Preoperative Identification of a Prognostic Factor for Pancreatic Neuroendocrine Tumors Using Multiphase Contrast-Enhanced Computed Tomography

Tumor vascularity on contrast-enhanced computed tomography as a predictive marker of metastatic potential for small nonfunctioning pancreatic neuroendocrine tumors

BACKGROUND

Although surgical resection is generally suggested for nonfunctioning pancreatic neuroendocrine tumors, observation can be proposed for carefully selected patients with small tumors. However, the indications for observation remain unclear.

METHODS

This retrospective study included 77 patients with nonfunctioning pancreatic neuroendocrine tumors, including small tumors (≤2.0 cm, n = 41), who received pancreatectomy. The ratio of the mean computed tomography value of a tumor in the late arterial/equilibrium phase (computed tomography a/e ratio) was used to evaluate tumor vascularity. Pathologic examinations of small tumors were conducted. The associations among the computed tomography a/e ratio, pathologic findings, and survival outcomes were investigated.

RESULTS

Small tumors were pathologically categorized by the degree of fibrosis as follows: medullary (n = 20), intermediate (n = 11), and fibrotic (n = 10). The fibrotic type had significantly lower computed tomography a/e ratios than the medullary type (median, 1.42 vs 2.03, P < .001). The median number of vessels with microscopic venous invasion was significantly higher in the fibrotic type than in the medullary type (4.5 vs 0.0, P < .001). The cutoff value of the computed tomography a/e ratio for predicting microscopic venous invasion was determined to be 1.54 by the receiver operating characteristic curve (area under the curve, 0.832; sensitivity, 80.0%; specificity, 83.9%; accuracy, 82.9%). Microscopic venous invasion was an independent prognostic factor for relapse-free survival in overall patients (hazard ratio 5.18, P = .017).

CONCLUSION

The computed tomography a/e ratio may be a useful predictor of the metastatic potential of nonfunctioning pancreatic neuroendocrine tumors and may help decide the indications of observation for small nonfunctioning pancreatic neuroendocrine tumors.

Advances in the imaging of gastroenteropancreatic neuroendocrine neoplasms

Gastroenteropancreatic neuroendocrine neoplasms comprise a heterogeneous group of tumors that differ in their pathogenesis, hormonal syndromes produced, biological behavior and consequently, in their requirement for and/or response to specific chemotherapeutic agents and molecular targeted therapies. Various imaging techniques are available for functional and morphological evaluation of these neoplasms and the selection of investigations performed in each patient should be customized to the clinical question. Also, with the increased availability of cross sectional imaging, these neoplasms are increasingly being detected incidentally in routine radiology practice. This article is a review of the various imaging modalities currently used in the evaluation of neuroendocrine neoplasms, along with a discussion of the role of advanced imaging techniques and a glimpse into the newer imaging horizons, mostly in the research stage.

Enucleation Is a Feasible Procedure for Well-Differentiated pNEN-A Matched Pair Analysis

The extent of surgical resection in the treatment of pancreatic neuroendocrine neoplasms (pNEN) is still controversial. This study aimed to evaluate the outcomes of enucleation for well-differentiated non-functional (nf) pNEN. Patients undergoing enucleation (2001−2020) were analyzed. Clinicopathological parameters, perioperative outcomes and survival were assessed. The analysis was performed as a nested case-control study and matched-pair analysis with formal resection. Sixty-one patients undergoing enucleation were identified. Compared to patients undergoing formal resection, enucleation was associated with a significantly shorter median length of operative time (128 (IQR 95−170) versus 263 (172−337) minutes, p < 0.0001) and a significantly lower rate of postoperative diabetes (2% versus 21%, p = 0.0020). There was no significant difference in postoperative pancreatic fistula rate (18% versus 16% type B/C, p = 1.0), Clavien−Dindo ≥ III complications (20% versus 26%, p = 0.5189), readmission rate (12% versus 15%, p = 0.6022) or length of hospital stay (8 (7−11) versus 10 (8−17) days, p = 0.0652). There was no 30-day mortality after enucleation compared to 1.6% (n = 1) after formal resection. 10-year overall survival (OS) and disease-free survival (DFS) was similar between the two groups (OS: 89% versus 77%, p = 0.2756; DFS: 98% versus 91%, p = 0.0873). Enucleation presents a safe surgical approach for well-differentiated nf-pNEN with good long-term outcomes for selected patients.

