Grading Of Pancreatic Neuroendocrine Tumors Research Articles

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.

Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

PurposeTo compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC). Materials and methodsPatients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared. ResultsThirty-seven patients (21 men, 16 women; mean age, 56±13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60±46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70±51 [SD] mm [range: 18 - 196mm] vs. 42±24 [SD] mm [range: 8 - 94mm], respectively; P=0.039), with more tumor necrosis (75% vs. 33%, respectively; P=0.030) and lower attenuation on precontrast (30±4 [SD] HU [range: 25-39 HU] vs. 37±6 [SD] [range: 25-45 HU], respectively; P=0.002) and on portal venous phase CT images (75±18 [SD] HU [range: 43 - 108 HU] vs. 92±19 [SD] HU [range: 46 - 117 HU], respectively; P=0.014). Hemorrhagic content on MRI was only observed in NEC (P=0.007). The mean ADC value was lower in NEC ([1.1±0.1 (SD)]×10−3 mm2/s [range: (0.91 - 1.3)×10−3 mm2/s] vs. [1.4±0.2 (SD)]×10−3 mm2/s [range: (1.1 - 1.6)×10−3 mm2/s]; P=0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7±0.2 [SD] [range: 4.2-5.1] vs. 4.5±0.4 [SD] [range: 3.7-4.9]; P=0.023). ConclusionPancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.

Endoscopic ultrasound-guided fine-needle aspiration for the diagnosis and grading of pancreatic neuroendocrine tumors: a retrospective analysis of 110 cases.

Data on the reliability of the Ki-67 index and grading calculations from endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) of pancreatic neuroendocrine tumors (PanNETs) are controversial. We aimed to assess the accuracy of these data compared with histology. Cytological analysis from EUS-FNA in patients with suspected PanNETs (n = 110) were compared with resection samples at a single institution. A minimum of 2000 cells were considered to be adequate for grading. Correlation and agreement between cytology and histology in grading and Ki-67 values, respectively, were investigated. Secondary outcomes included the diagnostic performance of EUS-FNA. EUS-FNA samples were adequate for PanNET diagnosis and PanNET grading in 98/110 (89.1 %) and 77/110 (70.0 %) patients, respectively; thus, 77 samples were adequate for comparing cytology vs. histology. There were 67 (62.0 %), 40 (36.4 %), and 1 (0.9 %) patients with a final diagnosis of G1, G2, and G3 tumors, respectively. EUS-FNA grading was concordant with surgical pathology in 81.8 % of patients; under- and overgrading occurred in 15.6 % and 2.6 %, respectively. The overall level of agreement for grading was moderate (Cohen's κ = 0.59, 95 % confidence interval [CI] 0.34 - 0.78). Spearman's rho for Ki-67 in tumors ≤ 20 mm and > 20 mm was strong and moderate, respectively (rho = 0.68, 95 %CI 0.47 - 0.83; rho = 0.59, 95 %CI 0.35 - 0.75). The Bland - Altman plot showed that the Ki-67 values were comparable and reproducible between the two measurements. Although they were not available for a significant number of patients, grading and Ki-67 values from cytology correlated with histology moderately to strongly.

Dual-color immunocytochemistry (Ki-67 with LCA) for precise grading of pancreatic neuroendocrine tumors with applicability to small biopsies and cell blocks.

Ki-67 (MIB-1) immunostaining to quantify the proliferative index of neuroendocrine tumors (NETs) has been recommended (especially for small biopsies). However, this has a number of challenges with nonrepresentative Ki-67 index due to interference by Ki-67 immunoreactive proliferating lymphocytes infiltrating the tumor and also some proliferating stromal cells including endothelial cells in the background. Our pilot project showed that dual-color immunostaining with inclusion of leukocyte common antigen (LCA) (Ki-67: nuclear brown; LCA: cytoplasmic red) can facilitate the weeding out of lymphocyte interference. We analyzed the results with 23 surgical cases of pancreatic NETs. This was followed by poststudy examination of 11 cases of endoscopic ultrasound-guided fine-needle aspiration of the pancreatic NETs (PanNETs) to evaluate the findings of the study. Dual-color immunostaining for Ki-67 with LCA increased the precision of quantifying Ki-67 index, due to ability to exclude LCA immunoreactive lymphocytes. Other nontumor Ki-67 immunoreactive cells such as endothelial and stromal cells could be distinguished morphologically. Digital methods were also attempted, but this approach could not distinguish infiltrating lymphocytes and other cells in sections resulting in erroneous results. This study demonstrated that grading of PanNET can be performed with increased precision with dual-color Ki-67 immunostaining protocol standardized in this study. As evaluated on a few cytopathology cases, this protocol is especially useful for the evaluation of small biopsies and cell block sections of fine-needle aspiration biopsy material where 50 high-power fields cannot be evaluated but have >500 tumor cell nuclei.

