 survey points in urban and rural areas in 14 provinces (autonomous regions and municipalities) in 7 regions in China from 2019 to 2021. FFM was measured using bioelectrical impedance meter. Finally, the body composition data of 70 853 children were included in the analysis. M ( Q1, Q3) was used to describe the gender and age specific FFM and FFMI of the children in different regions. Kruskal-Wallis H rank sum test was used to compare FFM and FFMI of boys and girls in same age group, boys in different age groups, girls in different age groups, as well as boys in same age group and girls in same age group in different regions. DSCF method was used for pairwise comparisons. Results:After the age of 11 years, the difference of FFMI between boys and girls increased year by year. The FFMI was 14.2 kg/m 2 in boys and 13.8 kg/m 2 in girls at 11 years old, the difference was significant ( χ2=135.86, P<0.001). The difference of FFMI between boys and girls exceed 1.0 kg/m 2 from 12 years old, and FFMI was 15.3 kg/m 2 in boys and 14.2 kg/m 2 in girls at 12 year old, the difference was significant ( χ2=597.27, P<0.001). The FFMI was 17.5 kg/m 2 in boys and 14.7 kg/m 2 in girls at 16 years old, the difference was significant ( χ2=2 543.60, P<0.001). The FFMI was higher in boys in northeast China, while the FFMI was lower in both boys and girls in northwest China. Conclusions:Gender specific difference was observed in the increase of FFMI with age. The FFMI was significantly higher in boys than in girls after 11 years old. It is necessary to pay attention to the problem of FFM in children in northeastern and northwestern China.

Objective:To investigate the distribution characteristics of body height, body weight and body mass index (BMI) in children aged 3-17 years in China.Methods:Data were obtained from the National Nutrition and Health Systematic Survey in 0-18 years old children in China. The study selected 70 853 children aged 3-17 years from 28 urban and rural survey sites in 14 provinces (autonomous regions and municipalities) in 7 regions of China with multi-stage stratified cluster random sampling. M ( Q1, Q3) was used to describe the region, age and gender specific body height, body weight and BMI in the children aged 3-17 years. Wilcoxon rank sum test was used to compare the body height, body weight, and BMI between boys and girls in same age group. Kruskal-Wallis H rank sum test was used to compare the body height, body weight and BMI among boys in different age groups and among girls in different age groups, as well as among boys in same age group and among girls in same age group from different regions, and DSCF method was used for further pairwise comparisons. Results:In this study, the median body height and body weight were 172.0 cm and 62.9 kg in 17-year-old boys and 160.0 cm and 53.7 kg in 17-year-old girls. The median for children's body height, body weight, and BMI in most age groups were higher in northeastern and northern China than in southern China, and the differences could be observed until age 17 years. The differences in body weight and BMI in children in northeastern and northern China were greater in Q3 than in Q1 compared with southern China. Conclusions:The body height of children aged 3-17 years continues to increase in China. Northeastern and northern China have more children with higher bodyweight, showing an obvious body weight increase trend, to which close attention needs to be paid.

Objective:To describe the distribution of fat mass (FM), fat mass percentage (FMP), and fat mass index (FMI) in children aged 3-17 years in China.Methods:Data of this study were from the National Nutrition and Health Systematic Survey in 0-18 years old children in China. A total of 70 853 children aged 3-17 years old selected from seven regions of China were included in this analysis. Body composition were measured by using bioelectrical impedance meter. The region, gender and age specific FM, FMP and FMI of the subjects were described by using M ( Q1, Q3). Kruskal-Wallis H rank sum test was used for the comparison of intergroup differences. DSCF method was used for pairwise comparisons. Results:The medians of FM, FMP and FMI were 3.0 kg, 18.3% and 2.9 kg/m 2 in boys aged 3 years and 2.9 kg, 19.0% and 2.9 kg/m 2 in girls aged 3 years, respectively. The FM increased with age and the FMP and FMI decreased with age in both boys and girls aged 3-5 years. After 11 years old, the FM, FMP and FMI decreased first and then increased in boys. From 6-17 years old, the FM, FMP and FMI increased gradually in girls. The FM, FMP and FMI were higher in girls than in boys after 12 years old (all P<0.05). The FM, FMP and FMI were relatively higher in children at the age of 6-14 in northeastern and northern China than in other regions. Conclusions:The age specific FM, FMP and FMI had different changing characteristics in boys and girls aged 3-17 years in seven regions of China. The FM, FMP and FMI also differed with region.