Preoperative Prediction of G1 and G2/3 Grades in Patients With Nonfunctional Pancreatic Neuroendocrine Tumors Using Multimodality Imaging

OBJECTIVES

We aimed to develop and validate a multimodality radiomics model for the preoperative prediction of nonfunctional pancreatic neuroendocrine tumor (NF-pNET) grade (G).

METHODS

This retrospective study assessed 123 patients with surgically resected, pathologically confirmed NF-pNETs who underwent multidetector computed tomography and MRI scans between December 2012 and May 2020. Radiomic features were extracted from multidetector computed tomography and MRI. Wilcoxon rank-sum test and Max-Relevance and Min-Redundancy tests were used to select the features. The linear discriminative analysis (LDA) was used to construct the four models including a clinical model, MRI radiomics model, computed tomography radiomics model, and mixed radiomics model. The performance of the models was assessed using a training cohort (82 patients) and a validation cohort (41 patients), and decision curve analysis was applied for clinical use.

RESULTS

We successfully constructed 4 models to predict the tumor grade of NF- pNETs. Model 4 combined 6 features of T2-weighted imaging radiomics features and 1 arterial-phase computed tomography radiomics feature, and showed better discrimination in the training cohort (AUC = 0.92) and validation cohort (AUC = 0.85) relative to the other models. In the decision curves, if the threshold probability was 0.07-0.87, the use of the radiomics score to distinguish NF-pNET G1 and G2/3 offered more benefit than did the use of a "treat all patients" or a "treat none" scheme in the training cohort of the MRI radiomics model.

CONCLUSION

The LDA classifier combining multimodality images may be a valuable noninvasive tool for distinguishing NF-pNET grades and avoid unnecessary surgery.

A New Preoperative Scoring System for Predicting Aggressiveness of Non-Functioning Pancreatic Neuroendocrine Neoplasms

The management of non-functioning pancreatic neuroendocrine neoplasms (NF-PanNENs) is still controversial. This study aimed to develop a new scoring system for treatment decisions at initial diagnosis based on the identification of the predictive factors for aggressive NF-PanNENs. Seventy-seven patients who had been pathologically diagnosed with NF-PanNENs were enrolled. We retrospectively reviewed 13 variables that could be assessed preoperatively. Univariate and multivariate stepwise logistic regression analyses were performed to identify factors for the aggressiveness of NF-PanNENs, and a scoring system was developed by assigning weighted points proportional to their β regression coefficient. Tumor size > 20 mm on contrast-enhanced computed tomography, tumor non-vascularity, and Ki-67 labeling index ≥5% on endoscopic ultrasound-guided fine-needle aspiration specimens were identified as independent factors for predicting the aggressiveness of NF-PanNENs. The new scoring system, developed using the identified factors, had an excellent discrimination ability, with area under the curve of 0.92 (95% CI, 0.85–0.99), and good calibration (p = 0.72, Hosmer-Lemeshow test). Ten-year overall survival rates in low-risk (0 point), intermediate-risk (1 to 2 points), and high-risk (3 to 4 points) groups were 100%, 90.9%, and 24.3%, respectively. This new scoring system would be useful for treatment decisions and prognostic prediction at initial diagnosis.

New Directions in Imaging Neuroendocrine Neoplasms

Purpose of Review

Accurate imaging is crucial for correct diagnosis, staging, and therapy of neuroendocrine neoplasms (NENs). The search for the optimal imaging technique has triggered rapid development in the field. This review aims at giving an overview on contemporary imaging methods and providing an outlook on current progresses.

Recent Findings

The discovery of molecular targets due to the overexpression of specific peptide hormone receptors on the NEN’s surface has triggered the development of multiple radionuclide imaging modalities. In addition to the established imaging technique of targeting somatostatin receptors, several alternative radioligands have been developed. Targeting the glucagon-like peptide-1 receptor by exendin-4 has a high sensitivity in localizing insulinomas. For dedifferentiated NENs, new molecular targets such as the C-X-C motif chemokine-receptor-4 have been evaluated. Other new targets involve the fibroblast activation protein and the cholecystokinin-2 receptors, where the ligand minigastrin opens new possibilities for the management of medullary thyroid carcinoma.

Summary

Molecular imaging is an emerging field that improves the management of NENs.

