Objective: To explore growth and intelligence development of low birth weight infants (LBWI) at 24 and 36 months of age, so as to provide reference for early monitoring and intervention of the development of LBWI. Methods: A total of 100 LBWI born and managed in Hefei Maternal and Child Health Care Institution were selected from 2012 October 1 to 2015 December 30, and 99 normal birth weight infants (NBWI) under child health management in the same sitinstitution were selected as controls. According a prospective cohort study method, and based on the establishment of a cohort and monitoring of childhood growth and development, a unified method was used to longitudinally follow up and observe the physical fitness of two groups of infants at the determined time points. The development of LBWI and NBWI at 24 and 36 months of age was surveyed using the Gesell Development Scale. Results: Weight, length and head circumference of LBWI children at the age of 15-36 months were significantly lower than those of NBWI children ( P <0.05). In addition, 117 children (43.98%) completed the full assessment of intelligent development scale, including 62 LBWI and 55 NBWI. The scores of Gesell in NBWI group was higher than that in LBWI group at 24 and 36 months of age, including adaptability, gross motor, fine metor skills, language and personal social functions ( t =-4.17, -3.82, -3.21 , -3.03, -2.61; -4.23, -3.16, -3.07, -3.13, -3.99, P <0.05). Multivariate linear regression analysis found that birth weight was positively correlated with adaptability, gross motor, fine motor skills, language functions at 24 and 36 months of age and personal social function at 36 months of age ( β =0.004, 0.010; 0.003, 0.008; 0.003, 0.007; 0.004, 0.009; 0.011, P ＜0.05). Conclusion The growth and development of LBWI children are significantly delayed compared to NBWI children. The scores of LBWI children are lower than those of NBWI children in all functional areas. Weight is the main factor affecting children s intellectual development. Early monitoring and intervention of low birth weight infants should be carried out to avoid or mitigate adverse consequences.

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.

Enteral nutrition plays an irreplaceable role in the nutritional treatment of critically ill patients. In order to help clinical medical staff to manage the common complications during the implementations of enteral nutrition for critically ill patients, the consensus writing team carried out literature retrieval, literature quality evaluation, evidence synthesis. Several topics such as diarrhea, aspiration, high gastric residual volume, abdominal distension, etc. were assessed by evidence-based methodology and Delphi method. After two rounds of expert investigations, Expert consensus on prevention and management of enteral nutrition therapy complications for critically ill patients in China (2021 edition) developed, and provided guidance for clinical medical staff.

Objective:To examine the efficacy of systematic family intervention (a parent-implemented early start Denver model, P-ESDM) on toddlers at high risk of autism spectrum disorders (IHRASD) who were aged 1 to 2.Methods:The developmental screening for infants aged 1-2 years in Hefei city was performed by using the standardized screening method.The monitoring network of referral-assessment-P-ESDM guidance-follow up-early intervention effect assessment was conducted on the screened children with positive results.A total of 110 patients with IHRASD aged 1 to 2 years were detected.Sixty-three cases that met the inclusion criteria and volunteered to take part in this study were divided P-ESDM group (31 cases) and the control group (32 cases). They were assessed before intervention, 3 and 6 months after intervention separately.Parents of the patients in the P-ESDM group attended 12 weeks of family intervention training courses, 1 hour per week, while parents of the patients in the control group rejected interventions available from us.Results:The proportion of fathers with college degree or above (71.0% vs.43.8%, χ2=7.315, P=0.026) and proportion of mothers with high school/secondary school degree or above (83.9% vs.65.6%, χ2=5.264, P=0.072) were significantly higher in the P-ESDM group than in the control group.Three months after intervention, the P-ESDM group showed decreased Aberrant Behavior Checklist (ABC) scores [29.0(20.0, 45.0) scores vs.48.0(33.0, 50.0) scores, Z=-2.298, P=0.022]and increased Infant-Junior Middle School Social Adaptive Capacity Scale (SM) scores[ 10.0(9.0, 10.0) scores vs.9.0(8.0, 10.0) scores, Z= -2.045, P=0.041], compared with the control group.No significant improvement was found by the Gesell tests in the development quotients (EQs) of five energy areas, namely, the adaptive energy area [(83.86±18.03) scores vs.(75.34±10.49) scores, t=1.734, P=0.090], big movement energy area [(90.24±10.79) scores vs.(85.20±8.97) scores, t=1.595, P=0.118], fine movement energy area [(85.18±14.99) scores vs.(83.41 ± 9.28) scores, t=0.429, P=0.670], language energy area [(59.28±15.01) scores vs.(51.09±9.37) scores, t=1.981, P=0.054] and individual-society energy area [(67.13±14.86) scores vs.(63.50±7.85) scores, t=0.908, P=0.369]. Twelve months after intervention, the P-ESDM group demonstrated significantly decreased ABC scores[20.0(12.0, 33.0) scores vs.45.0(32.3, 52.8) scores, Z=-3.783, P=0.000], increased SM scores[10.0(9.0, 10.0) scores vs.9.0(8.3, 10.0) scores, Z=-2.974, P=0.003], increased EQ of adaptive energy area [(80.83±17.20) scores vs.(72.34 ± 13.18) scores, t=2.203, P= 0.031], increased EQ of the individual-society energy area[(71.87±17.30) scores vs.(62.18±13.91) scores, t= 2.454, P= 0.017]and increased EQ of the language energy area[(68.96±19.93) scores vs.(53.42±14.88) scores, t= 3.515, P= 0.001], compared with the control groups. Conclusions:Early screening, diagnosis, and P-ESDM intervention can improve the outcome of young children aged 1 to 2 years at high risk of ASD.P-ESDM intervention for 12 months demonstrates obvious effects in patients with IHRASD.

