Hepatoid adenocarcinoma (HAC) of the pancreas is a rare, highly malignant tumor with poor prognosis. This article presents the CT and MR features of HAC of the pancreas, while also reviewing the relevant literatures to succinctly summarize the underlying pathophysiological mechanism, imaging diagnosis, and differential diagnosis. The objective is to enhance the understanding of HAC of the pancreas among clinicians and radiologists.
ObjectiveTo investigate the value of magnetic resonance (MR) imaging in the assessment of bile duct complications after cholecystectomy. MethodsFifty patients of having bile duct complications after cholecystectomy underwent MR imaging and had some positive manifestations. The indication for cholecystectomy was symptometic cholelithiasis in all cases. MR imaging was performed with a 1.5T clinical imager including all of the sequences of: ①T1 weighted imaging (T1WI) was performed in transverse and coronal plane before and after gadolinium-enhanced. ②T2 weighted imaging (T2WI) was performed in transverse plane. ③A true fast imaging with steadystate precession sequence (True fisp) was performed in coronal plane. ④MR cholangiopancreatography was also obtained. ResultsThe bile duct complications after cholecystectomy including: 22 cases of cholelithiasis, 15 cases of chronic cholangitis with or without bile duct abscess. Bile duct strictures or abruption at the confluence of hepatic and common bile duct in 6 and 3 cases respectively. Tumors of bile duct or pancreas in 9 cases. The other complications after cholecystectomy including bile leak with choleperitonitis and/or biloma and acute pancreatitis.ConclusionMR imaging was a valuable method for the assessment of bile duct complications after cholecystectomy. MR imaging could assess the etiology of bile duct complications. If there were bile duct obstruction, MR imaging could assess the location and the severe of obstruction. For bile duct or pancreatic tumors, MR imaging could assess the areas of tumor infiltration and resection and was helpful to select treatment methods. Before lapatoscopic cholecystectomy, the overall and careful imaging assessment for bile duct and gallbladder and its adjacent hepatic tissue and pancreas so to avoid missing the relative tumors.
【Abstract】Objective To investigate the MR imaging （MRI） appearances of postoperative residual liver after hepatic resection for hepatocellular carcinoma (HCC) and the MRI features of tumor recurrences.Methods Twenty patients with previous surgical resection of HCC underwent MR examination of upper abdomen for routine follow-up study or due to clinical suspicion of tumor recurrence. MRI protocol included T1W axial unenhanced images and Gadoliniumenhanced sequences, Gadolinium-enhanced VIBE sequence, unenhanced T2W axial images and coronal TrueFisp sequence.Results Thirteen patients showed normal edge of surgical resection, while 6 patients demonstrated MR signs of incision edge recurrence of HCC and 1 patient was suspicious of tumor recurrence at the incision edge. Among the 20 patients, 12 had MRI features of tumor recurrence of the residual liver, including invasion of left, right and common hepatic ducts 3 cases. Three patients had metastatic lymphadenopathy in portal hepatis, portacaval space and retroperitoneal space. Two patients showed extensive tumor implantation of peritoneum and mesentery. Conclusion MRI is effective in differentiating normal surgical incision edge of residual liver from tumor recurrence. It is also very useful for the early detection of intrahepatic and extrahepatic tumor lesions.
ObjectiveTo systematically review the value of radiomics in the diagnosis of glioblastoma. MethodsPubMed, EMbase, Web of Science and The Cochrane Library databases were electronically searched to collect studies on radiomics in the grading of gliomas or the differentiation diagnosis from inception to May 30th, 2021. Two reviewers independently screened literature, extracted data, and assessed the risk of bias and the quality of the included studies. Meta-analysis was then performed using Meta-Disc 1.4 software and RevMan 5.3 software. ResultsA total of 37 studies involving 2 746 subjects were included. The results of meta-analysis showed that the pooled sensitivity, specificity, and diagnostic odds ratio for the diagnosis of glioblastoma by radiomics were 0.91 (95%CI 0.89 to 0.92), 0.88 (95%CI 0.87 to 0.90), and 78.00 (95%CI 50.81 to 119.72), respectively. The area under the summary receiver operating characteristic (SROC) curve was 0.95. The key radiomic features for correct diagnosis of glioblastoma included intensity features and texture features of the lesions. ConclusionThe current evidence shows that radiomics provides good diagnostic accuracy for glioblastoma. Due to the limited quality and quantity of the included studies, more high-quality studies are required to verify the above conclusions.