Prognostic significance of extracellular volume fraction with equilibrium contrast-enhanced computed tomography for pancreatic neuroendocrine neoplasms

BACKGROUND

/Objectives: Identifying reliable pretreatment imaging biomarkers for pancreatic neuroendocrine neoplasm (PanNEN) is a key imperative. Extracellular volume (ECV) fraction quantified with equilibrium contrast-enhanced CT can be easily integrated into routine examinations. This study aimed to determine whether ECV fraction with equilibrium contrast-enhanced computed tomography (CECT) could predict long-term outcomes in patients with PanNEN.

METHODS

This study was a retrospective observational study of 80 patients pathologically diagnosed with PanNEN at a single institution. ECV fraction of the primary lesion was calculated using region-of-interest measurement within PanNEN and the aorta on unenhanced and equilibrium CECT. The impact of clinical factors and tumor ECV fraction on progression-free survival (PFS) and overall survival (OS) was assessed with univariate and multivariate analyses using Cox proportional hazards models. The correlation between WHO classification and tumor ECV fraction was evaluated using Kendall rank correlation coefficients.

RESULTS

PFS and OS rates were estimated as 93.4% and 94.6%, 78.7% and 86.2%, 78.7% and 77.0%, and 78.7% and 66.6% at 1, 3, 5, and 10 years, respectively. Multivariate analysis revealed that Union for International Cancer Control (UICC) stage (hazard ratio [HR] = 3.95, P = 0.003), WHO classification (HR = 12.27, P = 0.003), and tumor ECV fraction (HR = 11.93, P = 0.039) were independent predictors of PFS. Patient age (HR = 1.11, P < 0.001), UICC stage (HR = 3.14, P = 0.001), and tumor ECV fraction (HR = 5.27, P = 0.024) were independent significant variables for predicting OS. Tumor ECV fraction had a weak inverse relationship with WHO classification (P = 0.045, τ = -0.178).

CONCLUSIONS

ECV fraction determined by equilibrium CECT and UICC stage may predict survival in patients with PanNEN.

Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images

Background

To establish and validate a prediction model for pancreatic neuroendocrine neoplasms (pNENs) recurrence after radical surgery with preoperative computed tomography (CT) images.

Methods

We retrospectively collected data from 74 patients with pathologically confirmed pNENs (internal group: 56 patients, Hospital I; external validation group: 18 patients, Hospital II). Using the internal group, models were trained with CT findings evaluated by radiologists, radiomics, and deep learning radiomics (DLR) to predict 5-year pNEN recurrence. Radiomics and DLR models were established for arterial (A), venous (V), and arterial and venous (A&V) contrast phases. The model with the optimal performance was further combined with clinical information, and all patients were divided into high- and low-risk groups to analyze survival with the Kaplan-Meier method.

Results

In the internal group, the areas under the curves (AUCs) of DLR-A, DLR-V, and DLR-A&V models were 0.80, 0.58, and 0.72, respectively. The corresponding radiomics AUCs were 0.74, 0.68, and 0.70. The AUC of the CT findings model was 0.53. The DLR-A model represented the optimum; added clinical information improved the AUC from 0.80 to 0.83. In the validation group, the AUCs of DLR-A, DLR-V, and DLR-A&V models were 0.77, 0.48, and 0.64, respectively, and those of radiomics-A, radiomics-V, and radiomics-A&V models were 0.56, 0.52, and 0.56, respectively. The AUC of the CT findings model was 0.52. In the validation group, the comparison between the DLR-A and the random models showed a trend of significant difference (P=0.058). Recurrence-free survival differed significantly between high- and low-risk groups (P=0.003).

Conclusions

Using DLR, we successfully established a preoperative recurrence prediction model for pNEN patients after radical surgery. This allows a risk evaluation of pNEN recurrence, optimizing clinical decision-making.

Magnetic resonance imaging radiomic analysis can preoperatively predict G1 and G2/3 grades in patients with NF-pNETs

PURPOSE

We aimed to explore the relationship between the magnetic resonance imaging (MRI) radiomic score (rad-score) and the grades of non-functioning pancreatic neuroendocrine tumors (NF-pNETs) and evaluate the potential of the calculated MRI rad-score to differentiate grade 1 from grade 2/3 NF-pNETs.

