Nonhypervascular Pancreatic Neuroendocrine Tumors: Differential Diagnosis from Pancreatic Ductal Adenocarcinomas at MR Imaging-Retrospective Cross-sectional Study

Radiologic tumor border can further stratify prognosis in patients with pancreatic neuroendocrine tumor

BACKGROUND AND OBJECTIVES

Pancreatic neuroendocrine tumor (PanNET), although rare in incidence, is increasing in recent years. Several clinicopathologic and molecular factors have been suggested for patient stratification due to the extensive heterogeneity of PanNETs. We aimed to discover the prognostic role of assessing the tumor border of PanNETs with pre-operative computed tomography (CT) images and correlate them with other clinicopathologic factors.

METHODS

The radiologic, macroscopic, and microscopic tumor border of 183 surgically resected PanNET cases was evaluated using preoperative CT images (well defined vs. poorly defined), gross images (expansile vs. infiltrative), and hematoxylin and eosin-stained slides (pushing vs. infiltrative). The clinicopathologic and prognostic significance of the tumor border status was compared with other clinicopathologic factors.

RESULTS

A poorly defined radiologic tumor border was observed in 65 PanNET cases (35.5 %), and were more frequent in male patients (P = 0.031), and tumor with larger size, infiltrative macroscopic growth pattern, infiltrative microscopic tumor border, higher tumor grade, higher pT category, lymph node metastasis, lymphovascular and perineural invasions (all, P < 0.001). Patients with PanNET with a poorly defined radiologic tumor border had significantly worse overall survival (OS) and recurrence-free survival (RFS; both, P < 0.001). Multivariable analysis revealed that PanNET with a poorly defined radiologic border is an independent poor prognostic factor for both OS (P = 0.049) and RFS (P = 0.027).

CONCLUSION

Pre-operative CT-based tumor border evaluation can provide additional information regarding survival and recurrence in patients with PanNET.

Differentiating small (< 2 cm) pancreatic ductal adenocarcinoma from neuroendocrine tumors with multiparametric MRI-based radiomic features

OBJECTIVES

To assess MR-based radiomic analysis in preoperatively discriminating small (< 2 cm) pancreatic ductal adenocarcinomas (PDACs) from neuroendocrine tumors (PNETs).

METHODS

A total of 197 patients (146 in the training cohort, 51 in the validation cohort) from two centers were retrospectively collected. A total of 7338 radiomics features were extracted from T2-weighted, diffusion-weighted, T1-weighted, arterial phase, portal venous phase and delayed phase imaging. The optimal features were selected by the Mann-Whitney U test, Spearman's rank correlation test and least absolute shrinkage and selection operator method and used to construct the radiomic score (Rad-score). Conventional radiological and clinical features were also assessed. Multivariable logistic regression was used to construct a radiological model, a radiomic model and a fusion model.

RESULTS

Nine optimal features were identified and used to build the Rad-score. The radiomic model based on the Rad-score achieved satisfactory results with AUCs of 0.905 and 0.930, sensitivities of 0.780 and 0.800, specificities of 0.906 and 0.952 and accuracies of 0.836 and 0.863 for the training and validation cohorts, respectively. The fusion model, incorporating CA19-9, tumor margins, pancreatic duct dilatation and the Rad-score, exhibited the best performance with AUCs of 0.977 and 0.941, sensitivities of 0.914 and 0.852, specificities of 0.954 and 0.950, and accuracies of 0.932 and 0.894 for the training and validation cohorts, respectively.

CONCLUSIONS

The MR-based Rad-score is a novel image biomarker for discriminating small PDACs from PNETs. A fusion model combining radiomic, radiological and clinical features performed very well in differentially diagnosing these two tumors.

CLINICAL RELEVANCE STATEMENT

A fusion model combining MR-based radiomic, radiological, and clinical features could help differentiate between small pancreatic ductal adenocarcinomas and pancreatic neuroendocrine tumors.

