Objective: To explore the application value of free-breathing Star volumetric interpolated breath-hold examination (StarVIBE) sequence for acquisition of chest T1WI. Methods: Data of 37 patients with chest lesions detected with CT were collected. The patients were divided into unable-to-hold-breath group (n=17) and able-to-hold-breath group(n=20) according to whether they could hold breath for 20 s or not. Then all patients underwent plain and delayed phase enhanced StarVIBE sequence and conventional VIBE sequence MR examinations. Imaging qualities were evaluated using "5-point scale" in terms of lesion conspicuity, artifact, pulmonary vessels sharpness, mediastinal structures conspicuity and overall quality, and then were compared between sequences and groups, respectively. Results: In unable-to-hold-breath group, the scores of StarVIBE sequence were higher than those of conventional VIBE sequence on both plain and enhanced images (all P<0.05). In able-to-hold-breath group, plain images of StarVIBE sequence had lower scores of lesion conspicuity and pulmonary vessels sharpness than those of conventional VIBE sequence (both P<0.05), while there was no significant difference of the above scores on enhanced images (all P>0.05), and the scores of artifact and the overall image quality were not statistically different between two sequences (all P>0.05). However, in able-to-hold-breath group, StarVIBE sequence demonstrated higher score of pulmonary vessels sharpness on both plain and enhanced images (P<0.05). Conclusion: For acquisition of chest T1WI, using free-breathing StarVIBE sequence can achieve similar image quality to conventional VIBE sequence patients being able to breath-holding. For patients being unable to hold breath, relative good image quality could be obtained using free-breathing StarVIBE sequence.

Objective: To investigate the value of CT radiomics for preoperative prediction of gastric cancer lymphovascular invasion. Methods: Totally 181 patients with gastric cancer confirmed by surgical pathology were retrospectively collected and randomly divided into training set (n=120) and verification set (n=61). Firstly, the tumor area was delineated and segmented, and the radiomics features were extracted based on enhanced CT venous phase images. Then, the training set was used to screen features associated with lymphovascular invasion, and a radiomics signature was built. Finally, the model was validated based on the verification set, and ROC curve and calibration curve were used to assess the model's predictive power and fit assessment. Results: Seven radiomics features most relevant to lymphovascular invasion of gastric cancer were extracted and used to build the radiomics signature. The AUC of the training set was 0.742 (P=0.001, 95%CI [0.652, 0.831]), of the verification set was 0.727 (P=0.002, 95%CI [0.593, 0.853]). The optimal threshold based on the training set was 0.422. The accuracy, sensitivity and specificity of the model in the training set was 0.708, 0.586 and 0.806, respectively. This threshold was used for the verification set with accuracy, sensitivity, and specificity of 0.689, 0.519 and 0.824, respectively. The calibration curve showed that the radiomics signature had a good fit in both the training set and the verification set (both P>0.05). Conclusion: CT radiomics can be used as a novel non-invasive imaging method for preoperatively predicting lymphovascular invasion in gastric cancer.

Objective The aim of this study was to investi-gate the changes of brain function in patients with drug-naive idiopath-ic epilepsy ( DNIE ) using resting-state functional MRI ( rs-fMRI ) amplitude of low-frequency fluctuation ( ALFF) , analyze the correlation of abnormal brain regions with the clinical variable ( disease course) , and gain a deeper insight into the pathophysiological mechanisms of idiopathic epilepsy. Methods This study included 25 cases of DNIE (15 males and 10 females) and 34 cases of drug idiopathic epilepsy (DIE, 22 males and 12 females).Another 25 healthy volunteers matched with the DNIE patients in sex, age, education and handedness were recruited as normal controls.The rs-fMRI data obtained from all the subjects were processed, subjected to ALFF analysis, and compared among the DNIE, DIE, and nor-mal control groups.The correlation was evaluated between the ALFF statistical brain mapping and the course of disease. Results Obvious differences were found in ALFF among the DNIE, DIE and control subjects.Compared with the normal controls, the DNIE pa-tients showed increased ALFF in the right inferior temporal gyrus, right lingual gyrus and right cuneus, but decreased ALFF in the right insula, left hippocampus, right midbrain, right middle frontal gyrus, left anterior cingulated gyrus, left middle cingulate gyrus and right inferior parietal lobule.In comparison with the DIE patients, those of the DNIE group exhibited increased ALFF in the left inferior occipital gyrus, right middle occipital gyrus and left middle occipital gyrus, but decreased ALFF in the right inferior frontal gyrus, left insula, right superior temporal gyrus and right middle frontal gyrus.In the DNIE patients, the disease course was found to be correlated positively with ALFF in the right cerebellum posterior lobe, left cerebellar tonsil, right lingual gyrus, left orbital gyrus, left middle oc-cipital gyrus, left corpus callosum, left caudate nuclear, left superior frontal gyrus, left medial frontal gyrus, right precuneus and left middle frontal gyrus, but negatively with ALFF in the right parahippocampal, right superior temporal gyrus, left superior temporal gyrus and right post-central gyrus. Conclusion The ALFF of resting-state cerebral function is abnormal in DNIE patients.The correlation between ALFF and the clinical variable ( disease course) provides a new insight into the pathophysiological mechanisms of epilepsy.

