ObjectiveTo investigate the hotspots from researches on imaging of pancreatic neuroendocrine tumor in recent five years. MethodsThe bibliographies from research literatures on imaging of pancreatic neuroendocrine tumor from 2010 to 2015 in PubMed database were downloaded. The Bicomb 2.0 bibliographies analysis software was used to count high-frequency of Mesh major topics (MJMEs). SPSS 22.0 statistical software was applied for clustering analysis with MJMEs, then to get the topic hotspots. ResultsA total of 357 literatures were screened out during the years of 2010-2015. The MJMEs which frequency > 13 were 28. Taken the 28 MJMEs into clustering analysis, then three research hotspots were clustered. ConclusionResearches on imaging of the pancreatic neuroendocrine tumor in recent five years are mainly in terms of imaging techniques, a comparative study of pathology and endoscopic ultrasonography-fine needle aspiration, imaging and disease treatment.
ObjectiveTo investigate value of MSCT imaging on differentiating low grade pancreatic neuroendo-crine neoplasms (pNENs) from non-low grade pNENs. MethodThe clinical and CT data of 32 patients with pNENs,who were confirmed by pathological diagnosis from January 2014 to August 2015,were collected and analyzed retrospec-tively. ResultsThere were 15 patients with grade 1 in the low grade pNENs group,there were 11 patients with grade 2 and 6 patients with grade 3 in the non-low grade pNENs group.Compared with the low grade pNENs,the non-low grade pNENs had the larger diameter of the tumor (P=0.007),irregular tumor shape (P=0.006),obscure tumor margin (P=0.003),peripancreatic tissue or vascular invasion (P=0.036),lymphadenopathy (P=0.003),distant metastasis (P=0.019),lower absolute enhancement of tumor at the arterial (P=0.003) and the relative enhancement of tumor at the arterial (P=0.013). ConclusionThe analysis of MSCT features might help for differentiating low grade pNENs from non-low grade pNENs,so that more timely selection of appropriate treatment strategies would be made.
Objective To explore CT features that can be used to identify nonhypervascular pancreatic neuroendocrine neoplasm (pNEN) and pancreatic ductal adenocarcinoma (PDAC). Methods The patients with pathologically confirmed the pNEN and PDAC were retrospectively included from May 2010 to May 2017. The CT features were analyzed. The CT features were extracted by the multivariate logistic regression, and their diagnostic performances were calculated. Results Forty patients with the nonhypervascular pNEN (33 unfunctional, 7 functional) and 80 patients with the PDAC were included in this study. The features of significant differences between the nonhypervascular pNEN and the PDAC included: the location, long diameter, margin, uniform lesions, calcification, and vascular shadows of the lesion (P<0.05). The margin ［OR=14.63, 95% CI (2.82, 75.99)］, calcification ［OR=4.00, 95% CI (1.03, 15.59)］, and location ［OR=3.09, 95% CI(1.19, 7.99)］ of the lesion could independently identify the nonhypervascular pNEN. The multivariate logistic regression model of the differential diagnosis of the nonhypervascular pNEN and PDAC was obtained through the CT features of significant differences. The diagnostic sensitivity was 70.00%, 95% CI (53.5,83.4); specificity was 83.54%, 95% CI (73.5, 90.9); and area under the receiver operating curve was 0.824, 95% CI (0.743, 0.887). Conclusions Multivariate logistic regression model of CT features is helpful for differential diagnosis of nonhypervascular pNEN and PDAC. Features of margin and calcification of lesion are more valuable in differential diagnosis of nonhypervascular pNEN and PDAC.
ObjectiveTo summary the treatment of pancreatic neuroendocrine neoplasms (pNENs). MethodsArticles relevant to pNENs at home and abroad were collected and reviewed. ResultsBecause of rare incidence and non-specific clinical syndromes of pNENs, clinician had no enough cognition about it. For pNENs, surgery was still the preferred option, combining other treatments included chemotherapy, somatostatin analogue, α-interferon, molecular targeted therapy, and peptide receptor radionuclide therapy (PRRT). ConclusionSurgery is still considered as the preferred option for controlling the associated biochemical syndromes and curtailing the malignant progression of pNENs.
ObjectiveTo summarize the status and progress of imaging studies of pancreatic neuroendocrine neoplasms (pNENs).MethodThe relevant literatures published recently at domestic and abroad about the imaging of pNENs were collected and reviewed.ResultsDue to poor visibility of pancreatic body and tail, the application of ultrasound (US) was limited. Compared with US, endoscopic ultrasound (EUS) and contrast-enhanced ultrasound (CEUS) could improve the detection rate of pNENs. The ability of plain CT scans to differentiate pathological grades was still controversial, but the value of enhanced scan was higher. CT texture analysis was feasible in the discrimination of nonhypervascular pNENs and pancreatic ductal adenocarcinoma (PDAC). Teta2 was the parameter with the highest diagnostic performance. The enhanced features of MRI were similar to CT. Combined with the apparent diffusion coefficient (ADC) value, the diagnostic and classification capabilities of MRI were improved, and the sensitivity and specificity of different ADC thresholds were also different. 68Ga-tetraazacyclododecane tetraacetic acid (68Ga-DOTA) peptide PET-CT had good preliminary diagnostic value for well-differentiated pNENs, and 18Fluoro-fluorodeoxyglucose (18F-FDG) PET-CT had limited diagnostic value.ConclusionsSomatostatin receptor imaging is of high diagnostic value and can guide clinical treatment and predict prognosis, but it has not been widely used in China. Conventional morphological images have advantages in the diagnosis and classification of pNENs. Therefore, it is important to choose a proper image inspection method.
