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 clinical feasibility of predicting synchronous liver metastases based on MRI radiomics nomogram based on T2WI in rectal cancer. Methods The imaging and clinical data of 261 patients with primary rectal cancer admitted to Zhejiang People′s Hospital from April 2012 to May 2018 were retrospectively analyzed. 101 patients were accompanied by synchronous liver metastasis All cases were divided into training group (n=182) and verification group (n=79). T2WI image of each patient was selected to extract texture features by AK analysis software of GE company. A radiomics signature was constructed after reduction of dimension in training group by the least absolute shrinkage and selection operator (LASSO). Univariate logistic regression was used to select for independent clinical risk factors and multivariate logistic regression along with imaging omics tags were used to construct predictive models and nomogram. ROC was used to assess the accuracy of the nomogram in the training group and to verify them by the validation group. Finally, the clinical efficacy of each patient′s synchronized liver metastasis risk factor was calculated based on the nomogram. Results A total of 328 texture features were extracted from the T2WI. Seven most valuable features were selected after reducing the dimension by LASSO algorithm, including 3 co-occurrence matrices (GLCM) and 4 run-length matrices(RLM). Tumor staging and radiomic signatures were included in the Multifactor logistic regression to build the prediction model and nomogram. The accuracy of predicting SRLM was 0.862 and 0.844 in the training and the verification group, respectively. To evaluate the accuracy of the nomogram, radiomics signature and the tumor staging in all cases were 0.857, 0.832 and 0.663, respectively. There was no significant difference in the number of SRLM cases between the high risk group and the low risk group based on nomogram (P>0.05). Conclusion The radiomics nomogram based on T2WI can be used as a quantitative tool to predict synchronous liver metastases of rectal cancer.

Objective To observe the relationship between pilots' physiological data of anterior eye segments and flying time in order to provide a reference basis for better ophthalmological health care.Methods The ophthalmological data of the fighter pilots who were in the physical examination for transformation in General Hospital of Air Force or in annual examination but hospitalized due to non-ophthalmological reasons.Pilots were divided into ＜30 yr group (n=132) and ≥ 30 yr group (n=28).They were also divided into ＜1 000 h group (n=137) and ＞1 000 h group (n=23).Statistical analysis was done with SPSS 19.0 software.Results Pilots of different age groups had no statistic differences in visual acuity,axial length and mean corneal curvature (P＞0.05).The ratio of astigmatism with the rule and astigmatism against the rule between age groups also showed insignificant difference (x2 =0.013,P＞0.05).Pilots in ＜30 yr group had bigger spherical equivalent degree (t'=2.074,P＜0.05).The visual acuity,spherical equivalent degree,type of astigmatism,axial length,anterior chamber depth and mean corneal curvature were found no statistical difference between two age groups.No correlation existed between axial length in anterior chamber depth for the pilot in same group.A weak positive relationship was observed between visual acuity and spherical equivalent degree of the pilots in ＜30 yr group (r=0.287,P＜0.01) but in ≥ 30 yr group (P＞0.05).Axial length negatively correlated to mean corneal curvature in all pilots (r=-0.813).Conclusions Pilots' physiological data of anterior eye segments are mainly correlated with age but with flying time.

Objective To observe the relationship between pilots' physiological data of anterior eye segments and flying time in order to provide a reference basis for better ophthalmological health care.Methods The ophthalmological data of the fighter pilots who were in the physical examination for transformation in General Hospital of Air Force or in annual examination but hospitalized due to non-ophthalmological reasons.Pilots were divided into ＜30 yr group (n=132) and ≥ 30 yr group (n=28).They were also divided into ＜1 000 h group (n=137) and ＞1 000 h group (n=23).Statistical analysis was done with SPSS 19.0 software.Results Pilots of different age groups had no statistic differences in visual acuity,axial length and mean corneal curvature (P＞0.05).The ratio of astigmatism with the rule and astigmatism against the rule between age groups also showed insignificant difference (x2 =0.013,P＞0.05).Pilots in ＜30 yr group had bigger spherical equivalent degree (t'=2.074,P＜0.05).The visual acuity,spherical equivalent degree,type of astigmatism,axial length,anterior chamber depth and mean corneal curvature were found no statistical difference between two age groups.No correlation existed between axial length in anterior chamber depth for the pilot in same group.A weak positive relationship was observed between visual acuity and spherical equivalent degree of the pilots in ＜30 yr group (r=0.287,P＜0.01) but in ≥ 30 yr group (P＞0.05).Axial length negatively correlated to mean corneal curvature in all pilots (r=-0.813).Conclusions Pilots' physiological data of anterior eye segments are mainly correlated with age but with flying time.