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Background Gastric ectopic pancreas (GEPs) is a rare developmental anomaly which is difficult to differentiate it from submucosal tumor such as gastric stromal tumor (GST) by imaging methods. Since the treatments of the GEPs and GST are totally different, a correct diagnosis is essential. Therefore, we retrospectively investigated the CT features of them to help us deepen the understanding of GEPs and GST. Methods This study enrolled 17 GEPs and 119 GST, which were proven pathologically. We assessed clinical and CT features to identify significant differential features of GEPs from GST using univariate and multivariate analyses. Results In univariate analysis, among all clinicoradiologic features, features of age, symptom, tumor marker, location, contour, peritumoral infiltration or fat-line of peritumor, necrosis, calcification, CT attenuation value of unenhancement phase/arterial phase/portal venous phase (CTu/CTa/CTp), the CT attenuation value of arterial phase/portal venous phase minus that of unenhanced phase (DEAP/DEPP), long diameter (LD), short diameter (SD) were considered statistically significant for the differentiation of them. And the multivariate analysis revealed that location, peritumoral infiltration or fat-line of peritumor, necrosis and DEPP were independent factors affecting the identification of them. In addition, ROC analysis showed that the test efficiency of CTp was perfect (AUC = 0.900). Conclusion Location, the presence of peritumoral infiltration or fat-line of peritumor, necrosis and DEPP are useful CT differentiators of GEPs from GST. In addition, the test efficiency of CTp in differentiating them was perfect (AUC = 0.900).

Backgroud: To predict the malignancy of 1-5 cm gastric gastrointestinal stromal tumors (GISTs) in a CT risk assessment by machine learning (ML) using three models - Logistic Regression (LR), Decision Tree (DT) and Gradient Boosting Decision Tree (GBDT). Methods: 309 patients with gastric GISTs enrolled were divided into three cohorts for training (n=161), as well as internal validation (n=70) and external validation (n=78). Scikit-learn software was used to build three classifiers. Sensitivity, specificity, accuracy and area under the curve (AUC) were calculated to evaluate the performance of three models. The diagnostic difference between ML models and radiologists were compared in internal validation cohort. Important features were analyzed and compared in LR and GBDT. Results: GBDT achieved the largest AUC values (0.981 and 0.815) among three classifiers in training and internal validation cohorts and greatest accuracy (0.923, 0.833 and 0.844) in three cohorts. LR was found to have the largest AUC value (0.910) in external validation cohort. DT yielded the worst accuracy (0.790 and 0.727) and AUC (0.803 and 0.700) both in two validation cohorts. GBDT and LR showed more favorable performances than two radiologists. Long diameter was demonstrated to be the same and most important CT feature for GBDT and LR. Conclusions: ML classifiers were considered to be promising in prediction of risk classification of gastric GISTs less than 5 cm based on CT, especially GBDT and LR due to the high accuracy and strong robustness. Long diameter was found as the most important feature for risk stratification.

Background and Objectives. Recurrence and metastasis are the most important factors influencing the survival rate of patients with paragangliomas (PGLs). Accurate preoperative prediction of the risk factors and developing a reasonable therapy strategy can reduce the recurrence rate. Computed tomography (CT) is regarded as the preferred imaging modality for the initial evaluation of PGLs. However, only a few studies have investigated the relationship between CT features and the invasiveness of PGLs. Therefore, we investigated the prognostic importance of CT features for PGLs. Methods. We studied 51 abdominal PGL patients at the First Affiliated Hospital of Bengbu Medical College, Tongde Hospital, and Sir Run Shaw Hospital, Hangzhou, Zhejiang Province, China, from June 2009 to May 2019. Thereafter, the clinical research data, tumor biomarkers, and CT features were compared between the aggressive PGLs and the nonaggressive PGLs using independent-samples t-tests and chi-square tests. Results. Of the 51 cases, 43 were benign and 8 had malignant tendencies. Postoperative recurrence and metastasis were more likely to occur when the tumor diameter was >8 cm or/and the enhancement degree was not obvious. Clinical symptoms, tumor markers, sex, age, and CT image characteristics including morphology, presence of cystic degeneration, “pointed peach” sign, calcification, hemorrhage, enlarged lymph nodes, and peritumor and intratumor blood vessels were not significantly different between the two groups p > 0.05 . Conclusion. Our findings suggest that CT features, including size >8 cm and enhancement degree, could provide important evidence to assess risk factors for aggressive PGLs.

Abstract Background:Castleman's disease (CD) is an uncommon type of benign proliferation of the lymphoid tissue.The occurrence of CD in the liver is rare. Radiological diagnosis of hepatic CD by computed tomography (CT) and magnetic resonance imaging (MRI) remains difficult. It is difficult to distinguish between CD of the liver and hypervascular liver tumors such as focal nodular hyperplasia (FNH) and hepatocellular adenoma before surgery.Case presentation: We report a unique case of CD in the hepatis cauda lobe in a 26-year-old young woman after a health checkup,abdominal ultrasonography, CT and MRI results showed a 5 cm×3 cm sized hypervascular mass. The patient was diagnosed as CD by histologic examination. The liver mass showed the imaging feature of central low density or signal enhancement in the arterial phase on contrast-enhanced CT and MRI, the tortuous access vessel could be seen in the lesion. The tumor became homogeneous isodense or isointense to the surrounding liver tissues during the portal phase. Dffusion restriction demonstrated on MRI. All these imaging findings, which might be useful clues for identifying CD in the liver.The patient underwent complete resection of the tumor. The patient was followed-up without any further treatment and the disease has shown no signs of recurrence.Conclusions:Primarily hepatic CD without lymph node hyperplasia or clinical symptoms is extraordinarily rare and different from other hypervascular liver tumors. Therefore, it is extremely important to improve the awareness of this diagnosis. Attention should be paid to the difference hepatic CD between FNH, hepatocellular adenoma, lymphoma and so on. To avoid misdiagnosis with unnecessary surgical excision, distinguishing these diseases is crucial.