Objective To determine feasibility of texture analysis of CT images for the discrimination of nonhypervascular pancreatic neuroendocrine tumor (PNET) from pancreatic ductal adenocarcinoma (PDAC). Methods CT images of 15 pathologically proved as PNETs and 30 PDACs in West China Hospital of Sichuan University from January 2009 to January 2017 were retrospectively analyzed. Results Thirty best texture parameters were automatically selected by the combination of Fisher coefficient (Fisher)+classification error probability combined with average correlation coefficients (PA)+mutual information (MI). The 30 texture parameters of arterial phase (AP) CT images were distributed in co-occurrence matrix (18 parameters), run-length matrix (10 parameters), and autoregressive model (2 parameters). The distribution of parameters in portal venous phase (PVP) were co-occurrence matrix (15 parameters), run-length matrix (10 parameters), histogram (1 parameter), absolute gradient (1 parameter), and autoregressive model (3 parameters). In AP and PVP, the parameter with the highest diagnostic performance were both Teta2, and the area under curve (AUC) value was 0.829 and 0.740 (P<0.001,P=0.009), respectively. By the B11 of MaZda, the misclassification rate of raw data analysis (RDA)/K nearest neighbor classification (KNN), principal component analysis (PCA)/KNN, linear discriminant analysis (LDA)/KNN, and nonlinear discriminant analysis (NDA)/artificial neural network (ANN) was 28.89% (13/45), 28.89% (13/45), 0 (0/45), and 4.44% (2/45), respectively. In PVP, the misclassification rate of RDA/KNN, PCA/KNN, LDA/KNN, and NDA/ANN was 35.56% (16/45), 33.33% (15/45), 4.44% (2/45), and 11.11% (5/45), respectively. Conclusions CT texture analysis is feasible in the discrimination of nonhypervascular PNET and PDAC. Teta2 is the parameter with the highest diagnostic performance, and in AP, LDA/KNN modality has the lowest misclassification rate.
ObjectiveTo explore value of CT imaging features in differentiating pathological grades of pancreatic neuroendocrine neoplasms (pNENs). MethodsThe patients with pNENs admitted to the Sichuan Provincial People’s Hospital from October 2017 to December 2023 were retrospectively collected. The enrolled patients were assigned into a low-grade (G1+G2) pNENs and high-grade (G3+neuroendocrine carcinoma) pNENs. Then, the differences in gender, age, presence of neuroendocrine symptoms, tumor location, tumor diameter, clarity of the tumor boundary, cystic change, capsule, dilation of the biliary and pancreatic ducts, pancreatic parenchymal atrophy, vascular invasion, liver metastasis, lymph node metastasis, tumor enhancement pattern, Ct values in each phase, and the ratio of Ct value of the tumor to that of the normal pancreas (T/N value) between the low-grade pNENs and high-grade pNENs were compared. Subsequently, the multivariate logistic regression analysis was used to screen the CT imaging features with statistical significance and the receiver operating characteristic (ROC) curve was use to differentiate high-grade from low-grade pNENs. ResultsA total of 47 pNENs patients were enrolled, including 36 low-grade and 11 high-grade cases. Compared with the low-grade pNENs, the patients with high-grade pNENs had higher proportions of pancreaticobiliary duct dilation (χ2=9.124, P=0.003) and vascular invasion (χ2=10.967, P=0.001), more mild enhancement (χ2=9.192, P=0.010), larger tumor diameter (Z=–2.378, P=0.017), and lower Ct values and T/N ratios in the arterial and venous phases (P=0.001, P=0.032, P=0.006, P=0.018). The multivariate logistic regression analysis showed that the pancreaticobiliary duct dilation, vascular invasion, and decreased Ct value in the arterial phase were the predictive factors for the high-grade pNENs. The areas under the ROC curves of these three indicators for distinguishing low-grade from high-grade pNENs were 0.760, 0.749, and 0.843, respectively, the Ct value in the arterial phase had the strongest discriminatory ability, and its cutoff value was 78 HU. ConclusionsThe results of this study suggest that pancreatic ductal dilation, vascular invasion, and Ct value in arterial phase play important roles in differentiating high-grade pNENs from low-grade pNENs. Ct value in arterial phase has the greatest diagnostic efficiency.