METHODS

This retrospective study assessed 157 patients with surgically resected, pathologically confirmed NF-pNETs who underwent magnetic resonance scans from November 2012 to December 2019. Radiomic features were extracted from arterial and portal venous MRI. The least absolute shrinkage and selection operator method were used to select the features. Multivariate logistic regression models were used to analyze the association between the MRI rad-score and NF-pNET grades. The MRI rad-score performance was assessed based on its discriminative ability and clinical usefulness.

RESULTS

The MRI rad-score, which consisted of seven selected features, was significantly associated with the NF-pNET grades. Every 1-point increase in the rad-score was associated with a 35% increased risk of grade 2/3 disease. The score also showed high accuracy (area under the curve = 0.775). The best cut-off point for maximal sensitivity and specificity was at 0.41. In the decision curves, when the threshold probability was higher than 0.3, the rad-score used in this study to distinguish grades 1 and 2/3 NF-pNETs offered more benefits than the use of a treat-all-patients or a treat-none scheme.

CONCLUSIONS

The MRI rad-score showed a significant association with the grades of NF-pNETs. Thus, it may be used as a valuable non-invasive tool for differential NF-pNET grading.

Correlation between radiologic features on contrast-enhanced CT and pathological tumor grades in pancreatic neuroendocrine neoplasms

Contrast-enhanced computed tomography (CT) contributes to the increasing detection of pancreatic neuroendocrine neoplasms (PNENs). Nevertheless, its value for differentiating pathological tumor grades is not well recognized. In this report, we have conducted a retrospective study on the relationship between the 2017 World Health Organization (WHO) classification and CT imaging features in 94 patients. Most of the investigated features eventually provided statistically significant indicators for discerning PNENs G3 from PNENs G1/G2, including tumor size, shape, margin, heterogeneity, intratumoral blood vessels, vascular invasion, enhancement pattern in both contrast phases, enhancement degree in both phases, tumor-to-pancreas contrast ratio in both phases, common bile duct dilatation, lymph node metastases, and liver metastases. Ill-defined tumor margin was an independent predictor for PNENs G3 with the highest area under the curve (AUC) of 0.906 in the multivariable logistic regression and receiver operating characteristic curve analysis. The portal enhancement ratio (PER) was shown the highest AUC of 0.855 in terms of quantitative features. Our data suggest that the traditional contrast-enhanced CT still plays a vital role in differentiation of tumor grades and heterogeneity analysis prior to treatment.

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

RATIONALE AND OBJECTIVES

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

Radiomics developed with a combination of nonenhanced and portal venous phases can achieve favorable predictive accuracy for histological grade for G1/G2 NF-pNETs.

Anatomic and Functional Imaging of Neuroendocrine Tumors

OPINION STATEMENT

Neuroendocrine tumors (NETs) can occur in a wide variety of organs and display a spectrum of pathologic behavior. Accurate and effective imaging is paramount to the diagnosis, staging, therapy, and surveillance of patients with NET. There have been continuous advancements in the imaging of NET which includes anatomic and functional techniques.

CT-Based Radiomics Score for Distinguishing Between Grade 1 and Grade 2 Nonfunctioning Pancreatic Neuroendocrine Tumors

OBJECTIVE. The objective of our study was to explore the relationship between a CT-based radiomics score and grade of nonfunctioning pancreatic neuroendocrine tumors (PNETs) and to evaluate the ability of a calculated CT radiomics score to distinguish between grade 1 and grade 2 nonfunctioning PNETs. MATERIALS AND METHODS. This retrospective study assessed 102 patients with surgically resected, pathologically confirmed nonfunctioning PNETs who underwent MDCT from January 2014 to December 2017. Radiomic methods were used to extract features from portal venous phase CT scans, and the least absolute shrinkage and selection operator (LASSO) method was used to select the features. Multivariate logistic regression models were used to analyze the association between the CT radiomics score and nonfunctioning PNET grades. The performance of the CT radiomics score was assessed on the basis of its discriminative ability and clinical usefulness. RESULTS. The CT radiomics score, which consisted of four selected features, was significantly associated with nonfunctioning PNET grades. Every 1-point increase in radiomics score was associated with a 57% increased risk of grade 2 disease. The score also showed high accuracy (AUC = 0.86 for all PNETs; AUC = 0.81 for PNETs ≤ 2 cm). The best cutoff point for maximal sensitivity and specificity was a CT radiomics score of -0.134. Decision curve analysis showed that the CT radiomics score is clinically useful. CONCLUSION. The CT radiomics score shows a significant association with the grade of nonfunctioning PNETs and provides a potentially valuable noninvasive tool for distinguishing between different grades of nonfunctioning PNET, especially among patients with tumors 2 cm or smaller.

Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors

BACKGROUND

Endoscopic ultrasound-guided fine-needle aspiration is associated with the accurate determination of tumor grade. However, because it is an invasive procedure there is a need to explore alternative noninvasive procedures.

PURPOSE

To develop and validate a noncontrast radiomics model for the preoperative prediction of nonfunctional pancreatic neuroendocrine tumor (NF-pNET) grade (G).

STUDY TYPE

Retrospective, single-center study.

SUBJECTS

Patients with pathologically confirmed PNETs (139) were included.

FIELD STRENGTH/SEQUENCE

3T/breath-hold single-shot fast-spin echo T₂ -weighted sequence and unenhanced and dynamic contrast-enhanced T₁ -weighted fat-suppressed sequences.

ASSESSMENT

Tumor features on contrast MR images were evaluated by three board-certified abdominal radiologists.

STATISTICAL TESTS

Multivariable logistic regression analysis was used to develop the clinical model. The least absolute shrinkage and selection operator method and linear discriminative analysis (LDA) were used to select the features and to construct a radiomics model. The performance of the models was assessed using the training cohort (97 patients) and the validation cohort (42 patients), and decision curve analysis (DCA) was applied for clinical use.

RESULTS

The clinical model included 14 imaging features, and the corresponding area under the curve (AUC) was 0.769 (95% confidence interval [CI], 0.675-0.863) in the training cohort and 0.729 (95% CI, 0.568-0.890) in the validation cohort. The LDA included 14 selected radiomics features that showed good discrimination-in the training cohort (AUC, 0.851; 95% CI, 0.758-0.916) and the validation cohort (AUC, 0.736; 95% CI, 0.518-0.874). In the decision curves, if the threshold probability was 0.17-0.84, using the radiomics score to distinguish NF-pNET G1 and G2/3, offered more benefit than did the use of a treat-all-patients or treat-none scheme.

DATA CONCLUSION

The developed radiomics model using noncontrast MRI could help differentiate G1 and G2/3 tumors, to make the clinical decision, and screen pNETs grade.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1124-1136.

CT Enhancement and 3D Texture Analysis of Pancreatic Neuroendocrine Neoplasms

To evaluate pancreatic neuroendocrine neoplasms (panNENs) grade prediction by means of qualitative and quantitative CT evaluation, and 3D CT-texture analysis. Patients with histopathologically-proven panNEN, availability of Ki67% values and pre-treatment CT were included. CT images were retrospectively reviewed, and qualitative and quantitative images analysis were done; for quantitative analysis four enhancement-ratios and three permeability-ratios were created. 3D CT-texture imaging analysis was done (Mean Value; Variance; Skewness; Kurtosis; Entropy). Subsequently, these features were compared among the three grading (G) groups. 304 patients affected by panNENs were considered, and 100 patients were included. At qualitative evaluation, frequency of irregular margins was significantly different between tumor G groups. At quantitative evaluation, for all ratios, comparisons resulted statistical significant different between G1 and G3 groups and between G2 and G3 groups. At 3D CT-texture analysis, Kurtosis resulted statistical significant different among three G groups and Entropy resulted statistical significant different between G1 and G3 and between G2 and G3 groups. Quantitative CT evaluation of panNENs can predict tumor grade, discerning G1 from G3 and G2 from G3 tumors. CT-texture analysis can predict panNENs tumor grade, distinguishing G1 from G3 and G2 from G3, and G1 from G2 tumors.

