Diagnosis Of Pancreatic Neuroendocrine Tumors Research Articles

Chromothripsis is a novel biomarker for prognosis and differentiation diagnosis of pancreatic neuroendocrine neoplasms.

This study aimed to identify the role of chromothripsis as a novel biomarker in the prognosis and differentiation diagnosis of pancreatic neuroendocrine neoplasms (pNENs). We conducted next-generation gene sequencing in a cohort of 30 patients with high-grade (G3) pNENs. As a reference, a similar analysis was also performed on 25 patients with low-grade (G1/G2) pancreatic neuroendocrine tumors (pNETs). Chromothripsis and its relationship with clinicopathological features and prognosis were investigated. The results showed that DNA damage response and repair gene alteration and TP53 mutation were found in 29 and 11 patients, respectively. A total of 14 out of 55 patients had chromothripsis involving different chromosomes. Chromothripsis had a close relationship with TP53 alteration and higher grade. In the entire cohort, chromothripsis was associated with a higher risk of distant metastasis; both chromothripsis and metastasis (ENETS Stage IV) suggested a significantly shorter overall survival (OS). Importantly, in the high-grade pNENs group, chromothripsis was the only independent prognostic indicator significantly associated with a shorter OS, other than TP53 alteration or pathological pancreatic neuroendocrine carcinomas (pNECs) diagnosis. Chromothripsis can guide worse prognosis in pNENs, and help differentiate pNECs from high-grade (G3) pNETs.

Abstract B009: Identifying of a novel diagnostic markers for pancreatic neuroendocrine tumors by proteomics with patient blood

Abstract Neuroendocrine tumors are tumors originating from endocrine cells in various organs, and present very heterogeneous characteristics, and the incidence rate is gradually increasing. Among them, neuroendocrine tumors originating from pancreas are divided into functional tumors and non-functional tumors according to the presence hormone secretion function. Since non-functional tumors are often asymptomatic, early diagnosis is not easy, and differential diagnosis from pancreas ductal adenocarcinoma (PDAC) is difficult, making it difficult to determine the direction of treatment. Therefore, we aimed to find protein biomarkers for the diagnosis and differential diagnosis of pancreatic neuroendocrine tumors (PNET) through proteomic analysis of human blood. First, a total of 60 human plasma samples were obtained from 17 PNETs, 23 PDACs, and 20 Healthy donors (HD), and liquid chromatography-mass spectrometry (LC-MS) proteomics analysis was performed. For 60 cases of raw data obtained through LC-MS analysis, identification and label-free quantification were performed through the Human swissprot protein sequence database. Through pre-processing and normalization, an average of 549 protein lists for each sample were obtained. And through quantitative evaluation, the proteomes that were increased or decreased for each disease were analyzed and classified, and ACTR3, CD163, and LECT2, which were increased in PNET, were selected as diagnostic biomarker candidates. The diagnostic AUC for distinguishing PNET from HD was 0.706 for ACTR3, 0.971 for CD163, and 0.541 for LECT2. The diagnostic AUC to distinguish PNET from PDAC was 0.821 for ACTR3, 0.870 for CD163, and 0.783 for LECT2. We selected three candidates, ACTR3, CD163, and LECT2, as blood biomarkers for the accurate diagnosis of PNET. And through the combination of markers, it is expected that the sensitivity and accuracy of the diagnosis can be further increased. In the future, we will study the function as a marker that can simultaneously predict diagnosis and treatment response by identifying the molecular biological mechanism for the generation and progression of PNET through these three markers. Citation Format: Eun-Young Koh, Ji Hye Jeong, Hee-Sung Ahn, Kyunggon Kim, Eunsung Jun. Identifying of a novel diagnostic markers for pancreatic neuroendocrine tumors by proteomics with patient blood [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B009.