CT-Radiomic Approach to Predict G1/2 Nonfunctional Pancreatic Neuroendocrine Tumor

Tumor grading of nonfunctional pancreatic neuroendocrine tumors (NF-pNETs) determines the choice of clinical treatment and management. The pathological grade of pancreatic neuroendocrine tumors is usually assessed on postoperative specimens. The goal of our study is to establish a tumor grade (G) prediction model for preoperative G1/2 NF-pNETs using radiomics for multislice spiral CT image analysis. This retrospective study included a primary cohort of 59 patients and an independent validation cohort of 40 consecutive patients; their multislice spiral CT images were collected from October 2012 to October 2016 and October 2016 to June 2018, respectively. All 99 patients were diagnosed with clinicopathologically confirmed NF-pNETs. Most significant radiomic features were selected using the minimum redundancy and maximum relevance algorithm. Support vector machine classifier with a radial basis function-based predictive model was subsequently developed for clinical use. A total of 585 radiomics features were extracted from every phase for each patient. Six of these radiomics features were identified as most discriminant features for G1 and G2 tumors and used to construct the tumor grade prediction model. The prediction model resulted in the area under the curve values of 0.968 (95% CI: 0.900-0.991) and 0.876 (95% CI: 0.700-0.963) for the training cohort and validation cohort, respectively. Sensitivity and specificity were 96.4% and 83.9%, and 90.9% and 88.9% for the training and validation cohorts, respectively. The decision curves indicated that if the threshold probability is above 0.1, using the rad-score in the current study on G1/2 NF-pNETs is more beneficial than the treat-all-patients scheme or the treat-none scheme. Radiomics developed with a combination of nonenhanced and portal venous phases can achieve favorable predictive accuracy for histological grade for G1/G2 NF-pNETs.

A rare case of pancreatic neuroendocrine tumor

Pancreatic neuroendocrine tumors (PNETs) are a group of endocrine neoplasms that exhibit neuroendocrine phenotypes. These include the production of neuropeptides, large dense-core secretory vesicles, and a lack of neural structures. They arise in the pancreas and are among the most common neuroendocrine tumors. A case of 38 years old female presented with a painless progressive swelling in the right upper abdomen since one month which is insidious in onset, gradually increasing in size with a history of significant weight loss and patient is not a known diabetic. On Examination, a swelling of size 5x7cm noted involving epigastric and right hypochondrium, which is not moving with respiration and dull on percussion. Plane of swelling is Intra-abdominal& retroperitoneal. Computed tomography abdomen (CECT) showed well defined rounded iso dense lesion showing heterogeneous enhancement in arterial and venous phase. Magnetic resonance cholangiopancreatography (MRCP) showed well defined lobulated heterogeneous solid lesion with central areas of necrosis seen in the pancreaticoduodenal groove (PDG) involving main pancreatic duct (MPD) and loss of fat planes with surrounding structures most likely malignant etiology. The patient underwent pancreaticoduodenectomy surgery (Whipple procedure) and had an uneventful recovery. Post-operative biopsy report confirmed pancreatic neuroendocrine tumor grade II (well-differentiated type), stage II a – pT3 N0 Mx.

Accuracy of Ki-67 Index Obtained from Fine-Needle Aspiration Specimens in the Diagnosis and Grading of Pancreatic Neuroendocrine Tumors

Livingston, Austin; Strong, Erin MD, MBA, MPH; Christians, Kathleen MD, FACS; Dua, Kulwinder S. MD; Hagen, Catherine MD; Evans, Douglas B. MD, FACS; Clarke, Callisia N. MD, MS, FACS Author Information

Deep learning for World Health Organization grades of pancreatic neuroendocrine tumors on contrast-enhanced magnetic resonance images: a preliminary study.