KEY POINTS

Preoperatively differentiating small pancreatic ductal adenocarcinomas (PDACs) and pancreatic neuroendocrine tumors (PNETs) is challenging. Multiparametric MRI-based Rad-score can be used for discriminating small PDACs from PNETs. A fusion model incorporating radiomic, radiological, and clinical features differentiated small PDACs from PNETs well.

The radiomorphological appearance of the invasive margin in pancreatic cancer is associated with tumor budding

PURPOSE

Pancreatic cancer (PDAC) is characterized by infiltrative, spiculated tumor growth into the surrounding non-neoplastic tissue. Clinically, its diagnosis is often established by magnetic resonance imaging (MRI). At the invasive margin, tumor buds can be detected by histology, an established marker associated with poor prognosis in different types of tumors.

METHODS

We analyzed PDAC by determining the degree of tumor spiculation on T2-weighted MRI using a 3-tier grading system. The grade of spiculation was correlated with the density of tumor buds quantified in histological sections of the respective surgical specimen according to the guidelines of the International Tumor Budding Consensus Conference (n = 28 patients).

RESULTS

64% of tumors revealed intermediate to high spiculation on MRI. In over 90% of cases, tumor buds were detected. We observed a significant positive rank correlation between the grade of radiological tumor spiculation and the histopathological number of tumor buds (rs = 0.745, p < 0.001). The number of tumor buds was not significantly associated with tumor stage, presence of lymph node metastases, or histopathological grading (p ≥ 0.352).

CONCLUSION

Our study identifies a readily available radiological marker for non-invasive estimation of tumor budding, as a correlate for infiltrative tumor growth. This finding could help to identify PDAC patients who might benefit from more extensive peripancreatic soft tissue resection during surgery or stratify patients for personalized therapy concepts.

Contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) features for characterizing serous microcystic adenomas (SMAs): In comparison to pancreatic neuroendocrine tumors (pNETs)

Objectives

Serous microcystic adenoma (SMA), a primary benign pancreatic tumor which can be clinically followed-up instead of undergoing surgery, are sometimes mis-distinguished as pancreatic neuroendocrine tumor (pNET) in regular preoperative imaging examinations. This study aimed to analyze preoperative contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) features of SMAs in comparison to pNETs.

Material and methods

In this retrospective study, patients with imaging-diagnosed pancreatic lesions were screened between October 2020 to October 2022 (ethical approval No. B2020-309R). Performing by a Siemens Sequoia (Siemens Medical Solutions, Mountain View, CA, USA) equipped with a 5C-1 curved array transducer (3.0-4.5 MHz), CEUS examination was conducted to observe the microvascular perfusion patterns of pancreatic lesions in arterial phase, venous/late phases (VLP) using SonoVue® (Bracco Imaging Spa, Milan, Italy) as the contrast agent. Virtual touch tissue imaging and quantification (VTIQ) - SWE was used to measure the shear wave velocity (SWV, m/s) value to represent the quantitative stiffness of pancreatic lesions. Multivariate logistic regression was performed to analyze potential ultrasound and clinical features in discriminating SMAs and pNETs.

Results

Finally, 30 SMA and 40 pNET patients were included. All pancreatic lesions were pathologically proven via biopsy or surgery. During the arterial phase of CEUS, most SMAs and pNETs showed iso- or hyperenhancement (29/30, 97 % and 31/40, 78 %), with a specific early honeycomb enhancement pattern appeared in 14/30 (47 %) SMA lesions. During the VLP, while most of the SMA lesions remained iso- or hyperenhancement (25/30, 83 %), nearly half of the pNET lesions revealed an attenuated hypoenhancement (17/40, 43 %). The proportion of hypoenhancement pattern during the VLP of CEUS differed significantly between SMAs and pNETs (P = 0.021). The measured SWV value of SMAs was significantly higher than pNETs (2.04 ± 0.70 m/s versus 1.42 ± 0.44 m/s, P = 0.002). Taking a SWV value > 1.83 m/s as a cutoff in differentiating SMAs and pNETs, the area under the receiver operating characteristic curve (AUROC) was 0.825, with sensitivity, specificity and likelihood ratio (+) of 85.71 %, 72.73 % and 3.143, respectively. Multivariate logistic regression revealed that SWV value (m/s) of the pancreatic lesion was an independent variable in discriminating SMA and pNET.