Objective To explore the effect of CT slice thickness on the reproducibility of radiomic features in urinary stones.Methods A retrospective analysis was performed on 30 cases of stones diagnosed by urinary CT.Thin-slice(1 mm)images were reconstructed into two groups of images with thicknesses of 2 mm and 5 mm.Two radiologists conducted manual segmentation independently and then extracted features.The reproducibility of the radiomic features was calculated using the concordance correlation coefficient(CCC).Results Under different slice thickness conditions by the same radiologist,the average CCC of all groups was less than 0.85(P＜0.05).For radiologist A,the proportion of stone features with CCC＜0.85 were 50.13％,79.91％,and 82.38％in the 1 mm vs 2 mm group,2 mm vs 5 mm group,and 1 mm vs 5 mm group,respectively.Corresponding values for radiologist B were 44.55％,79.47％and 82.32％,respectively.Among the seven categories of radiomic features,the morphological features with CCC＜0.85 was 100％in the 1 mm vs 5 mm group.Conclusion CT slice thickness significantly affected the reproducibility of radiomic features in urinary stones,with the greatest effect observed in morphological features.Thin-slice imaging demonstrated more stable reproducibility of stone characteristics.

Objective To observe the value of preoperative CT radiomics models for predicting composition of in vivo urinary calculi.Methods Totally 543 urolithiasis patients were retrospectively enrolled and divided into calcium oxalate monohydrate stone group(group A,n=373),anhydrous uric acid stone group(group B,n=86),carbonate apatite group(group C,n=30),ammonium urate stone group(group D,n=28)and ammonium magnesium phosphate hexahydrate stone group(group E,n=26)according to the composition of calculi,also divided into training set and test set at the ratio of 7:3.Radiomics features were extracted and screened based on plain CT images of urinary system.Five binary task models(model A-E corresponding to group A-E)and a quinary task model were constructed using least absolute shrinkage and selection operator algorithm for predicting the composition of calculi in vivo.Then receiver operating characteristic curves were drawn,and the area under the curves(AUC)were calculated to evaluate the predictive efficacy of binary task models,while the accuracy,precision,recall and F1 score were used to evaluate the predictive efficacy of the quinary task model.Results All binary task models had good efficacy for predicting the composition of urinary calculi in vivo,with AUC of 0.860-0.948 in training set and of 0.856-0.933 in test set.The accuracy,precision,recall and F1 score of the quinary task model for predicting the composition of in vivo urinary calculi was 82.25％,83.79％,46.23％and 0.596 in training set,respectively,while was 80.63％,75.26％,43.48％and 0.551 in test set,respectively.Conclusion Binary task radiomics models based on preoperative plain CT had good efficacy for predicting the composition of in vivo urinary calculi,while the quinary task radiomics model had high accuracy but relatively poor stability.

Objective     To analyze the clinical information of COVID-19 patients of Shanghai National Exhibition and Convention Center cabin hospital, and to explore the medical management strategy to provide thoughtful suggestions for other cabin hospitals and governments as valuable references. Methods     The clinical data of 174 308 patients confirmed COVID-19 in Shanghai National Exhibition and Convention Center cabin hospital from April 9 to May 31, 2022 were retrospectively reviewed. There were 103 539 male and 70 769 female patients, with an average age of 41.50±15.30 years. Medical and nursing management strategy was summarized. Results     Among the 174 308 patients, 71.5% (124 630 patients) were asymptomatic. The vaccination rate of patients with COVID-19 in the cabin hospital was 76.5% (133 338 patients), and the majority of none vaccinated patients were children under the age of 10 years and the elderly over the age of 60 years, the vaccination rate of whom was only 25.0% (1 322 patients) and 63.9% (13 715 patients), respectively. In addition, the proportion of mild symptom type in the patients not vaccinated was significantly higher than that in the vaccinated patients (P≤0.01). The average hospitalization time of patients in cabin hospital was 7.39±0.53 days, which was 7.01±2.12 days for patients under 60 years and 8.21±0.82 days for patients over 60 years. The hospitalization time of elderly patients was significantly longer (P≤0.01), and the hospitalization time of elderly patients at age over 60 years without vaccination was 8.94±1.71 days, which was significantly longer than the average hospitalization time and the time of elderly patients vaccinated (P≤0.01). The number of patients combined with basic diseases was 27 864 (16.0%), of which cardiovascular diseases accounted for 81.3% (22 653 patients). A total of 2 085 patients were transferred and treated in designated hospitals. Conclusion     Large scale cabin hospitals are helpful to cut off the source of infection. Attention shall be paid to the sorting of admission and timely transfer to other hospital during the patients management. Most of the patients have a good prognosis after treatment. The vaccination of key population and community-based screening will be the next step of focus.