The World Health Organization (WHO) grading system of pancreatic neuroendocrine tumor (PNET) plays an important role in the clinical decision. The rarity of PNET often negatively affects the radiological application of deep learning algorithms due to the low availability of radiological images. We tried to investigate the feasibility of predicting WHO grades of PNET on contrast-enhanced magnetic resonance (MR) images by deep learning algorithms. Ninety-six patients with PNET underwent preoperative contrast-enhanced MR imaging. Fivefold cross-validation was used in which five iterations of training and validation were performed. Within every iteration, on the training set augmented by synthetic images generated from generative adversarial network (GAN), a convolutional neural network (CNN) was trained and its performance was evaluated on the paired internal validation set. Finally, the trained CNNs from cross-validation and their averaged counterpart were separately assessed on another ten patients from a different external validation set. Averaging the results across the five iterations in the cross-validation, for the CNN model, the average accuracy was 85.13% ± 0.44% and micro-average AUC was 0.9117 ± 0.0053. Evaluated on the external validation set, the average accuracy of the five trained CNNs ranges between 79.08 and 82.35%, and the range of micro-average AUC was between 0.8825 and 0.8932. The average accuracy and micro-average AUC of the averaged CNN were 81.05% and 0.8847, respectively. Synthetic images generated from GAN could be used to alleviate the difficulty of radiological image collection for uncommon disease like PNET. With the help of GAN, the CNN showed the potential to predict the WHO grades of PNET on contrast-enhanced MR images.

A Clinicopathologic and Molecular Update of Pancreatic Neuroendocrine Neoplasms With a Focus on the New World Health Organization Classification.

According to the 2017 World Health Organization classification, pancreatic neuroendocrine neoplasms (PanNENs) include a new category of pancreatic neuroendocrine tumor, grade 3, which is often difficult to differentiate from pancreatic neuroendocrine carcinoma. However, pancreatic neuroendocrine tumor grade 3 and pancreatic neuroendocrine carcinoma are distinct entities with very different clinical presentation, prognosis, and therapeutic strategies. Recent discoveries on the molecular characteristics of pancreatic neuroendocrine tumors also play an essential role in the pathologic differential diagnosis of PanNENs. In addition, the histopathologic varieties of PanNENs bring in many differential diagnoses with other pancreatic neoplasms, especially acinar cell carcinoma, solid pseudopapillary neoplasm, and ductal adenocarcinoma. To provide a brief update of the World Health Organization classification; the clinical, histopathologic, immunohistochemical, and molecular characteristics; and the differential diagnoses and biological behavior of PanNENs. Analysis of the pertinent literature (PubMed) and authors' clinical practice experience based on institutional and consultation materials. The evolving clinical, histopathologic, immunohistochemical, and molecular features of PanNENs are reviewed. Important differential diagnoses with other neoplasms of the pancreas are discussed.

Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumor grades using biochemical and tumor markers.

BACKGROUNDThe incidence of pancreatic neuroendocrine tumors (PNETs) is now increasing rapidly. The tumor grade of PNETs significantly affects the treatment strategy and prognosis. However, there is still no effective way to non-invasively classify PNET grades. Machine learning (ML) algorithms have shown potential in improving the prediction accuracy using comprehensive data.AIMTo provide a ML approach to predict PNET tumor grade using clinical data.METHODSThe clinical data of histologically confirmed PNET cases between 2012 and 2018 were collected. A method of minimum P for the Chi-square test was used to divide the continuous variables into binary variables. The continuous variables were transformed into binary variables according to the cutoff value, while the P value was minimum. Four classical supervised ML models, including logistic regression, support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) were trained by clinical data, and the models were labeled with the pathological tumor grade of each PNET patient. The performance of each model, including the weight of the different parameters, were evaluated.RESULTSIn total, 91 PNET cases were included in this study, in which 32 were G1, 48 were G2 and 11 were G3. The results showed that there were significant differences among the clinical parameters of patients with different grades. Patients with higher grades tended to have higher values of total bilirubin, alpha fetoprotein, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 72-4. Among the models we used, LDA performed best in predicting the PNET tumor grade. Meanwhile, MLP had the highest recall rate for G3 cases. All of the models stabilized when the sample size was over 70 percent of the total, except for SVM. Different parameters varied in affecting the outcomes of the models. Overall, alanine transaminase, total bilirubin, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 72-4 affected the outcome greater than other parameters.CONCLUSIONML could be a simple and effective method in non-invasively predicting PNET grades by using the routine data obtained from the results of biochemical and tumor markers.

CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study.