C1QA and COMP: plasma-based biomarkers for early diagnosis of pancreatic neuroendocrine tumors

Pancreatic Neuroendocrine tumors (PanNET) are challenging to diagnose and often detected at advanced stages due to a lack of specific and sensitive biomarkers. This study utilized proteomics as a valuable approach for cancer biomarker discovery; therefore, mass spectrometry-based proteomic profiling was conducted on plasma samples from 12 subjects (3 controls; 5 Grade I, 4 Grade II PanNET patients) to identify potential proteins capable of effectively distinguishing PanNET from healthy controls. Data are available via ProteomeXchange with the identifier PXD045045. 13.2% of proteins were uniquely identified in PanNET, while 60% were commonly expressed in PanNET and controls. 17 proteins exhibiting significant differential expression between PanNET and controls were identified with downstream analysis. Further, 5 proteins (C1QA, COMP, HSP90B1, ITGA2B, and FN1) were selected by pathway analysis and were validated using Western blot analysis. Significant downregulation of C1QA (p = 0.001: within groups, 0.03: control vs. grade I, 0.0013: grade I vs. grade II) and COMP (p = 0.011: within groups, 0.019: control vs grade I) were observed in PanNET Grade I & II than in controls. Subsequently, ELISA on 38 samples revealed significant downregulation of C1QA and COMP with increasing disease severity. This study shows the potential of C1QA and COMP in the early detection of PanNET, highlighting their role in the search for early-stage (Grade-I and Grade-II) diagnostic markers and therapeutic targets for PanNET.

A preoperative CT radiomics model to predict disease-free survival in patients with pancreatic neuroendocrine tumors.

Imaging has demonstrated capabilities in the diagnosis of pancreatic neuroendocrine tumors (pNETs) but its utility for prognostic prediction has not been elucidated yet. The aim of this study was to build a radiomics model using preoperative computed tomography (CT) data that may help predict recurrence-free survival (RFS) or overall survival (OS) in patients with pNET. Radiomics features of preoperative CT data obtained from patients with surgically resected pNET were extracted using 3D-Slicer® software with manual segmentation. Discriminant features were selected with penalized regression using least absolute shrinkage and selection operator method with training on tumor Ki67 rate (≤2 or >2). Selected features were used to build a radiomics index ranging from 0 to 1. A receiving operator curve was built to select optimal cut-off value of radiomics index to predict patient RFS and OS. RFS and OS were assessed using Kaplan-Meier analysis. Thirty-seven patients (median age, 61 years; 20 men) with 37 pNETs (Grade1, 21/37 [56%]; Grade 2, 11/37 [29%]; Grade 3, 4/37 [10%]) were included. Patients with a radiomics index >0.4 had a shorter median RFS (36 months; range: 1-133) than those with a radiomics index ≤0.4 (84 months; range: 9-148) (P = 0.013). No associations were found between radiomics index and OS (P = 0.86). The proposed radiomics index allows identifying patients with shorter RFS after pNET surgery. This result, however, requires prospective validation but may improve patient' care by intensifying imaging follow-up for those identified at high risk of recurrence.

The role of needle-based confocal laser endomicroscopy in the diagnosis of pancreatic neuroendocrine tumors.

Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is a highly accurate method for diagnosing pancreatic neuroendocrine tumors (PNETs); however, some PNETs are difficult to diagnose. Recently, the efficacy of needle-based confocal laser endomicroscopy (nCLE) in diagnosing solid pancreatic masses has been reported. However, the efficacy of nCLE in the diagnosis of PNETs remains unknown and only a small number of cases have been reported. Hence, this study aimed to evaluate the efficacy of nCLE in the diagnosis of PNETs. This single-center retrospective study evaluated 30 consecutive patients with suspected PNETs on contrast-enhanced computed tomography, who consented to nCLE combined with EUS-FNA and were diagnosed using EUS-FNA or surgical resection. The diagnostic criteria for PNETs using nCLE were based on the nesting and trabecular and glandular arrangement of tumor cell clusters surrounded by capillary vessels and fibrosis, as reported in previous studies. The diagnosis using nCLE was classified into three categories: misdiagnosis in three cases (10%), non-diagnostic in six cases (20%), and diagnostic in 21 cases (70%). nCLE was able to diagnose PNET in one of the two cases with inconclusive EUS-FNA. Although further development of the resolution and optimization of the diagnostic criteria are required, nCLE may constitute a useful diagnostic option in cases of inconclusive EUS-FNA for PNETs.

Dynamic 68Ga-DOTA0-Tyr3-octreotate positron emission tomography-computed tomography for the evaluation of pancreatic neuroendocrine tumors: a pilot study.