Objective:To analyze the relationship between body mass index (BMI) and fat mass percentage (FMP) in children aged 3-17 years in China.Methods:The BMI and FMP data of children aged 3-17 years from the National Nutrition and Health Systematic Survey in 0-18 years old children in China was analyzed. BMI- Z score/BMI and FMP were used to classify the subjects, respectively. Spearman correlation was used to analyze the correlation between BMI and FMP. The consistency between BMI- Z score/BMI and FMP in classifying the subjects was measured using Kappa coefficient. Results:The FMP of malnutrition, normal and overweight/obesity in boys was higher in age group 10-13-year than in other age groups (all P<0.001). The FMP of all nutritional status in girls increased with age (all P<0.05). The BMI of boys in all the FMP levels increased with age (all P<0.05). When the FMP of girls was 25%- or ≥30%, BMI increased with age (all P<0.001). The relationship between BMI and FMP was strong ( r=0.705, P<0.001), with r of 0.618 in boys and 0.884 in girls. The consistency between BMI- Z score/BMI and FMP in classifying the subjects was found to be moderate (Kappa=0.574, P<0.001). Conclusions:There was a strong relationship between BMI and FMP. The consistency between BMI- Z score/BMI and FMP in classifying the subjects was moderate.

Objective:To evaluate the consistency between bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) in the measurement of body composition in children aged 6-17 years in China.Methods:Fat-free mass, fat mass and fat mass percentage were measured by both BIA and DXA in 1 161 children. t-test or Wilcoxon paired test was used to evaluate the different outcome of the two methods. The correlation and consistency between the methods were evaluated by Spearman correlation coefficients ( r) and Bland-Altman analysis. Results:Body compositions measured by BIA was positively correlated with those measured by DXA (fat mass r=0.95, fat-free mass r=0.98, fat mass percentage r=0.86, all P<0.05). Comparing with DXA, BIA underestimate children's fat mass [the mean difference is -3.15 kg, and the SD is 2.35 kg, 95% limits of agreement (LoA): -7.74-1.45 kg] and fat mass percentage (the mean difference is -8.45%, and the SD is 4.63%, 95% LoA: -17.53%-0.64%). Conclusions:Body compositions measured by BIA was highly positively correlated with those measured by DXA. BIA has certain application value in the measurement of body fat mass and fat-free mass of children aged 6-17 years.

Objective:To investigate the correlation between three-dimensional histogram analysis of dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and Gleason score(GS)in prostate cancer(Pca)from two hospital, and its diagnostic efficacy for discriminating low-grade from high-grade Pca.Methods:A total of 102 pathologically confirmed Pca patients in the First Affiliated Hospital of Zhejiang Chinese Medical University and Hangzhou Traditional Chinese Medical Hospital(TCM Hospital)Affiliated to Zhejiang Chinese Medical University from January 2017 to October 2020 were retrospectively analyzed.The quantitative parameters of Pca, including transport constant(K trans), rate constant(K ep), percent volume of the extravascular extracellular space(V e)and fraction of the Intraplasmic contrast volume(V p), were obtained by manually layer by layer delineating of interested regions of all lesions on the original DCE-MRI imaging.Then the three-dimensional histogram analysis of the above parameters were performed to obtain the minimum, maximum, median, mean, area, 10 thpercentile, 25 thpercentile, 75 thpercentile and 90 thpercentile.The correlations between quantitative parameters and GS, and diagnostic efficiencies were analyzed. Results:102 Pca patients were divided into low-grade prostate cancer group(GS≤3+ 4)(n=44)and high-grade Pca group(GS≥4+ 3)(n=58). There were no statistically significant differences in age and location of lesions between the two groups( P>0.05), but there were statistically significant differences in Gleason score, PSA level and lesion diameter between the two groups( U=0.000, 730.000, 711.000, all P<0.05). The median, mean, 10 thpercentile, 25 thpercentile, 75 thpercentile, 90 thpercentile derived from K trans, and K ep(median, mean, 10%, 25%, 75%, 90%)together with maximum of K transand mean for V e were positively correlated with GS( r=0.405 to 0.583, P<0.05), in which mean of K transhad the highest positive correlation( r=0.583, P=0.000). The histogram parameters derived from V pwere negatively correlated with GS( r=-0.301 to 0.341, P<0.05). The area under ROC of 75th percentile derived from K transwas the highest(0.832). When the cut-off value of 75 thpercentile derived from K transwas ≥0.680/min, its Youden index, sensitivity, and specificity were 0.594, 0.776, 0.818, respectively. Conclusions:The three-dimensional histogram of DCE-MRI quantitative parameters has correlation with GS in Pca patients, can be used to discriminate low-grade from high-grade Pca.