To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs). One hundred thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use. The fusion radiomic signature has significant association with histologic grade (p < 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950-0.998) in the training cohort and 0.902 (95% CI 0.798-1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram. We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients. • Radiomic signature has strong discriminatory ability for the histologic grade of PNETs. • Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading. • The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.

Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance.

PurposeThe purpose of this study was to evaluate the performance of magnetic resonance imaging (MRI) findings and texture parameters for prediction of the histopathologic grade of pancreatic neuroendocrine tumors (PNETs) with 3-T magnetic resonance.Patients and methodsPNETs are classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity. A total of 77 patients with pathologically confirmed PNETs met the inclusion criteria. Texture analysis (TA) was applied to T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) maps. Patient demographics, MRI findings, and texture parameters were compared among three different histopathologic subtypes by using Fisher’s exact tests or Kruskal–Wallis test. Then, logistic regression analysis was adopted to predict tumor grades. ROC curves and AUCs were calculated to assess the diagnostic performance of MRI findings and texture parameters in prediction of tumor grades.ResultsThere were 31 G1, 29 G2, and 17 G3 patients. Compared with G1, G2/G3 tumors showed higher frequencies of an ill-defined margin, a predominantly solid tumor type, local invasion or metastases, hypo-enhancement at the arterial phase, and restriction diffusion. Four T2-based (inverse difference moment, energy, correlation, and differenceEntropy) and five DWI-based (correlation, contrast, inverse difference moment, maxintensity, and entropy) TA parameters exhibited statistical significance among PNETs (P<0.001). The AUCs of six predicting models on T2WI and DWI ranged from 0.703–0.989.ConclusionOur data indicate that MRI findings, including tumor margin, texture, local invasion or metastases, tumor enhancement, and diffusion restriction, as well as texture parameters can aid the prediction of PNETs grading.

Diagnostic Performance of Apparent Diffusion Coefficient for Prediction of Grading of Pancreatic Neuroendocrine Tumors: A Systematic Review and Meta-analysis.

The aim of this study was to evaluate the diagnostic value of apparent diffusion coefficient (ADC) for the World Health Organization grade of pancreatic neuroendocrine tumors (pNETs). The MEDLINE, Google Scholar, PubMed, and Embase databases were searched to identify relevant original articles investigating the ADC value in predicting the grade of pNETs. The pooled sensitivity (SE), specificity (SP), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated by using random effects models. Subgroup analysis was performed to discover heterogeneity effects. Nine studies with 386 patients met our inclusion criteria. For identifying G1 from G2/3, the pooled SE, SP, PLR, NLR, and area under the curve of the summary receiver operating characteristic curve were 0.84 (95% confidence interval [95% CI], 0.73-0.91), 0.87 (95% CI, 0.72-0.94), 6.3 (95% CI, 2.7-14.6), 0.19 (95% CI, 0.10-0.34), and 0.91 (95% CI, 0.89-0.94), respectively. The summary estimates for ADC in distinguishing G3 from G1/2 were as follows: SE, 0.93 (95% CI, 0.66-0.99); SP, 0.92 (95% CI, 0.86-0.95); PLR, 11.1 (95% CI, 6.6-18.6); NLR, 0.08 (95% CI, 0.01-0.45); and area under the curve, 0.92 (95% CI, 0.85-0.96). Diffusion-weighted imaging is a reliable tool for predicting the grade of pNETs, especially for G3. Moreover, the combination of 3.0-T device and higher b value can slightly help improve SE and SP.

Simple Vascular Architecture Classification in Predicting Pancreatic Neuroendocrine Tumor Grade and Prognosis.

Vascularity is a critical feature in the evaluation of pancreatic neuroendocrine tumor (PNET). When done by EUS, contrast agents are recommended. However, vascular architecture (VA) can also be evaluated by routine Doppler flow in EUS without contrast agents. Our aim was to provide a simple VA classification in EUS for PNET grade and prognosis. All pathologically proven PNET cases with EUS between 2012 and 2018 were retrospectively analyzed. The Doppler imaging was retrieved for VA classification. Predictive model construction was performed by machine learning algorithms. A total of 112 PNET cases were evaluated, among which 93 cases were subjected to VA classification. The VA was classified into type A (peritumoral with or without intratumoral vessels [A1 or A2]); type B (only intratumoral vessels); and type C (flow was absent). The VA classification was significantly correlated with tumor grades: 74% type A1 was G1, 73% type B was G2, and 58% type C was G3. Multivariate analysis indicated that elevated serum CA19-9 and type C classification were the independent predictors of G3 tumor. Five machine learning models were constructed, among which random forest was the best one with an AUC of 0.9972. Low-risk patients classified by this model exhibited better prognosis than high-risk patients (p = 0.0087). In the novel simple VA classification, peritumoral, intratumoral, and absent vessels are prone to be G1, G2, and G3, respectively. Combined with serum CA19-9 and lesion size, the VA classification could predict tumor grade and prognosis in PNET.

