The purpose of this study was to establish a clinical prediction model for the differential diagnosis of pulmonary cystic echinococcosis (CE) and pulmonary abscess according to computed tomography (CT)-based radiomics signatures and clinical indicators. This is a retrospective single-centre study. A total of 117 patients, including 53 with pulmonary CE and 64 with pulmonary abscess, were included in our study and were randomly divided into a training set (n = 95) and validation set (n = 22). Radiomics features were extracted from CT images, a radiomics signature was constructed, and clinical indicators were evaluated to establish a clinical prediction model. Finally, a model combining imaging radiomics features and clinical indicators was constructed. The performance of the nomogram, radiomics signature and clinical prediction model was evaluated and validated with the training and test datasets, and then the three models were compared. The radiomics signature of this study was established by 25 features, and the radiomics nomogram was constructed by using clinical factors and the radiomics signature. Finally, the areas under the receiver operating characteristic curve (AUCs) for the training set and test set were 0.970 and 0.983, respectively. Decision curve analysis showed that the radiologic nomogram was better than the clinical prediction model and individual radiologic characteristic model in differentiating pulmonary CE from pulmonary abscess. The radiological nomogram and models based on clinical factors and individual radiomics features can distinguish pulmonary CE from pulmonary abscess and will be of great help to clinical diagnoses in the future.

Objective: To investigate the potential key biomarkers for the diagnosis of coronary artery disease (CAD) in pericoronal adipose tissue using bioinformatics analysis, and to explore the mechanism underlying the occurrence and progression of CAD. Methods: Two datasets were downloaded from the Gene Expression Omnibus (GEO) database for bioinformatics analysis, the differentially expressed genes (DEGs) were identified, and the relevant biological pathways of these genes were functionally annotated and enriched by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Meanwhile, functional enrichment and protein–protein interaction (PPI) network analyses. Pericoronary adipose tissue and subcutaneous adipose tissue of patients with CAD(n=60) were analyzed and verified by quantitative reverse transcription polymerase chain reaction (RT-qPCR). Results: A total of 82 DEGs from CAD patients and healthy individuals. The results of enrichment analysis showed that the top DEGs were mainly enriched in the retinol metabolism, carbon metabolism, and peroxisome proliferator-activated receptor (PPAR) signaling pathway. Among them, the PPAR signaling pathway with the strongest correlation with the key genes was associated with the downstream target protein Janus kinase (JAK), and verification using RT-qPCR revealed that the expression levels of JAK, monocyte chemoattractant protein-1 (MCP-1), platelet endothelial cell adhesion molecule-1 (PECAM-1/CD31), IL-6, and leptin in pericoronary PVAT tissue were significantly upregulated. In contrast, the expression level of PPAR was significantly reduced (P<0.05). Conclusion: This study revealed 4 DEGs in pericoronal adipose tissue for diagnosing CAD, which may improve understanding of CAD and assist scholars to explore the molecular mechanism of CAD.

Coronary artery fistula (CAF) is a rare coronary anomaly defined as a communication between coronary artery and other heart chambers or vascular structures. In this case report, a 32-year-old woman with a giant left main coronary artery to the right atrium fistula with endocarditis was presented. CAF was diagnosed by transthoracic echocardiography and subsequently confirmed by cardiac computerized tomographic and coronary angiography. The patient received antibiotic treatment for infective endocarditis for 6 weeks preoperatively. The fistula was successfully treated with surgical repair. The patient is well now after 18 months of follow-up.

Background. Alveolar echinococcus (AE) is a severe health problem in endemic areas. In recent years, the incidence of this disease in China has been increasing. The study was designed to illustrate the multi-slice computed tomography (MSCT) and magnetic resonance imaging (MRI) features of extrahepatic AE.Methods. A cohort of 33 patients who suffered from extrahepatic AE was enrolled consecutively from January 2012 to December 2017. The MSCT and MRI features of extrahepatic AE were recorded and analyzed by experienced radiologists. The MSCT and MRI agreements for detecting imaging features of extrahepatic AE were calculated using kappa statistics.Results. All cases secondary to hepatic AE, except two primary extrahepatic AE, were found in this study. Locations of extrahepatic AE included 19 (57.6%) lung, 10 (30.3%) adrenal gland, 9 (27.3%) brain, 5 (15.2%) peritoneal cavity, 5 (15.2%) spleen, 4 (12.1%) diaphragm, 3 (9.1%) kidney, 3 (9.1%) retroperitoneal, and 2 (6.1%) vertebra; Involvement of 1 (3.0%) heart, 1 (3.0%) mediastinum, 1 (3.0%) muscle, and 1 (3.0%) pancreas was rare. AE of the lung usually appeared as irregular and scattered nodules with small vacuoles or cavities inside and peripheral distribution. Multiple cerebral nodules with calcification and surrounding edema were the most common features seen in brain AE. Adrenal gland AE presented as plaques containing different sizes of hypodense areas and different amounts of calcification. Injection of contrast medium showed no enhancement of lesions except in the brain. Very good agreements were seen between MSCT and MR for detecting number (κ=0.841, p=0.000), border (κ=0.911, p=0.000) and size (κ=0.864, p=0.000) of extrahepatic AE.Conclusions. MSCT and MRI are reliable imaging methods for the diagnosis of extrahepatic AE. When one AE patient is clinically confirmed, MSCT scan from the head to pelvis should be performed to exclude other organs AE.