BackgroundThis study aims to analyze the computed tomography (CT) and magnetic resonance imaging(MRI) characteristics of hepatic epithelioid hemangioendothelioma (HEHE).MethodsEleven patients with histopathologically confirmed HEHE via surgical excision or biopsy were included. Imaging findings of these 11 patients were retrospectively analyzed (CT images obtained from all patients and MR images from five patients). Patterns of growth, characteristics of distribution, density/signal features, patterns of contrast enhancement, and changes of adjacent tissues were evaluated.ResultsHEHE is characterized by multiple lesions in the liver. HEHE could be further categorized as three types when considering patterns of growth: nodular type(5 cases), coalescent type(1 case) and mixed type(5 cases). In this study, a total of 312 lesions were detected, 214(74.3 %) of which were subcapsular. All lesions appeared as hypodense while round lower density were found within 10 lesions(<2 cm) on unenhanced CT images. On MRI, all lesions demonstrated low signal intensity on T1 weighted images and high heterogeneous signal intensity on T2 weighted images when compared to the normal liver parenchyma. Other imaging features included “lollipop sign”(6 cases) and capsular retraction(6 cases). On contrast-enhanced CT and MRI, lesions smaller than 2.0 cm mostly showed mild homogeneous enhancement (214/227, 94.3 %); lesions measuring 2.0–3.0 cm in diameter showed ring-like enhancement (16/53,30.2 %) and heterogeneous delayed enhancement (29/53,54.7 %); lesions larger than 3.0 cm demonstrated heterogeneous delayed enhancement (26/32, 81.3 %).ConclusionThe imaging findings of HEHE showed some typical imaging features and size-dependent patterns with contrast enhancement on both CT and MR images, these features can be used for accurate imaging diagnosis of HEHE.

Objective To investigate the imaging features observed in preoperative Gd-EOB-DTPA-dynamic enhanced MRI and correlated with the presence of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Methods 66 HCCs in 60 patients with preoperative Gd-EOB-DTPA-dynamic enhanced MRI were retrospectively analyzed. Features including tumor size, signal homogeneity, tumor capsule, tumor margin, peritumor enhancement during mid-arterial phase, peritumor hypointensity during hepatobiliary phase, signal intensity ratio on DWI and apparent diffusion coefficients (ADC), T1 relaxation times, and the reduction rate between pre- and postcontrast enhancement images were assessed. Correlation between these features and histopathological presence of MVI was analyzed to establish a prediction model. Results Histopathology confirmed that MVI were observed in 17 of 66 HCCs. Univariate analysis showed tumor size (p = 0.003), margin (p = 0.013), peritumor enhancement (p = 0.001), and hypointensity during hepatobiliary phase (p = 0.004) were associated with MVI. A multiple logistic regression model was established, which showed tumor size, margin, and peritumor enhancement were combined predictors for the presence of MVI (α = 0.1). R2 of this prediction model was 0.353, and the sensitivity and specificity were 52.9% and 93.0%, respectively. Conclusion Large tumor size, irregular tumor margin, and peritumor enhancement in preoperative Gd-EOB-DTPA-dynamic enhanced MRI can predict the presence of MVI in HCC.

BackgroundPancreatic schwannoma is a rare tumor. Preoperative diagnosis of pancreatic schwannoma is challenging due to its tendency to mimic other lesions of the pancreas. We describe a case of pancreatic schwannoma and present a review of the cases currently reported in the English literature to identify characteristics of pancreatic schwannoma on imaging.Case presentationA 53-year-old male presented with a history of intermittent periumbilical abdominal pain and lower back pain for 1 week. Based on ultrasound (US) and computed tomography (CT) findings, we made a preoperative diagnosis of solid pseudopapillary tumor and performed a standard pancreaticoduodenectomy. Pathological examination showed that the tumor was composed of spindle cells with a palisading arrangement, and immunohistochemistry revealed strong positive staining for S-100 protein, which was consistent with a diagnosis of pancreatic schwannoma. At the 8-month follow-up visit, the patient was doing well without recurrent disease, and his abdominal pain had resolved.ConclusionsAlthough pancreatic schwannoma is rare, it should be included in the list of differential diagnoses of pancreatic masses, both solid and cystic. A tumor size larger than 6.90 cm, vascular encasement, or visceral invasion should elicit suspicion of malignant transformation.

Introduction: The pathological grading of pancreatic neuroendocrine neoplasms (pNENs) is an independent predictor of survival and indicator for treatment. Deep learning (DL) with a convolutional neural network (CNN) may improve the preoperative prediction of pNEN grading. Methods: Ninety-three pNEN patients with preoperative contrast-enhanced computed tomography (CECT) from Hospital I were retrospectively enrolled. A CNN-based DL algorithm was applied to the CECT images to obtain 3 models (arterial, venous, and arterial/venous models), the performances of which were evaluated via an eightfold cross-validation technique. The CECT images of the optimal phase were used for comparing the DL and traditional machine learning (TML) models in predicting the pathological grading of pNENs. The performance of radiologists by using qualitative and quantitative computed tomography findings was also evaluated. The best DL model from the eightfold cross-validation was evaluated on an independent testing set of 19 patients from Hospital II who were scanned on a different scanner. The Kaplan-Meier (KM) analysis was employed for survival analysis. Results: The area under the curve (AUC; 0.81) of arterial phase in validation set was significantly higher than those of venous (AUC 0.57, p = 0.03) and arterial/venous phase (AUC 0.70, p = 0.03) in predicting the pathological grading of pNENs. Compared with the TML models, the DL model gave a higher (although insignificantly) AUC. The highest OR was achieved for the p ratio <0.9, the AUC and accuracy for diagnosing G3 pNENs were 0.80 and 79.1% respectively. The DL algorithm achieved an AUC of 0.82 and an accuracy of 88.1% for the independent testing set. The KM analysis showed a statistical significant difference between the predicted G1/2 and G3 groups in the progression-free survival (p = 0.001) and overall survival (p < 0.001). Conclusion: The CNN-based DL method showed a relatively robust performance in predicting pathological grading of pNENs from CECT images.

The aim of the study is to investigate if the fat content of the liver and pancreas may indicate impaired glucose tolerance (IGT) or type 2 diabetes mellitus (T2DM). A total of 83 subjects (34 men; aged 46.5 ± 13.5 years) were characterized as T2DM, IGT, or normal glucose tolerant (NGT). NGT individuals were stratified as <40 or ≥40 years. Standard laboratory tests were conducted for insulin resistance and β-cell dysfunction. The magnetic resonance imaging Dixon technique was used to determine fat distribution in the liver and pancreas. Correlations among liver and pancreatic fat volume fractions (LFVFs and PFVFs, respectively) and laboratory parameters were analyzed. Among the groups, fat distribution was consistent throughout sections of the liver and pancreas, and LFVFs closely correlated with PFVFs. LFVFs correlated more closely than PFVFs with insulin resistance and β-cell function. Both the LFVFs and PFVFs were the highest in the T2DM patients, less in the IGT, and least in the NGT; all differences were significant. The PFVFs of the NGT subjects ≥40 years were significantly higher than that of those <40 years. The fat content of the liver and pancreas, particularly the liver, may be a biomarker for IGT and T2DM.