Ki67 Scoring in Pancreatic Neuroendocrine Tumors By a New Method.

Ki67 scoring is required for the grading of pancreatic neuroendocrine tumors. Ongoing debate exists about the best scoring method in terms of accuracy and practicality. Manual counting of cells in camera-captured/printed images is a widely used and accepted method and considered the most reliable one among the manual methods. It requires counting 500 to 2000 cells to determine the Ki67 score accurately and it is time and energy consuming. We investigated the possibility of achieving the same results by counting only a particular fraction of tumor cells in a printed image in a series of 45 (24 grade 1 and 21 grade 2) pancreatic neuroendocrine tumors. After counting Ki67-positive tumor cells in the whole image, the tumor cells were counted within one-tenth of the same image with the aid of a previously prepared grid on an acetate sheet. The cell number obtained was multiplied by 10 to estimate the total cell count and the Ki67 score was calculated. The agreement between the results of the acetate grid and conventional whole-image counting method was assessed. Near-perfect agreement was achieved regarding the total cell count and Ki67 score. The agreement on tumor grade between the two methods was perfect. The time spent on the process was significantly less than that spent on the conventional method. Although it needs to be validated in a larger series, the acetate grid method might be considered an alternative method for Ki67 scoring in neuroendocrine tumors.

The diagnostic value of FNA biopsy in grading pancreatic neuroendocrine tumors.

There has been much controversy regarding the accuracy of grading pancreatic neuroendocrine tumors (PNETs) on fine-needle aspiration (FNA) biopsy. The objectives of this study were to evaluate whether grading according to the fraction of Ki-67-positive tumor cells (the Ki-67 proliferation index) on material from endoscopic ultrasound-guided FNA biopsies correlated with grading on surgical resection specimens and to evaluate the minimum amount of FNA material needed. A case series of 27 PNETs with FNA biopsies and corresponding surgical resection specimens at the authors' institution were evaluated. Tumors were graded on FNA and surgical specimens with an evaluation of Ki-67 index according to 2010 World Health Organization criteria. Chart reviews were conducted to evaluate recurrence or clinical progression in patients who were being managed conservatively with observation. The evaluation of grading between FNA and tumor resection specimens revealed that 22 of 26 FNA specimens (84.6%) had Ki-67 results comparable to those in the corresponding surgical resection specimens, thus allowing for accurate grading. Correct FNA diagnosis with the ability to distinguish between grade 1 and 2 tumors had a positive predictive value of 88.9%, with 72.7% sensitivity, 93.3% specificity, and a P value of .00081. In addition, 24 of 26 cases contained less than 2000 cells, of which 20 were correctly graded on FNA material. Seven of 26 FNA samples had less than 1000 cells, of which 6 were correctly graded, including 2 that had only 50 cells. The current results exhibit good correlation between FNA grade and final grade on surgical resection specimens using Ki-67 index, even in samples with less than the recommended total cell count. Therefore, grading of PNETs on FNA with the Ki-67 proliferation index should be assessed and is a practical parameter to report to clinicians. Cancer Cytopathol 2018;126:170-8. © 2017 American Cancer Society.

Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis.