Differentiation of hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma using contrast-enhanced computed tomography

Hypovascular pancreatic neuroendocrine tumors (hypo-PNETs) are often misdiagnosed as pancreatic ductal adenocarcinoma (PDAC). However, the treatment options and prognosis of PNETs and PDAC are substantially different. This retrospective study differentiated hypo-PNETs from PDAC using contrast-enhanced CT (CE-CT). Clinical data and CE-CT findings, including tumor location, size, boundary, pancreatic duct dilatation, local invasion or metastases, tumor contrast enhancement, and tumor-to-pancreas enhancement ratio, were compared between 39 PDACs and 18 hypo-PNETs. At CT imaging, hypo-PNETs showed a higher frequency of a well-defined margin and lower frequencies of pancreatic duct dilatation and local invasion or metastasis when compared with PDAC (p < 0.05 for all). The mean attenuation of hypo-PNETs at the arterial and portal venous phase was significantly higher than that of PDAC (p < 0.001, p = 0.003, respectively). Similar results were observed in tumor-to-pancreas enhancement ratio. Tumor attenuation and tumor-to-pancreas enhancement ratio at the arterial phase showed the largest area under the curve (AUC) of 0.888 and 0.812 with 83.3–88.9% of sensitivity and 61.6–77.0% of specificity. Pancreatic duct dilatation, local invasion or metastasis, and tumor attenuation at the portal venous phase also showed acceptable AUC (0.703–0.748). Thus CE-CT features, especially the enhancement degree at the arterial phases, may be useful for differentiating hypo-PNETs from PDAC using CE-CT.

Imaging of pancreatic neuroendocrine tumors: recent advances, current status, and controversies

INTRODUCTION

Recently, there have been a number of advances in imaging pancreatic neuroendocrine tumors (panNETs), as well as other neuroendocrine tumors (NETs), which have had a profound effect on the management and treatment of these patients, but in some cases are also associated with controversies. Areas covered: These advances are the result of numerous studies attempting to better define the roles of both cross-sectional imaging, endoscopic ultrasound, with or without fine-needle aspiration, and molecular imaging in both sporadic and inherited panNET syndromes; the increased attempt to develop imaging parameters that correlate with tumor classification or have prognostic value; the rapidly increasing use of molecular imaging in these tumors and the attempt to develop imaging parameters that correlate with treatment/outcome results. Each of these areas and the associated controversies are reviewed. Expert commentary: There have been numerous advances in all aspects of the imaging of panNETs, as well as other NETs, in the last few years. The advances are leading to expanded roles of imaging in the management of these patients and the results being seen in panNETs/GI-NETs with these newer techniques are already being used in more common tumors.

Neuroendocrine neoplasm of pancreas with cystic degeneration mimicking mucinous cystic neoplasm

Endoscopic ultrasound is increasingly being used for evaluation of pancreatic diseases and pancreatic tumors. Among various pancreatic cystic lesions, cystic degeneration of pancreatic neuroendocrine neoplasm is of the challenge in making diagnosis. Although unique characteristic of each type of pancreatic cystic lesions has been proposed abundantly, typical morphology of cystic degeneration of pancreatic neuroendocrine neoplasm is still unclear. We, herein, reported a case of 66-year-old woman who was incidentally found to have a cystic lesion in the tail of pancreas upon screening transabdominal ultrasonography. A well-defined cystic lesion with rim calcification was noted on subsequent abdominal computed tomography. Endoscopic ultrasound revealed a markedly thick-wall cystic lesion containing solid nodule inside which was not enhanced following contrast-enhanced study. A mucinous cystic neoplasm was suspected and the patient was proceeded with distal pancreatectomy. A definite diagnosis of neuroendocrine neoplasm was confirmed after staining with synaptophysin and chromogranin A. We performed a meticulous review on current literatures focusing on endoscopic characteristics of pancreatic neuroendocrine neoplasms with cystic degeneration.

Association between pathologic grade and multiphase computed tomography enhancement in pancreatic neuroendocrine neoplasm

BACKGROUND AND AIM

Pancreatic neuroendocrine neoplasms (PanNENs) are rare diseases but gradually increasing in prevalence with different prognosis. Multiphase contrast-enhanced computed tomography (CT) is known as widely used imaging modality for the diagnosis of pancreatic tumors. We aimed to investigate whether CT enhancement pattern is associated with the pathologic tumor grade and can predict that of PanNEN.

METHODS

Ninety PanNEN patients who underwent multiphase enhanced CT before pathologic diagnosis were retrospectively reviewed. CT enhancement values at each phase were measured, and its relation with pathologic grade was assessed.