68Ga-DOTA0-Tyr3-octreotate (68Ga-DOTATATE) is a radiolabeled somatostatin analog used for the diagnosis of pancreatic neuroendocrine tumors (pNETs), and standardized uptake value (SUV) measurements for therapeutic monitoring is recommended. However, changes in net influx rate (Ki) may better reflect treatment effects than may those of the SUV. The aim of this study was to investigate the value of dynamic 68Ga-DOTATATE positron emission tomography-computed tomography (PET-CT) in the evaluation of pNETs. Dynamic PET-CT scans over 60 min were acquired for 7 patients with localized pancreatic mass before surgery. Maximal and mean SUV (SUVmax and SUVmean) were measured in tumors and normal pancreatic body as reference tissue (RT). Time-activity curves (TACs) were extracted from tumors and RT. A 2-tissue compartment model was used to calculate the rate constants K1, k2, and k3 (min-1); Ki (mL/g/min); and K1:k2 ratio. The following statistical tests were used to evaluate the results: the Shapiro-Wilk, Student t test, Mann-Whitney, Spearman, and Pearson rank correlation tests. Among 6 patients, 8 primary tumors were histopathologically proven to be pNETs. Moreover, 6 lesions with high uptake of 68Ga-DOTATATE showed an ascending TAC pattern, while 2 lesions with no or low uptake showed a descending TAC pattern. The mean SUVmax and SUVmean of pNETs were 46.4±40.2 (range, 3.9-109.9) and 21.9±16.0 (range, 0.5-42.8), respectively, which were significantly higher than the SUVmax of 4.2±0.6 (range, 3.1-4.9) and the SUVmean of 2.7±1.0 (range, 1.4-3.6) for the RT (P=0.021 and P=0.036), respectively. The Ki of pNETs was statistically higher than that of the RT [pNET: 0.366±0.372 (range, 0.019-0.992); RT: 0.060±0.017 (range, 0.04-0.08); P=0.036]. The mean K1:k2 ratio in pNETs was 12-fold higher than that of RT (6.06 vs. 0.50). In pNETs, there was a positive correlation between SUVmax and Ki (r=0.952; P<0.001) and between SUVmean and Ki (r=0.905; P=0.002). Another patient was diagnosed with intrapancreatic accessory spleen. The uptake of 68Ga-DOTATATE by pNETs can be explained by its high Ki value and K1:k2 ratio. Dynamic 68Ga-DOTATATE PET-CT can serve as a potential tool for evaluating pNETs and support the further assessment of a larger cohort of patients.

Association of guideline adherence in diagnosis and treatment of pancreatic neuroendocrine tumors with survival.

e16241 Background: Clinical guidelines and their adherence are important instruments to ensure quality in diagnosis and treatment, especially when treating rare diseases like pancreatic neuroendocrine tumors (PanNET). However, data on guideline adherence and its impact on outcome in PanNET is limited. Methods: Cases of PanNET according to 2017 WHO definition at our university hospital between 2010 and 2019 were retrospectively identified. Adherence to ENETS guideline 2007/2012 and the German S2k guideline was evaluated among four categories (diagnostics: chromogranin A (CgA) testing, somatostatin receptor imaging, discussion in an interdisciplinary tumor board; pathology: reporting of grading, Ki67 and synaptophysin expression; surgical treatment; systemic treatment including aftercare). Guideline adherence within these categories was valued with one point per fulfilled criterion and a final score between 0-8 points was calculated. Data was analyzed using Student’s t-test, Chi-square test and Spearman’s correlation coefficient test. Results were considered significant at α = 0.05. Results: Overall, 115 patients (47% female) with a diagnosis of PanNET were identified. Mean age was 61 (±13.5) years. Mean overall survival (OS) was 45 months, mean recurrence free survival (RFS) was 47 months. During the study period 21 patients (18%) had died, 6 patients (5%) were lost-to-follow-up and 11 patients (10%) had a recurrent disease after initially curative resection. 24 patients (21%) presented with metastatic disease upon diagnosis. Guideline adherence concerning pathology reports (97%) as well as systemic (91%) and surgical treatment (75%) was high, while complete adherence in diagnostic modalities was low (5%). However, the latter was mainly driven by lack of CgA testing (31%), and somatostatin receptor imaging (11%) not being performed on initial diagnosis, but in most cases during follow-up. Subsequently, complete guideline adherence (8/8) across all categories was rare (3%). However, a higher overall score was associated with survival (p = 0.032, V = 0.321). Long term survivors had a higher mean overall adherence score (5.6 vs. 6.1; p = 0.05). In addition, the composite score of surgical and systemic therapy was significantly correlated with RFS (ρ = 0.254, p = 0.016). Conclusions: The quality in diagnosis and treatment of PanNET in our cohort was high. These are the first data to demonstrate a positive impact of guideline adherence on survival and RFS in PanNET.

