Objective:To investigate the characteristics of thymus in patients with COVID-19, and to analyze the CT features and dynamic changes of thymus.Methods:Data of 241 patients diagnosed with COVID-19 admitted to Chongqing Three Gorges Central Hospital from Jan. to Mar. 2020 were retrospectively analyzed, and 242 consecutive subjects were selected as the control group from Nov. to Dec. 2019. The thymus classification, size, and average CT values between COVID-19 patients and the control group were compared, as well as those among different clinical types for COVID-19 patients, before and after treatment, were analyzed.Results:① The attenuation of the thymus: 64.7% (156/241) complete fatty replacement thymus, 17.8% (43/241) predominantly fatty thymus, 11.2% (27/241) approximately one-half fatty and one-half soft-tissue-attenuation thymus, and 6.2% (15/241) predominantly soft-tissue thymus in COVID-19 patients were found. 48.3% (117/242) complete fatty replacement thymus, 25.6% (62/242) predominantly fatty thymus, 10.3% (25/242) approximately one-half fatty and one-half soft-tissue-attenuation thymus, and 15.7% (38/242) predominantly soft-tissue thymus were found in the control group. Complete fatty replacement thymus was an independent factor affecting COVID-19 in 40 to 59 years old patients ( OR=3.071, P=0.000) . The rate of complete fatty replacement thymus: severe or critical type > common type > mild type. ② Size: There was no statistical difference of the thymus size between COVID-19 patients and the control group ( P>0.05) , no statistical difference among the mild type, common type and severe or critical type ( P>0.05) , no statistical difference between before and after treatment ( P>0.05) , and there was no correlation with treatment duration ( r=0.047, r=0.071) . ③ Density: There was no statistical difference of the CT value of thymus between COVID-19 patients and the control group ( P>0.05) , no statistical difference among the mild, common and severe type ( P>0.05) . One patient had a 17 HU increase in thymus density after treatment, but there was no statistical difference in 78 patients in thymus CT values between before and after treatment ( P>0.05) , and there was no correlation with treatment duration (r=0.013) . Conclusions:COVID-19 patients have a high rate of complete fatty replacement thymus. And the heavier the clinical classification, the higher the rate of complete fatty replacement thymus. Complete fatty replacement thymus is a risk factor for COVID-19 patients in 40 to 59 years old.

Objective To investigate the clinical value of radiomics nomogram,which is established by 18F-fluorodeoxyglucose (FDG) PET/CT radiomics signature combined with clinical-pathologic risk factors,in predicting the prognosis of patients with postoperative gastric carcinoma.Methods 18F-FDG PET/CT data of 207 patients (143 males,64 females,age range:20-85 years) with postoperative gastric carcinoma from January 2008 to August 2015 was reviewed retrospectively.Patients were divided into training group (n=104) and validation group (n =103),and the clinicopathologic information and disease-free survival (DFS) data were acquired.Significant textural features were selected from PET/CT images,and radiomics score (RS) for individual patient was calculated based on the radiomics signatures.The relationship between RS and DFS was analyzed.Cox regression analysis was performed to determine the risk factors ofDFS.The radiomics nomo-gram,obtained from combination of RS with clinicopathologic risk factors,was established and further evaluated in predictive value for recurrence or metastasis of postoperative gastric carcinoma,and the concordance index (C-index) was calculated.Results Cox regression analysis demonstrated that RS,tumor location,depth of invasion,lymph node metastasis,and distant metastasis were the significant risk factors for DFS (hazard ratios:2.148-2.828,all P＜0.05).The radiomics nomogram combined with RS and 4 clinicopathologic risk factors had a better prediction for the estimated DFS,comparing to RS alone.C-index of radiomics nomogram and RS were 0.830 and 0.700 in training group,and 0.776 and 0.681 in validation group,respectively.Conclusion Radiomics nomogram which is established by radiomics signatures and clinicopathologic risk factors may be better for predicting DFS of patients with postoperative gastric carcinoma.

Objective To evaluate the diagnostic performance of digital breast tomosynthesis (DBT) breast X-ray photography image texture characteristics based deep learning classification model on differentiating malignant masses. Methods Retrospectively collected 132 cases with simplex breast lesions (89 benign lesions and 43 malignant lesions) which were confirmed by pathology and DBT during January 2016 to December 2016 in Nanfang Hospital. DBT was performed before biopsy and surgery. Image of cranio-caudal view (CC) and medio-lateral oblique (MLO) were captured. The lesion area was segmented to acquire ROI by ITK-SNAP software. Then the processed images were input into MATLAB R2015b to establish a feature model for extracting texture features. The characteristics with high correlation was analyzed from Fisher score and one sample t test. We built up support vector machine (SVM) classification model based on extracted texture and added neural network model (CNN) for deep learning classification model. We randomly assigned collected cases into training group and validation group. The diagnosis of benign and malignant lesions were served as the reference. The efficiency was evaluated by ROC classification model. Result We extracted 82 texture characteristics from 132 images of leisure (132 images of CC and 132 images of MLO) by establishing deep learning classification model of breast lesions. We randomly chose and combined characteristics from 15 texture characteristics with statistical significance, then differentiated benign and malignant by SVM classification model. After 50 iterations on each combination of characteristics, the average diagnostic efficacy was compared to obtained the one with higher efficacy. Nine of CC and 8 of MLO was selected. The result showed that the sensitivity, specificity, accuracy and area under curve (AUC) of the model to differentiate simplex breast lesions for CC were 0.68, 0.77, 0.74 and 0.74, for MLO were 0.71, 0.71, 0.71 and 0.76. Conclusions MLO has better diagnostic performance for the diagnosis than CC. The deep learning classification model on breast lesions which was built upon DBT image texture characteristics on MLO could differentiate malignant masses effectively.

【Objective】 To evaluate the interference of anti-CD47 monoclonal antibody on transfusion compatibility detection, in order to establish methods for removing interference and evaluate its efficacy. 【Methods】 Blood samples from 8 patients in our clinical trial who were treated with anti-CD47 monoclonal antibody from Tianjing and Xinda were collected. ABO and Rh blood group antigen identification, direct anti-human globulin test, unexpected antibody screening test and cross-matching test were performed by ZZAP, Gamma-clone(an anti-globulin reagent lacking IgG4) and Immucor Capture-R solid phase agglutination kit. 【Results】 ABO blood group identification of 5 subjects were interfered after treatment with anti-CD47 monoclonal antibody. All 8 subjects showed 2+ to 4+ agglutination intensity on direct anti-human globulin test and 3+ to 4+ on unexpected antibody screening. The results of unexpected antibody screening by Gamma-clone and Immucor Capture-R solid phase agglutination kit were all negative, while the cross-matching test were compatible. Patients with anemia caused by CD47 monoclonal antibody treatment were transfused with 2 U suspension red blood cells, and the evaluation showed that the transfusion was effective. 【Conclusion】 The CD47 monoclonal antibody can interfere with transfusion compatibility detection, and the use of antiglobulin reagents lacking IgG4 and Immucor Capture-R solid phase agglutination kit can remove the interference, with good transfusion efficacy in patients.