Objective To explore the value of mammogram texture analysis in prediction of metastasis of axillary lymph nodes for breast carcinoma.Methods Mammograms and clinical data of 171 patients with breast carcinoma confirmed by pathology were retrospectively analyzed,and all patients underwent axillary lymph node dissection (ALND).Then the patients were divided into axillary lymph node metastasis group and non-metastasis group according to the result of ALND.The texture features of these lesions were statistically analyzed,including gray-level histogram texture parameters (mean value,standard deviation,skewness,kurtosis and variance) and gray-level co-occurrence matrix texture parameters (energy,entropy,correlation,inertia,inverse difference moment and contrast).Results In all of 171 breast cancer patients,96patients had axillary lymph node metastasis,while 75 patients had no metastasis.Mammograms showed negative axillary lymph nodes in 119 patients and positive axillary lymph nodes in 52 patients,and the sensitivity and specificity of mammograms in the diagnosis of positive axillary lymph nodes was 48.96％ (47/96) and 93.33％ (70/75),respectively.Mammogram texture analysis showed the values of energy,entropy,inverse difference moment and correlation in axillary lymph node metastasis group were higher than those in non-metastasis group,while the values of inertia and contrast in the axillary lymph node metastasis group were lower than those in non-metastasis group (all P＜0.05).The rest texture parameters had no significant differences between two groups (all P＞0.05).Area under curve (AUC) for texture parameters of energy,entropy,inertia,inverse difference moment,correlation and contrast was 0.610,0.610,0.374,0.599,0.612 and 0.421 (all P＜0.05),respectively.AUC of mammography,mammogram texture features,and the combination of mammography and texture features was 0.711,0.676 and 0.787 (all P＜0.05),respectively.The sensitivity and specificity of mammogram texture features,the combination of mammography and texture features in diagnosis of axillary lymph nodes metastasis was 62.5％ and 64.6％,66.7％ and 82.7％,respectively.Conclusion Mammogram texture parameters are helpful for predicting axillary lymph node metastasis,and the combination of mammography and texture features can improve diagnostic efficiency of axillary lymph node metastasis.

ObjectiveTo evaluate the proficiency and consistency of domestic and foreign testing institutions in the field of veterinary drug residue detection in food， and to promote international cooperation and mutual recognition of testing results among these institutions. MethodsA robust statistical analysis was conducted on the testing results of 20 laboratories in eight countries and regions across North America， Europe， and Asia. The laboratories’ testing capabilities were evaluated using Z-score comparison. ResultsAmong the 20 participating laboratories， 18 achieved satisfactory results， resulting in a satisfaction rate of 90%， while 2 laboratories （10%） failed to meet the requirements. The satisfaction rate of domestic laboratories （100%） was higher than that of foreign laboratories （81.8%）. ConclusionDomestic laboratories perform better than overseas laboratories in determining veterinary drug residues in food. To enhance testing capabilities， these overseas laboratories with unsatisfactory evaluation results should strengthen their daily quality control and ensure traceability of original records.

Objective To explore the classification of gliomas according to the theory and method of radiomics. Methods In this study, 161 pathologically confirmed glioma patients were retrospectively selected from 2012 to 2016 including 52 low-grade gliomas and 109 high-grade gliomas.Three hundred and forty-six quantization features were extracted from the MRI images, including shape, density, texture and wavelet imaging features. Mutual information and logistic regression model were used to select feature reduction and prediction model. The predictive ability of the model was validated using 10-fold cross-validation. Results Nineteen radiomics features were chosen from 346 quantization features. The sensitivity of the model was 96.3% (105/109), the specificity was 78.8% (41/52), the area under the curve (AUC) was 0.952 7, and the accuracy was 90.7%(146/161). Conclusion The solution proposed in this paper showed that radiomics can non-invasively and quickly provide an adjunct to the clinical grade of glioma with high accuracy.