Objective:To investigate the value of radiomics based on unenhanced CT texture analysis in predicting the WHO/International Society of Urological Pathology (ISUP) grading of clear cell renal cell carcinoma (ccRCC).Methods:Postoperative pathology-confirmed ccRCC subjects ( n=90) who received CT scanning and had a definite pathological grading in Cancer Hospital of the University of Chinese Academy of Sciences were collected retrospectively from December 2016 to May 2019. The cases were randomly divided into training group ( n=63) and test group ( n=27) as a ratio of 7∶3. All cases were classified into low grade (grades Ⅰ and Ⅱ, n=57) and high grade (grades Ⅲ and Ⅳ, n=37) according to the new pathological grading (WHO/ISUP grading, version 2016) of renal carcinoma. 3D-ROI segmentation was performed on unenhanced CT images and 93 texture features were extracted. The least absolute shrinkage and selection operator (LASSO) regression was used to reduct dimension of texture parameters and then the radiomics score (Rad-score) was established. The logistic regression was used to develop the prediction model with the pathological grading as the gold standard. The ROC curve and calibration curve were used to evaluate the predictive performance of the model, and the area under the curve (AUC), accuracy, sensitivity and specificity were calculated. The Hosmer-Lemeshow test was used to evaluate calibration degree of the model. Results:The 10 non-zero coefficient texture features were screened out through dimension reduction steps. The Rad-score was formed according to the linear combination of these ten features and corresponding coefficients, and then the prediction model was developed. The AUC of the model in training group was 0.933 (95%CI 0.862-1.000), the sensitivity was 92.3%, the specificity was 89.2%, and the model accuracy was 90.5%. The calibration curve showed the good calibration ( P=0.257). The AUC value in test group was 0.875 (95%CI 0.734-1.000), the sensitivity, specificity and accuracy were 72.7%, 87.5% and 81.5%. The calibration curve showed the good calibration ( P=0.125). Conclusion:The radiomics prediction model based on unenhanced CT texture analysis have application potential for the evaluation of WHO/ISUP grading of ccRCC.

Objective:To compare the preliminary clinical effect of mitral valve replacement and mitral valvuloplasty on hypertrophic obstructive cardiomyopathy with mitral regurgitation.Methods:From January 2010 to December 2013, the patients undergoing cardiac surgery at Bakulev Cardiovascular Surgery Research Center in Russia were randomly divided into two groups: Forty-one patients received left ventricular outflow tract hypertrophy myocardial resection (Morrow operation) combined with mitral valve replacement (MVR) as MVR group; Forty-seven patients received Morrow surgery combined with mitral valve repair (MVr) as MVr group.The primary end point: death, secondary end point: thrombosis complications (cerebral infarction, peripheral arterial embolism), recurrence of mitral regurgitation and left ventricular outflow tract pressure difference were compared between the two groups.Results:In the MVr group, 6 cases were converted to MVR and were excluded from the study.The survival rates of MVR group and MVR group were 78.9% and 96.6%, respectively , and the thromboembolic free survival rates of MVR group and MVr group were 83.2% and 100%, respectively. The differences were statistically significant( P=0.034, 0.026, respectively). There was no significant difference in mitral regurgitation and left ventricular outflow tract pressure difference between MVR group and MVR group 24 months after operation( P=1.000, 0.934, respectively). Conclusion:Operation combined with MVR or MVr is an effective method to relieve left ventricular outflow tract obstruction and mitral regurgitation. Morrow operation combined with MVr can improve survival rate and reduce thrombosis complications.

