Objective To compare the initial detection rate and the accuracy of qualitative diagnosis of multislice spiral computed tomography (MSCT),gastrointestinal tract radiography,gastroscopy,and endoscopic ultrasonography in the diagnosis of gastric lipoma,with a focus on evaluating the diagnostic value of MSCT.Methods Twenty-six patients with gastric lipoma,6 males and 20 females with a mean age of 61 years (ranging from 41 to 82 years) and confirmed by pathology,were enrolled in the present study.Their clinical,pathologic and imaging findings were retrospectively analyzed.All the patients underwent gastroscopy,and plain and dynamic enhanced MDCT scans.Of them 21 cases underwent endoscopic ultrasonography and 12 cases gastrointestinal tract radiography.Results The detection rate was 88.5％(23/26) and 80.8％(21/26),P＞0.05 for MSCT and gastroscopy,respectively,in the initial diagnosis of gastric lipomas,both higher than that of gastrointestinal tract radiography (41.7％,5/12).The accuracy of qualitative diagnosis of MSCT (100％,23/23) was higher than that of endoscopic ultrasonography (71.4％,15/21),gastrointestinal tract radiography (0％,0/5) and gastroscopy (0％,0/21).The lesions located at the gastric antrum in 18 cases (69.2％,including 10 front wall,6 posterior wall and 2 pyloric canal),the gastric body in 7 cases (26.9％) and the gastric fundus in 1 case (3.8％).All the lipomas presented as round or ovoid nodule with clear boundary on MSCT images,and homogeneous or mixed low density.The CT values,long dimensions and volumes ranged from-50 to-95Hu (mean-72.58Hu),5.7 to 40.7mm (mean 17.67mm) and 0.02 to 7.03cm3 (mean 1.89cm3),respectively.Contrast-enhanced CT scans showed no enhancement in all the lesions.Conclusion MSCT can make accurate locating and qualitative diagnosis for gastric lipomas.

Objective To construct a radiomic model based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI ) for preoperative prediction of hepatocellular carcinoma (HCC ) differentiation and validate its clinical value.Methods Clinical and imaging data of 187 HCC patients who received surgical treatment in the Third Affiliated Hospital of Chongqing Medical University from June 2018 to January 2024 were collected and retrospectively analyzed.According to the postoperative pathological results,they were divided into low-differentiation group (n=58)and non-low-differentiation group (n=129),andrandomly divided into the training group (n=149)and the validation group (n=38)with the ratio of 8∶2. Univariate analysis was used to assess the clinical indicators related to HCC differentiation,and then a clinical model was constructed. Pyramidimics software was used to extract the radiomic features of IVIM-DWI functional images,and minimum absolute contraction and selection operator logistic regression algorithm were employed to screen those highly correlated indicators with HCC differentiation.Support vector machine (SVM),logistic regression (LR)and random forest (RF)algorithms were utilized to construct different image omics models.SVM algorithm was applied to construct the combined imaging omics and clinical model. The internal verification of the model was carried out by using ten-fold cross-validation.Receiver operating characteristic (ROC)curve,area under the curve (AUC),calibration curve,and decision curve analysis (DCA)were used to evaluate the diagnostic value and clinical benefits of clinical model,radiomic model,and their combination.Results A total of 4060 radiological features were extracted,and after feature screening and dimensionality reduction,24 features were finally included to construct the model.Among all models,the predictive performance of the radiomic model and the radiomic-clinical combined model was better than that of the clinical model.In the comparison between the radiomic model constructed by SVM algorithm and the radiomics-clinical combined model,the AUC value was 0.954 (0.908～1.000)for the former model,and was 0.943 (0.905～0.982)for the latter model in the training set,and there was no significant difference between them.In the validation set,the AUC value was 0.807 (0.640～0.975 )and 0.876 (0.743～1.000),respectively,with statistical difference between the 2 models (P<0.05).Calibration curve analysis showed that the radiomic model and the radiomics-clinical combined model had good goodness of fit.DCA indicated that the net benefit was higher and the threshold probability range was larger in both the radiomic model and the radiomics-clinical combined model,and the net benefit of the combined model was larger. Conclusion Both the radiomic model and the combined radiomics-clinical model constructed on the basis of IVIM-DWI functional images can better predict the severity of HCC differentiation before surgery.

Objective To explore the types of variations in the origin of left gastric artery (LGA) using MSCT angiography.Methods The abdominal MSCT angiography data of 1 500 patients were respectively reviewed,in thoses the abdominal aorta,celiac trunk,LGA,common hepatic artery (CHA),splenic artery (SA) and superior mesenteric artery (SMA)were shown clearly.The origins of the LGA and related artries were focused.A new typing method (types Ⅰ-Ⅹ) was established.And the incidence of various types was calculated.Results The normal anatomical origin (type Ⅰ) of LGAwas noted in 1 342 cases (1 342/1 500,89.47％).Eight types of LGA variant origin were identified in 70 cases (70/1 500,4.67％).LGA variant origin combined with celiomesenteric trunk (CMT) were observed in 47 cases (47/1 500,3.13％).The most common type of LGA origin variation was LGA originated from the abdominal aorta combined with CMT (type Ⅴ) which was found in 24 cases (24/1 500,1.60％).And the least common type was the namely LGA,SA,CHA and SMA arose independently from abdominal aorta (type Ⅵ) which was found in 3 cases (3/1 500,0.20 ％).LGA originated from SMA (type Ⅷ) was not found in all 1 500 cases.Conclusion There are many kinds of variations in the origin of LGA.The new typing method can contribute the comprehensive and intensive data for understanding the anatomical and radiographic features.