Tumores neuroendócrinos pancreáticos: revisão histopatológica

Introduction: Pancreatic neuroendocrine tumors are considered rare. They are classified into functioning and non-functioning. Objective: To define and classify pancreatic neuroendocrine tumors according to their histopathological and immunohistochemical evaluation, associated with diagnostic criteria. Method: This is a narrative review of publications found in PubMed, SciELO and Google Scholar. Results: These tumors can be well or poorly differentiated and have distinct microscopic characteristics. Well-differentiated cells are small in shape, have uniform round or oval nuclei, finely granular cytoplasm indicating strong secretory capacity, and maintain the organoid structure. Presence of tumor necrosis, increased mitotic activity and high Ki-67 index indicate a high probability of neuroendocrine neoplasia. Chromogranin A and synaptophysin favor the diagnosis of well-differentiated. The positive staining of BCL 10 together with the absence of expression of chromogranin A and synaptophysin show poor tumor differentiation. The presence of positive staining for hormone expressions does not define the tumor as functioning. Conclusion: There was an increase in the diagnosis of pancreatic neuroendocrine tumors with the use of imaging techniques and awareness of the disease. Histopathological analysis with immunohistochemistry, especially when there are consuming symptoms, can indicate the type of carcinoma and lead to the most appropriate treatment.

Tumeurs neuroendocrines digestives bien différenciées: focus sur les tumeurs pancréatiques

Pancreatic neuroendocrine tumors are rare tumors showing a rising incidence. They are well-differentiated tumors, classified by grade according to their Ki67 index value (grade 1 to 3). Pancreatic neuroendocrine tumors are mainly sporadic tumors but about 10% arise within endocrine tumor syndromes such as multiple endocrine neoplasia type 1. They can be responsible for functional syndromes or non-specific clinical symptoms depending on tumor extension. However, there is also an increase of incidental diagnoses of nonfunctional pancreatic neuroendocrine tumors with the widespread use of high-quality imaging techniques. About 50% of pancreatic neuroendocrine tumors are diagnosed at a metastatic stage, with metastases often located in the liver. Chromogranin A, CT-scan and often an abdominal MRI, and functional imaging should be performed for tumor staging and follow-up. Imaging with PET/CT with68Ga-labeled somatostatin analogues has the highest sensitivity for the diagnosis of pancreatic neuroendocrine tumors, while 18fluorodeoxyglucose PET/CT can sometimes be useful. Overall, they are rather indolent tumors with prolonged survival. Surgery is the recommended treatment in the localized setting, with the exception of small<2cm nonfunctional tumors that can be monitored with imaging techniques. For advanced tumors, there are several available treatments such as somatostatine analogues, chemotherapy, targeted therapies (sunitinib, everolimus), locoregional ablative therapies and Peptide Receptor Radiolabelled Therapy. The treatment strategy will depend on the initial tumor staging, tumor grade, aggressiveness and patient's choice.

Comment on: Are Parenchyma-Sparing Resections Really Appropriate for Small (

Comment on: Are Parenchyma-Sparing Resections Really Appropriate for Small (<3 cm) Nonfunctional Pancreatic Neuroendocrine Tumors?

Use of dual-layer spectral detector computed tomography in the diagnosis of pancreatic neuroendocrine neoplasms