Objective: To explore the risk factors of predicting white matter hyperintensities progression based on radiomics of MRI of whole-brain white matter. Methods: The imaging and clinical data of 152 patients with white matter hyperintensities admitted to Zhejiang People′s Hospital from March 2014 to October 2018 were retrospectively analyzed. The whole brain white matter on baseline T₁WI images of each patient were segmented by SPM12 software package, and images of white matter were imported into AK software for texture feature extraction and dimensionality reduction. At last, least absolute shrinkage and selection operator(LASSO) was used to calculate the score of radiomics signature of each patient. According to the improved Fazekas scale, patients with WMH progression were divided into three groups: any white matter hyperintensities (AWMH), periventricular white matter hyperintensities (PWMH) and deep white matter hyperintensities (DWMH). Statistical differences of clinical factors and radiomics signature between WMH progression subgroups and non-progression subgroups were compared with independent sample t test or Mann-Whitney U test, and Univariate logistic regression was used to select independent clinical risk factors and multivariate logistic regression along with imaging omics tags were used to construct predictive models, which was evaluated by ROC curve. And the correlation between the clinical indicators of independent risk factors and the texture feature of radiomics signature was analyzed. Results: The efficacy of the model for the detection for AWMH progression, PWMH progression and DWMH progression was 0.818,0.778 and 0.824, respectively. Age is an independent risk factor for AWMH progression and DWMH progression[OR(95%CI), 4.776(2.152-10.601) vs. 3.851(1.101-8.245); P<0.05]. BMI is an independent risk factor for DWMH[OR(95%CI), 3.004(1.204-7.370); P<0.05], and hyperlipidemia is an independent risk factor for AWMH and PWMH[OR(95%CI), 4.062(1.834-8.998) vs. 3.549(1.666-7.563); P<0.05]. In AWMH subgroup, Surface Area was negatively correlated with age and low density lipoprotein(LDL) (r=-0.401, -0.312), and Inverse Difference Moment_ALLDirection_offset 1_SD was negatively correlated with age(r=-0.412). In PWMH subgroup, Compactness 1 was negatively correlated with LDL(r=-0.198), and Inverse Difference Moment_angle 0_offset 7 was positively correlated with LDL(r=0.252). In DWMH subgroup, LongRunEmphasis_ALLDirection_offset 7 was negatively correlated with age(r=-0.322), and GLCMEntropy_angle0_offset 4 was negatively correlated with age(r=-0.278). GLCMEntropy_AllDirection_offset4 was negatively correlated with body mass index(r=-0.514). Conclusion Radiomics based on whole-brain white matter MR imaging can predict WMH progression and identify the risk factors in high-risk populations, thus providing early additional information to conventional magnetic resonance imaging to predict WMH progression.

Objective: To investigate the prognostic value of radiomics analysis in predicting axillary lymph nodes (ALN) metastasis of breast cancer based on dynamic contrast-enhanced MR imaging (DCE-MRI). Methods: One hundred and ninety-six patients with suspected breast cancer were prospectively collected for dynamic breast DCE-MRI. Enhanced MR imaging data of 72 axillary lymph nodes were evaluated separately by a chief radiologist and a resident, and the consistency analysis was performed. Lymph nodes were dichotomized according to the pathology results derived from operation or biopsy under real-time virtual sonography based on MRI data. Clinical and imaging data were also divided into corresponding groups. (Imaging) Data from both groups were respectively classified as training set and testing set by stratified sampling in proportion with 3∶1. AK software was applied to extract 6 major categories of 385 features (including histogram, morphology, texture parameters, gray level co-occurrence matrix, run-length matrix and grey level zone size matrix from imaging), and a set of statistically significant features were subsequently obtained by dimension reduction. The prediction model was established through binary classification logistic regression and employed to externally test the validation set by the method of confusion matrix. Meanwhile, ROC analysis was applied to assess the diagnostic performance of the model. Results: Of the 72 axillary lymph nodes, 35 were metastatic negative and 37 were positive. The consistency of enhanced MRI radiomics features was good, between 0.841 and 0.980. Uniformity, ClusterProminence_AllDirection_offset1_SD, Correlation_AllDirection_offset1, LongRunEmphasis_angle90_offset7 and SurfaceVolumeRatio were statistically significant differences (P<0.01), the area under the ROC between 0.747 and 0.931. In the training and testing group, the areas under the ROC, sensitivity, specificity and accuracy of the model were 0.953, 0.893, 0.926, 92.6% (50/54) and 0.944, 0.900, 1.000, 88.9% (16/18) respectively. Conclusion The prediction model based on radiomic features may provide a non-invasive and effective approach to the assessment of the risk of ALN metastasis of breast cancer.