RESULTS

Ninety PanNENs included 62 G1 (68.9%), 21 G2 (23.3%), and 7 G3 (7.8%). The enhancement values of the early arterial phase were significantly different among three groups (G1 119.4 HU, G2 94.7 HU, and G3 64.8 HU; G1 vs G2, P = 0.043; G1 vs G3, P = 0.001; and G2 vs G3, P = 0.027). In the late arterial phase, there was a difference between grade 1/2 and 3 but no significant difference between grade 1 and grade 2 (G1 164.3 HU, G2 142.9 HU, and G3 94.1 HU; G1 vs G2, P = 0.804; G1 vs G3, P = 0.016; and G2 vs G3, P = 0.022). The enhancement value of the portal phase did not differ significantly among the three groups. Diagnostic ability of the early arterial enhancement value for the differentiation of the G1 (cutoff 109.5 HU, sensitivity 73.3%, and specificity 62.5%) was comparable with that of the tumor size (cutoff 20.5 mm, sensitivity 68.9%, and specificity 66.7%).

CONCLUSIONS

Computed tomography enhancement value at early arterial phase and its changing pattern can be a useful predictor for the differentiation of pathologic grade of PanNENs.

Pancreatic Neuroendocrine Tumors: Computed Tomography Enhancement, But Not Histological Grade, Correlates With Tumor Aggression

OBJECTIVES

The aims of this study were to assess computed tomography enhancement of pancreatic neuroendocrine tumors (NETs), determine correlation with histological vascularity and fibrosis, and identify a biomarker for tumor aggression.

METHODS

The arterial and venous enhancement of NET was calculated on computed tomography for 56 patients. Tumor size and vascularity/fibrosis were assessed. Tumor aggression was grouped by World Health Organization and Hochwald grade and the presence of metastases. Variables were assessed for correlation. Groups were compared using t test/Wilcoxon rank sum test.

RESULTS

Arterial enhancement and dynamic washout (r = 0.35, P = 0.02; r = 0.34, P = 0.02, respectively) correlate with vascularity. There is inverse correlation between vascularity and fibrosis (r = -0.62, P < 0.001), but no correlation between enhancement and fibrosis. Metastatic NET had less arterial (mean, -2 [standard deviationi {SD}, 27.1] Hounsfield unit [HU]; 35.7 [SD, 57.5] HU; P = 0.01) and venous (12.6 [SD, 14.4] HU; 29.2 [SD, 38.3] HU; P = 0.04) enhancement and less washout (8.5 [SD, 18.5] HU; 26.8 [SD, 30] HU, P = 0.02) compared with nonmetastatic NET. These differences were not present when comparing by tumor grade. Arterial hypoenhancement was the only significant predictor of metastases.

CONCLUSIONS

Aggressive tumors, as determined by metastases, but not histological grade, enhance less than nonmetastatic tumors.

A simple morphological classification to estimate the malignant potential of pancreatic neuroendocrine tumors

BACKGROUND

A novel morphological classification using resected specimens predicted malignant potential and prognosis in patients with pancreatic neuroendocrine tumors (P-NETs). The aim of this study was to examine the predictive ability of morphological diagnoses made using non-invasive multi-detector computed tomography (MDCT) in P-NETs.

METHODS

Between 2002 and 2015, 154 patients were diagnosed with P-NETs at the Tokyo Medical and Dental University, and 82 patients who underwent surgical treatment were enrolled. The primary tumors were classified by MDCT into three types: Type I, simple nodular tumor; Type II, simple nodular tumor with extra-nodular growth; and Type III, confluent multinodular tumor. Patients were stratified by 15 clinical specialists according to classification and without any other clinical or pathological information. Clinicopathological features and patient survival were reviewed retrospectively.

RESULTS

The mean observation time was 1004 days. Forty-six, 22, and 14 patients had Type I, II, and III tumors, respectively. Morphological classification was significantly correlated with advanced features such as tumor size, Ki-67 index, and synchronous liver metastasis (p < 0.001 for all). There were significant differences between all three tumor types as judged by ENETS TNM classification (p < 0.001), AJCC TNM classification (p = 0.046), WHO 2004 classification (p < 0.001), and WHO 2010 classification (p < 0.001). Five-year progression-free survival (PFS) rates for patients with Type I, II, and III tumors were 97, 43, and 31%, respectively (I vs. II, p < 0.001; I vs. III, p < 0.001; II vs. III, p = 0.017). Multivariate analysis revealed Type II/III tumors and synchronous liver metastasis to be independent risk factors for poor PFS.

CONCLUSION

A novel simple morphological classification system would predict Type II and III tumors that may have higher malignant potential than Type I tumors.