PurposeTo explore the optimal energy level of dual-layer spectral detector computed tomography (DLCT) images of pancreatic neuroendocrine neoplasms (pNENs) and investigate the value in their detection. MethodsThis retrospective analysis included 134 pNEN patients with 136 lesions; they underwent contrast-enhanced DLCT scanning with histopathological confirmation of pNENs. Virtual monoenergetic images (VMI) of 40–100 keV, iodine concentration map (IC map), Z-effective atomic number map (Zeff map), and conventional images were analysed. The optimal energy level was obtained by comparing the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The lesion detection rates of DLCT and conventional images were compared. Subjective image analysis was performed by two readers who assessed the image quality and lesion conspicuity on a 5-point scale. ResultsThe SNR of VMIs from 40 to 80 keV (arterial phase, P < 0.001; venous phase, P < 0.05) and CNR from 40 to 60 keV (arterial and venous phases, each P < 0.05) were higher than that of conventional images; VMI40keV showed the highest SNR and CNR. There was a good inter-reader agreement between the two reviewers (Kappa values > 0.61); the scores of Zeff and IC maps were higher than those of conventional images and VMI40keV (P < 0.05). The detection performance of DLCT images was better than conventional images. ConclusionsThe VMI40keV demonstrated the best CNR and SNR of pNENs compared to other VMIs. Zeff and IC maps improve objective image quality and reader preference compared to conventional images. These findings could possess important clinical implications in formulating treatment strategies.

Texture and CT-features in differentiation of hypervascular pancreatic neuroendocrine tumors from renal cell carcinoma metastases: diagnostic model

Objective: to develop a diagnostic model that includes CT and radiomic features for the differential diagnosis of pancreatic neuroendocrine tumors (PNETs) G1 and G2 and pancreatic renal cell carcinoma (RCC) metastases.Material and Methods. 78 patients with 79 hypervascular PNETs and 17 patients with 24 pancreatic RCC metastases who underwent pancreatic resection and histological verification were selected in the study. All the patients underwent preoperative contrast enhanced CT (CECT). We assessed tumor attenuation, composition (cystic/solid), homogeneity (homogeneous/heterogeneous), calcification and presence of the main pancreatic duct (MPD) dilation. We calculated lesion-to-parenchyma contrast (LPC), relative tumor enhancement ratio (RTE) and extracted 52 texture features for arterial phase of CECT. Qualitative and texture features were compared between PNETs and pancreatic RCC metastasis. The selection of predictors for the logistic model was carried out in 2 successive stages: 1) selection of predictors based on one-factor logistic models, the selection criterion was p &lt; 0.2; 2) selection of predictors using L2 regularization (LASSO regression after standardization of independent variables). The selected predictors were included in a logistic regression model without interactions, the coefficients of which were estimated using the maximum likelihood method with a penalty of 0.8.Results. There was no difference in composition, homogeneity (homogeneous/heterogeneous) and presence of the MPD dilation between groups. We did not find calcification in pancreatic RCC metastasis, in contrast to the PNETs (9% contained calcifications). After selection, the LCR, CONVENTIONAL_HUmin, GLCM_Correlation, NGLDM_Coarseness were included in the final diagnostic model, which showed a sensitivity and specificity of 95.8%; 62% in the prediction of pancreatic RCC metastases.Conclusion. The diagnostic model developed on the basis of texture and CT-features has high sensitivity (95.8%) with moderate specificity (62%), which allows it to be used in complex diagnostic cases to determine the patient's treatment tactics.

Endosonografia a elastografia v diagnostike neuroendokrinných nádorov pankreasu

Summary: Introduction: The group of neuroendocrine tumors derived from pancreatic cells is called pancreatic neuroendocrine tumors (PNETs). The combination of EUS and elastography (EG) expands diagnostic and imaging capabilities. Aim: The aim of our work was to determine the representative images of B-mode for PNET, evaluation of a typical EG image of PNETs, use of strain ratio (SR) and “strain histogram” (SH) in differential diagnosis of PNETs, determination of SR and SH cut off value for PNETs and comparison of standardized measurements with literature. Methods: Patients examined at the Internal Gastroenterology Clinic were included in the cohort. A total of 31 patients were examined. The group included 25 patients (8 men, 17 women). The mean age in group was 52.76 years (14–74). Non-invasive examination by endoscopic ultrasonography was performed on all patients. After locating the lesion by ultrasound, the first recording was made after freezing the image in B-mode and performing size measurement. Subsequently, a Strain elastography measurement was performed. In the monitored group we recorded an average size of 12.75 mm. Results: The characteristics of the image in B-mode were as follows for PNETs 68% hypoechogenic, 12% hyperechogenic, 12% isoechogenic and 8% mixed echogenicity. 80% of PNETs in B-mode were sharply demarcated and 20% with blurred borders. The accuracy of the value assignment typical of pancreatic malignancies using elastography was 96% for PNET in 5-degree classification system and 88% for a 4-degree classification, for SR cut off &gt;3.2 with a sensitivity of 80% and SH cut off &lt;50 100%. Discussion: Given the average tumor sizes observed in our study, EUS provides high sensitivity in PNET diagnostics and allows diagnostics at a time when minimally invasive removal is still possible. In addition to the typical picture of hypoechogenicity and the less frequently described pictures of hyperechogenicity and isoechogenicity for PNET, we also observed mixed echogenicity. The elastographic qualitative strain image evaluated by the four- and five- -degree classification proved to be reliable in distinguishing PNET from benign tumors. In quantitative elastography, the values of SR are between malignant and benign deposits of the pancreas, one of the reasons for such values may be the diversity of this group of diseases with different mitotic activity – grade of the tumor. Conclusion: Consistency of the results published by us, shows the applicability of this method in deciding on a definitive diagnosis if tumor histology is not and cannot be available. Key words: neuroendocrine tumor – pancreas – ultrasonography – elastography