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.

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 explore the value of the minimum apparent diffusion coefficient (ADC-min) value in the diagnosis of ductal carcinoma in situ (DCIS) and invasive cancer of breast.Methods One hundred and forty nine cases of breast cancer verified by histopathology were included in this retrospective study.All the patients underwent dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI) before the biopsy.The ADC min value and its correlation with invasive ductal carcinoma(IDC),DCIS and IDC-DCIS were analyzed.Results The mean ADC-min values for IDC,IDC-DCIS and DCIS were (0.95±0.16)×10-3 mm2/s,(1.07±0.13)×10-3 mm2/s and (1.24±0.18)×10-3 mm2/s,respectively.The ADC-min value of the three groups showed an increasing trend and there were significant differences (F=32.08,P＜0.01).The optimal cutoff ADC-min value was 1.02 × 10-3 mm2/s to differentiate DCIS from invasive cancer with a sensitivity of 95.0％ and a specificity of 63.6％.Conclusion The ADC min values are significantly different among IDC,IDC-DCIS and DCIS.It may be used as a reliable tool to differentiate DCIS and invasive cancer of breast.

Objective To explore the value of CT radiomics model in predicting three-year survival time in patients with primary hepatocellular carcinoma (HCC). Methods Eighty one patients pathologically or clinically confirmed HCC and B stageof Barcelona clinical liver cancer before transcatheter arterial chemoembolization (TACE) in Zhejiang Cancer Hospitalwere retrospectively enrolled from January 2010 to June 2014.A primary cohort consisted of 64 patients and an independent validation cohort consisted of 17 patients. The patients were divided into survival group of 39 cases and death groupof 42 cases duringthree-year follow-up. All the patients underwentnon-enhanced and contrast-enhanced CTimages scan before TACE. Three hundered and seventy six quantization radiomics features were extracted from the arterial phase and portal phase CTimages of target lesion. LASSO regression model was used for data dimension reduction. Logistic regression was used to develop the prediction model. The predictive ability of the model was validated using the area under the curve (AUC) of receiver operating characteristic(ROC) analysis. Results The radiomics features selected from the arterial and portal phase were 8 and 5, respectively. The arterial prediction model showed AUC=0.833, sensitivity=83.9％(26/31), specificity=81.8％(27/33), accuracy=82.8％(53/64)in primary datasetand AUC=0.861, sensitivity=75.0％(6/8), specificity=100.0％(9/9), accuracy=88.2％(15/17)in independent validation dataset.The portal prediction model showed AUC=0.858, sensitivity=83.3％(25/30), specificity=85.3％(29/34), accuracy=84.4％(54/64)in primary dataset and AUC=0.750, sensitivity=75.0％(6/8), specificity=100.0％(9/9), accuracy=88.2(15/17)in independent validation dataset. Conclusion This study shows CT radiomics model can be conveniently used to facilitate the preoperative individualized prediction of three-year survival time in patients with HCC.

Transcatheter arterial chemoembolization (TACE) has already been a mature and an effective treatment for advanced hepatocellular carcinoma (HCC).Clinically,it is very important to quickly and accurately evaluate the postoperative curative effect with minimally invasive technique so as to determine the next treatment options.At present,postoperative conventional CT and MRI are the main means to assess the curative effect of TACE,but it is a pity that after the treatment the functional changes of the tumor occur earlier than the morphological changes.In recent years,functional MRI techniques,such as diffusionweighted imaging (DWI),multi-b value DWI,dynamic contrast-enhanced (DCE) imaging,etc.have been more and more used for quantitative evaluation of the diffusion of water molecules and the blood microcirculation perfusion within the tumor tissue,and some progresses have been achieved in the evaluation of curative efficacy for tumor.This paper aims to make a comprehensive review about the research progress of the above mentioned functional imaging methods as well as their current application status in evaluation of the curative effect of TACE.

Objective To discuss the application of routine CT three-phase perfusion parameter,that is arterial enhancement fraction (AEF) value,in evaluating the curative effect of transcatheter arterial chemoembolization (TACE) for hepatocellular carcinoma (HCC).Methods The clinical data of a total of 30 patients with pathologically proved HCC were enrolled in this study.Routine CT three-phase perfusion scan was performed 1-3 days before as well as 30-40 days after TACE in all patients.AEF value was calculated by using CT Kinetics software (GE Healthcare).The formula for calculating AEF value was as follows:AEF value=(arterial phase CT value-plain scan CT value)÷(portal phase CT value-plain scan CT value).The results were statistically analyzed.Results Effective treatment group had 17 patients,and ineffective treatment group had 13 patients.The postoperative AEF values in the effective treatment group and the ineffective treatment group were (0.351±0.090) and (0.438±0.050) respectively,the difference between the two groups was statistically significant (P＜0.05).Taking postoperative AEF value of 0.392 as the critical value to predict the postoperative effect of TACE,the sensitivity and specificity were 86.7％ and 73.2％ respectively,and the area under the curve was 0.876 (P＜0.001).Conclusion The routine CT three-phase perfusion parameter (AEF) can quantitatively reflect the hemodynamic changes of HCC after TACE,which is helpful for making early evaluation of TACE effect,meanwhile,no additional radiation dose will be added.