目的探讨胰腺血管活性肠肽瘤（VIPoma）的临床、实验室和影像学特点，以及诊断方法和治疗手段。 方法报道1例胰腺VIPoma，同时检索国内文献得到49例胰腺VIPoma患者临床资料，并对此50例病例进行分析。 结果分泌性腹泻、低血钾和代谢性酸中毒是胰腺VIPoma的主要临床表现，血浆血管活性肠肽（VIP）水平增高具有诊断价值。经手术治疗，腹泻症状可减轻或消失；对于远处转移患者，生长抑素、化疗、干扰素等治疗均有效。 结论VIPoma早期诊断困难，确诊依赖于典型临床症状、血浆VIP水平、影像学检查以及免疫组化检查。手术切除可改善预后，生长抑素等治疗可缓解症状，发生转移者也应积极治疗。
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 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 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 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.
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 investigate current status and hot issues of pancreatic neuroendocrine neoplasm (pNEN) imaging research.MethodsThe literatures focusing on pNEN and published from 1998 to 2018 were retrieved from the core database of Web of Science. The quantitative analysis of literatures was then conducted by using the CiteSpace software based on the bibliometrics method. The research trend was then summarized systematically and the potential research fronts and focuses were explored.ResultsA total of 190 articles in the field of pNEN imaging research were retrieved, and the top three countries in the literatures were the United States, Germany, and Italy. The clustering of co-citation of pNEN included the endoscopic ultrasound, current diagnosis, prospective evaluation, cystic pancreatic neuroendocrine tumor, hypervascular neuroendocrine tumor, nonfunctioning pancreatic neuroendocrine tumor, intravoxel incoherent motion, and metastastic lesion. The hot of keywords in the field of pNEN included the fine needle aspiration, CT, diagnosis, pancreas, cancer, neuroendocrine tumor, neoplasm, carcinoma, and management. The hot keywords clustering had the neuroendocrine tumor, pancreatic mass size, non-hyperfunctioning neuroendocrine tumor, CT appearance, metastatic lesion, ancillary studies, somatostatin analogues, somatostatinoma, intraoperative ultrasound, and multiple endcorine neoplasia 1.ConclusionAccurate imaging diagnosis of pNEN is still a hot issue in this field.