Application of spectral CT combined with perfusion scan in diagnosis of pancreatic neuroendocrine tumors

BackgroundPancreatic neuroendocrine tumors (pNETs) are heterogeneous tumors from the pancreatic neuroendocrine system, and early diagnosis is important for tumor prognosis and treatment. In this study, we aimed to explore the diagnostic value of spectral CT combined with perfusion scanning in improving the detection rate of pNETs.MethodsFrom December 2018 to December 2020, 58 patients with clinically suspected pNETs were prospectively enrolled in the study for one-stop spectral CT combined with perfusion scanning, 36 patients were confirmed with pNETs by histopathology. An independent cohort of 30 patients with pNETs who underwent routine pancreatic perfusion scanning in our hospital during the same period were retrospectively collected. The image characters of pNETs versus tumor-free pancreatic parenchymal were examined.ResultsThe detection rate of spectral CT combined with perfusion was 83.1–96.2%. CT values of the pNETs lesions under each single energy in the arterial phase were statistically higher than those of the adjacent normal pancreatic parenchyma. IC, WC and NIC, in the arterial phase of pNETs lesion were all statistically higher than those of the adjacent normal pancreatic parenchyma. The perfusion parameters of pNETs including BF, BV and MSI were significantly higher than those in normal parenchyma. The average effective radiation dose during the perfusion combined energy spectrum enhanced scanning process was 17.51 ± 2.18 mSv.ConclusionThe one-stop spectral CT combined with perfusion scan improves the detection of pNETs according to morphological features, perfusion parameters and energy spectrum characters with a relatively small radiation dose.

Plasma protein biomarkers for the detection of pancreatic neuroendocrine tumors and differentiation from small intestinal neuroendocrine tumors.

There is an unmet need for novel biomarkers to diagnose and monitor patients with neuroendocrine neoplasms. The EXPLAIN study explores a multi-plasma protein and supervised machine learning strategy to improve the diagnosis of pancreatic neuroendocrine tumors (PanNET) and differentiate them from small intestinal neuroendocrine tumors (SI-NET). At time of diagnosis, blood samples were collected and analyzed from 39 patients with PanNET, 135 with SI-NET (World Health Organization Grade 1-2) and 144 controls. Exclusion criteria were other malignant diseases, chronic inflammatory diseases, reduced kidney or liver function. Prosed Oncology-II (i.e., OLink) was used to measure 92 cancer related plasma proteins. Chromogranin A was analyzed separately. Median age in all groups was 65-67 years and with a similar sex distribution (females: PanNET, 51%; SI-NET, 42%; controls, 42%). Tumor grade (G1/G2): PanNET, 39/61%; SI-NET, 46/54%. Patients with liver metastases: PanNET, 78%; SI-NET, 63%. The classification model of PanNET versus controls provided a sensitivity (SEN) of 0.84, specificity (SPE) 0.98, positive predictive value (PPV) of 0.92 and negative predictive value (NPV) of 0.95, and area under the receiver operating characteristic curve (AUROC) of 0.99; the model for the discrimination of PanNET versus SI-NET providing a SEN 0.61, SPE 0.96, PPV 0.83, NPV 0.90 and AUROC 0.98. These results suggest that a multi-plasma protein strategy can significantly improve diagnostic accuracy of PanNET and SI-NET.