We applied resting-state functional magnetic resonance imaging (rfMRI) combined with graph theory to analyze 90 regions of the infantile small world neural network of the whole brain. We tried to get the following two points clear:① whether the parameters of the node property of the infantile small world neural network are correlated with the level of infantile intelligence development; ② whether the parameters of the infantile small world neural network are correlated with the children's baseline parameters, i.e., the demographic parameters such as gender, age, parents' education level, etc. Twelve cases of healthy infants were included in the investigation (9 males and 3 females with the average age of 33.42±8.42 months.) We then evaluated the level of infantile intelligence of all the cases and graded by Gesell Development Scale Test. We used a Siemens 3.0T Trio imaging system to perform resting-state (rs) EPI scans, and collected the BOLD functional Magnetic Resonance Imaging (fMRI) data. We performed the data processing with Statistical Parametric Mapping 5(SPM5) based on Matlab environment. Furthermore, we got the attributes of the whole brain small world and node attributes of 90 encephalic regions of templates of Anatomatic Automatic Labeling (ALL). At last, we carried out correlation study between the above-mentioned attitudes, intelligence scale parameters and demographic data. The results showed that many node attributes of small world neural network were closely correlated with intelligence scale parameters. Betweeness was mainly centered in thalamus, superior frontal gyrus, and occipital lobe (negative correlation). The r value of superior occipital gyrus associated with the individual and social intelligent scale was -0.729 (P=0.007); degree was mainly centered in amygdaloid nucleus, superior frontal gyrus, and inferior parietal gyrus (positive correlation). The r value of inferior parietal gyrus associated with the gross motor intelligent scale was 0.725 (P=0.008); efficiency was mainly centered in inferior frontal gyrus, inferior parietal gyrus, and insular lobe (positive correlation). The r value of inferior parietal gyrus associated with the language intelligent scale was 0.738 (P=0.006); Anoda cluster coefficient (anodalCp) was centered in frontal lobe, inferior parietal gyrus, and paracentral lobule (positive correlation); Node shortest path length (nlp) was centered in frontal lobe, inferior parietal gyrus, and insular lobe. The distribution of the encephalic regions in the left and right brain was different. However, no statistical significance was found between the correlation of monolithic attributes of small world and intelligence scale. The encephalic regions, in which node attributes of small world were related to other demographic indices, were mainly centered in temporal lobe, cuneus, cingulated gyrus, angular gyrus, and paracentral lobule areas. Most of them belong to the default mode network (DMN). The node attributes of small world neural network are widely related to infantile intelligence level, moreover the distribution is characteristic in different encephalic regions. The distribution of dominant encephalic is in accordance the related functions. The existing correlations reflect the ever changing small world nervous network during infantile development.
The study aims to investigate whether there is difference in pre-treatment white matter parameters in treatment-resistant and treatment-responsive schizophrenia. Diffusion tensor imaging (DTI) was acquired from 60 first-episode drug-naïve schizophrenia (39 treatment-responsive and 21 treatment-resistant schizophrenia patients) and 69 age- and gender-matched healthy controls. Imaging data was preprocessed via FSL software, then diffusion parameters including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were extracted. Besides, structural network matrix was constructed based on deterministic fiber tracking. The differences of diffusion parameters and topology attributes between three groups were analyzed using analysis of variance (ANOVA). Compared with healthy controls, treatment-responsive schizophrenia showed altered white matter mainly in anterior thalamus radiation, splenium of corpus callosum, cingulum bundle as well as superior longitudinal fasciculus. While treatment-resistant schizophrenia patients showed white matter abnormalities in anterior thalamus radiation, cingulum bundle, fornix and pontine crossing tract relative to healthy controls. Treatment-resistant schizophrenia showed more severe white matter abnormalities in anterior thalamus radiation compared with treatment-responsive patients. There was no significant difference in white matter network topological attributes among the three groups. The performance of support vector machine (SVM) showed accuracy of 63.37% in separating the two patient subgroups (P = 0.04). In this study, we showed different patterns of white matter alterations in treatment-responsive and treatment-resistant schizophrenia compared with healthy controls before treatment, which may help guiding patient identification, targeted treatment and prognosis improvement at baseline drug-naïve state.
Coronavirus disease 2019 that started in Wuhan, Hubei Province in December 2019, has spread rapidly across China. Radiography provides an important basis for clinical diagnosis of coronavirus disease 2019 and plays a crucial role in fighting against the epidemic. In the face of public health emergencies, how to quickly set up a team for accurate prevention and control in the first time and how to carry out radiological examination and diagnosis ina scientific, orderly, safe and efficient manner are the key measures to support the hospital in effectively responding to the epidemic. This paper summarized the innovative management experience of the radiology department of West China Hospital of Sichuan University in the prevention and control of nosocomial infection and discipline construction in the fight against the outbreak of coronavirus disease 2019.