The purposes of this study were to assess whether CT texture analysis and CT features are predictive of pancreatic neuroendocrine tumor (PNET) grade based on the World Health Organization (WHO) classification and to identify features related to disease progression after surgery. Preoperative contrast-enhanced CT images of 101 patients with PNETs were assessed. The images were evaluated for tumor location, tumor size, tumor pattern, predominantly solid or cystic composition, presence of calcification, presence of heterogeneous enhancement on contrast-enhanced images, presence of pancreatic duct dilatation, presence of pancreatic atrophy, presence of vascular involvement by the tumor, and presence of lymphadenopathy. Texture features were also extracted from CT images. Surgically verified tumors were graded according to the WHO classification, and patients underwent CT or MRI follow-up after surgical resection. Data were analyzed with chi-square tests, kappa statistics, logistic regression analysis, and Kaplan-Meier curves. The CT features predictive of a more aggressive tumor (grades 2 and 3) were size larger than 2.0 cm (odds ratio [OR], 3.3; p = 0.014), presence of vascular involvement (OR, 25.2; p = 0.003), presence of pancreatic ductal dilatation (OR, 6.0; p = 0.002), and presence of lymphadenopathy (OR, 6.8; p = 0.002). The texture parameter entropy (OR, 3.7; p = 0.008) was also predictive of more aggressive tumors. Differences in progression-free survival distribution were found for grade 1 versus grades 2 and 3 tumors (χ2 [df, 1] = 21.6; p < 0.001); for PNETs with vascular involvement (χ2 [df, 1] = 20.8; p < 0.001); and for tumors with entropy (spatial scale filter 2) values greater than 4.65 (χ2 (df, 1) = 4.4; p = 0.037). CT texture analysis and CT features are predictive of PNET aggressiveness and can be used to identify patients at risk of early disease progression after surgical resection.

Concordance of Grading of Pancreatic Neuroendocrine Tumors Using Ki-67 on Cytology with Histologic Grade

Concordance of Grading of Pancreatic Neuroendocrine Tumors Using Ki-67 on Cytology with Histologic Grade

Pancreatic Neuroendocrine Tumors: Update on the New World Health Organization Classification

The first classification of the pancreatic neuroendocrine neoplasms (PanNENs) that characterized the individual tumor by criteria with prognostic significance appeared in 1995. These criteria included a distinction by site and size, by degree of histological differentiation (well vs poor), by function (with and without hormonal syndrome), by angioinvasion, and by metastatic spread. The subsequently developed World Health Organization classifications in 2000, 2004, and 2010 largely followed this concept, but added proliferative activity as the best criterion reflecting tumor growth. In 2010, the classification combined histological differentiation with stratification into 3 tiers of proliferative activity, mainly using Ki67 as the most reliable measure of proliferation. The predictive value of this classification and grading system that was soon accompanied by an adequate TNM staging system has proved to be so excellent that all major treatment options of PanNENs, as well as extrapancreatic NENs, have been currently based on this classification. The new World Health Organization 2017 classification is a refinement of the previous version. Its main change is the introduction of a “pancreatic neuroendocrine tumor grade 3” category to recognize grade-discordant pancreatic neuroendocrine tumors and distinguish them from pancreatic neuroendocrine carcinomas, which are defined by their poorly differentiated nature. There is increasing evidence that this phenotypical classification of PanNENs allows a targeted molecular analysis, which is going to broaden our understanding of the tumors' biology.

Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis.

Background Pancreatic neuroendocrine tumors (PNET) include heterogeneous tumors with a variable degree of inherent biologic aggressiveness represented by the histopathologic grade. Although several studies investigated the computed tomography (CT) characteristics which can predict the histopathologic grade of PNET, accurate prediction of the PNET grade by CT examination alone is still limited. Purpose To investigate the important CT findings and CT texture variables for prediction of grade of PNET. Material and Methods Sixty-six patients with pathologically confirmed PNETs (grade 1 = 45, grades 2/3 = 21) underwent preoperative contrast-enhanced CT. Two reviewers determined the presence of predefined CT findings. CT texture was also analyzed on arterial and portal phase using both two-dimensional (2D) and three-dimensional (3D) analysis. Multivariate logistic regression analysis was performed in order to identify significant predictors for tumor grade. Results Among CT findings and CT texture variables, the significant predictors for grade 2/3 tumors were an ill-defined margin (odds ratio [OR] = 7.273), lower sphericity (OR = 0.409) on arterial 2D analysis, higher skewness (OR = 1.972) and lower sphericity (OR = 0.408) on arterial 3D analysis, lower kurtosis (OR = 0.436) and lower sphericity (OR = 0.420) on portal 2D analysis, and a larger surface area (OR = 2.007) and lower sphericity (OR = 0.503) on portal 3D analysis ( P < 0.05). Diagnostic performance of texture analysis was superior to CT findings (AUC = 0.774 vs. 0.683). Conclusion CT is useful for predicting grade 2/3 PNET using not only the imaging findings including an ill-defined margin, but also the CT texture variables such as lower sphericity, higher skewness, and lower kurtosis.