Role of Endoscopic Ultrasound in the Diagnosis of Pancreatic Neuroendocrine Neoplasms.

Although pancreatic neuroendocrine neoplasms (PNENs) are relatively rare tumors, their number is increasing with advances in diagnostic imaging modalities. Even small lesions that are difficult to detect using computed tomography or magnetic resonance imaging can now be detected with endoscopic ultrasound (EUS). Contrast-enhanced EUS is useful, and not only diagnosis but also malignancy detection has become possible by evaluating the vascularity of tumors. Pathological diagnosis using EUS with fine-needle aspiration (EUS-FNA) is useful when diagnostic imaging is difficult. EUS-FNA can also be used to evaluate the grade of malignancy. Pooling the data of the studies that compared the PNENs grading between EUS-FNA samples and surgical specimens showed a concordance rate of 77.5% (κ-statistic = 0.65, 95% confidence interval = 0.59–0.71, p < 0.01). Stratified analysis for small tumor size (2 cm) showed that the concordance rate was 84.5% and the kappa correlation index was 0.59 (95% confidence interval = 0.43–0.74, p < 0.01). The evolution of ultrasound imaging technologies such as contrast-enhanced and elastography and the artificial intelligence that analyzes them, the evolution of needles, and genetic analysis, will further develop the diagnosis and treatment of PNENs in the future.

Diagnostic Role and Therapeutic Perspectives of Endoscopic Ultrasound for Pancreatic Neuroendocrine Tumor

Pancreatic neuroendocrine tumors (PNETs) are rare tumors with malignant potential, but their incidence has appreciably increased over the last few decades. Diagnosis of PNETs is often difficult with conventional imaging methods. Cross-sectional imaging localizes less than 10% of PNETs less than 1 cm in diameter. Endoscopic ultrasound (EUS) has been shown to be superior to other imaging techniques in the preoperative localization and diagnosis of PNETs. The finding of a hyperenhanced lesion on contrastenhanced EUS was highly predictive of PNETs different from adenocarcinoma. Preoperative assessment of tumor differentiation and Ki-67 is an important prognostic factor for grading. Thus, EUS-guided fine needle biopsy may play a key role in the work-up of nonfunctioning PNETs, and attempts to measure Ki-67 on cytologic and histologic samples have been made. According to the European Neuroendocrine Tumor Society guidelines, there is no clear evidence of a survival benefit of surgery for nonfunctioning G1 PNETs &lt;2 cm in diameter. The benefits of curative surgery must be weighed against operative morbidity and mortality. Although surgical resection remains the mainstay for PNETs, EUS-guided ablation offers a relatively safe and effective treatment option in patients deemed unfit for, or who do not want to undergo, surgery. EUS-guided ablation provides an attractive therapeutic nonsurgical option for patients; however, further studies that better elucidate the long-term outcomes with standardization of technique will help define its role in the treatment of PNETs. The role of EUS in the diagnosis and treatment of PNETs is expected to make much progress in the future.

Primary pancreatic glomus tumor invading into the superior mesenteric vein: a case report.

BackgroundGlomus tumors are subcutaneous tumors arising from glomus bodies, thermoregulatory components of the skin. These tumors could occur in visceral organs where glomus bodies are not normally present. Herein, we report a case of primary pancreatic glomus tumor with aggressive direct invasion into the superior mesenteric vein (SMV). To the best of our knowledge, this is the second case report of a glomus tumor arising in the pancreas.Case presentationA 46-year-old woman was referred to our hospital due to vomiting with epigastric and back pain. Dynamic-CT revealed a well-circumscribed hypervascular mass, measuring 37 mm in its maximal diameter involving the pancreatic head. Both CT and endoscopic ultrasonography (EUS) revealed direct invasion into the SMV and radiologically suspected tumor thrombus. Biopsy sample obtained by EUS-guided fine needle aspiration revealed proliferation of small cells, round-to-oval tumor cells with round nuclei and scant cytoplasm. A histological diagnosis of pancreatic neuroendocrine tumor, G1 was initially considered. Therefore, subtotal stomach-preserving pancreatoduodenectomy using Child-II reconstruction was subsequently performed. Her SMV was resected and reconstructed due to extensive tumor involvement. Subsequent histopathological analysis revealed solid tumor cells proliferation that comprised oval-shaped nuclei and scant cytoplasm around disorganized or slit-shaped vessels in hematoxylin–eosin-stained slides. Immunohistochemical analysis then demonstrated positive immunoreactivity for smooth muscle actin, vimentin, and CD34, but negative for chromogranin A, synaptophysin, CD56, and signal transducer and activator of transcription 6. Based on these histological findings of resected specimens, the lesion was subsequently diagnosed as a primary pancreatic glomus tumor harboring direct invasion into the SMV. Her postoperative course was uneventful and annual surveys for the following 4 years post-op detected no clinical signs of recurrence.ConclusionsWe report a very rare case of glomus tumor of the pancreas accompanied by venous invasion. Curative surgical resection is the best treatment option for pancreatic glomus tumors. Although pancreatic glomus tumor is rare, it should be taken into consideration in the differential diagnosis of a pancreatic solid tumor with hypervascularity.

Application of low dose pancreas perfusion CT combined with enhancement scanning in diagnosis of pancreatic neuroendocrine tumors

PurposeTo explore the diagnostic value of pancreatic perfusion CT combined with contrast-enhanced CT in one-time scanning (PCECT) in pancreatic neuroendocrine tumors (PNETs) and to evaluate the difference of perfusion parameters between different grades of PNETs. Materials and methodsFrom October 2016 to December 2018, forty consecutive patients with histopathological-proven PNETs were identified retrospectively that received PCECT for the preoperative PNETs evaluation. Two board certified radiologists who were blinded to the clinical data evaluated the images independently. The image characters of PNETs vs. tumor-free pancreatic parenchymal and different grades of PNETs were analyzed. ResultsOne-time PCECT scanning had a detection rate of 89.1% for PNETs, which was higher than the detection accuracy of the perfusion CT only (83.6%). The perfusion parameters of PNETs including blood volume (BV), blood flow (BF), mean slope of increase (MSI), and capillary surface permeability (PS) were significantly increased than those of tumor-free pancreatic parenchyma (p < 0.05, respectively). For differential comparison between grade I (G1) and grade II (G2) tumors, the parameters of BF and impulse residue function (IRF) of tumor tissue were significantly higher in the G2 tumors (p < 0.05, for both). In this study, the total radiation dose of the whole PCECT scan was 16.241 ± 2.289 mSv. ConclusionThe one-time PCECT scan may improve the detection of PNETs according to morphological features and perfusion parameters with a relative small radiation dose. The perfusion parameters of BF and IRF may be used to help distinguish G1 and G2 tumors in the preoperative evaluation.

Chromogranin A: A Valuable Serum Diagnostic Marker for Non-Insulinoma Neuroendocrine Tumors of the Pancreas in a Chinese Population.

BackgroundPancreatic neuroendocrine tumors (P-NETs) are uncommon neoplasms, with few studies to date assessing serum biomarkers for the diagnosis of P-NETs. This study assessed the ability of serum chromogranin A (CgA) concentrations to distinguish P-NETs from other pancreatic lesions in a Chinese population and to determine the histological grades of P-NETs.Material/MethodsThis prospective study enrolled 165 patients, including 73 with proven P-NETs, 60 with malignant tumors of the pancreas, and 32 with benign lesions of the pancreas. Serum CgA concentrations were measured by ELISA.ResultsSerum CgA concentrations were significantly higher in patients with P-NET than in patients with other pancreatic malignancies and benign lesions (P<0.001), but did not differ significantly in the latter 2 groups (P=0.827). Serum CgA concentrations were significantly higher in patients with non-insulinoma P-NETs than in the other groups (P<0.001), but did not differ significantly in patients with insulinoma and patients with non-P-NETs (P=0.668). Receiver operating characteristic (ROC) curves revealed that a serum CgA concentration of 77.8 ng/ml could distinguish patients with non-insulinoma P-NETs from patients with non-P-NETs, with a sensitivity of 96.7%, a specificity of 76.1%, and an area under the ROC curve of 0.897. In patients with P-NETs, multifactor analysis showed that the non-insulinoma subtype and the presence of liver metastases were associated with elevated serum CgA (both p<0.001).ConclusionsSerum CgA concentration may be a valuable diagnostic biomarker for non-insulinoma P-NETs. Elevated serum CgA is likely associated with liver metastases.