Conclusion

By comprehensively evaluating CEUS patterns and SWE features, SMA and pNET may be well differentiated before the operation. While SMA typically presents as harder lesion in VTIQ-SWE, exhibiting a specific honeycomb hyperenhancement pattern during the arterial phase of CEUS, pNET is characterized by relative softness, occasionally displaying a wash-out pattern during the VLP of CEUS.

A nomogram to preoperatively predict the aggressiveness of non-functional pancreatic neuroendocrine tumors based on CT features

OBJECTIVES

To develop a nomogram to predict the aggressiveness of non-functional pancreatic neuroendocrine tumors (NF-pNETs) based on preoperative computed tomography (CT) features.

METHODS

This study included 176 patients undergoing radical resection for NF-pNETs. These patients were randomly divided into the training (n = 123) and validation sets (n = 53). A nomogram was developed based on preoperative predictors of aggressiveness of the NF-pNETs which were identified by univariable and multivariable logistic regression analysis. The aggressiveness of NF-pNETs was defined as a composite measure including G3 grading, N+, distant metastases, and/ or disease recurrence.

RESULTS

Altogether, the number of patients with highly aggressive NF-pNETs was 37 (30.08 %) and 15 (28.30 %) in the training and validation sets, respectively. Multivariable logistic regression analysis identified that tumor size, biliopancreatic duct dilatation, lymphadenopathy, and enhancement pattern were preoperative predictors of aggressiveness. Those variables were used to develop a nomogram with good concordance statistics of 0.89 and 0.86 for predicting aggressiveness in the training and validation sets, respectively. With a nomogram score of 59, patients with NF-pNETs were divided into low-aggressive and high-aggressive groups. The high-aggressive group had decreased overall survival (OS) and disease-free survival (DFS). Moreover, the nomogram showed good performance in predicting OS and DFS at 3, 5, and 10 years.

CONCLUSION

The nomogram integrating CT features helped preoperatively predict the aggressiveness of NF-pNETs and could potentially facilitate clinical decision-making.

Dual-energy CT improves differentiation of non-hypervascular pancreatic neuroendocrine neoplasms from CA 19-9-negative pancreatic ductal adenocarcinomas

PURPOSE

To evaluate the utility of dual-energy CT (DECT) in differentiating non-hypervascular pancreatic neuroendocrine neoplasms (PNENs) from pancreatic ductal adenocarcinomas (PDACs) with negative carbohydrate antigen 19-9 (CA 19-9).

METHODS

This retrospective study included 26 and 39 patients with pathologically confirmed non-hypervascular PNENs and CA 19-9-negative PDACs, respectively, who underwent contrast-enhanced DECT before treatment between June 2019 and December 2021. The clinical, conventional CT qualitative, conventional CT quantitative, and DECT quantitative parameters of the two groups were compared using univariate analysis and selected by least absolute shrinkage and selection operator regression (LASSO) analysis. Multivariate logistic regression analyses were performed to build qualitative, conventional CT quantitative, DECT quantitative, and comprehensive models. The areas under the receiver operating characteristic curve (AUCs) of the models were compared using DeLong's test.

RESULTS

The AUCs of the DECT quantitative (based on normalized iodine concentrations [nICs] in the arterial and portal venous phases: 0.918; 95% confidence interval [CI] 0.852-0.985) and comprehensive (based on tumour location and nICs in the arterial and portal venous phases: 0.966; 95% CI 0.889-0.995) models were higher than those of the qualitative (based on tumour location: 0.782; 95% CI 0.665-0.899) and conventional CT quantitative (based on normalized conventional CT attenuation in the arterial phase: 0.665; 95% CI 0.533-0.797; all P < 0.05) models. The DECT quantitative and comprehensive models had comparable performances (P = 0.076).

CONCLUSIONS

Higher nICs in the arterial and portal venous phases were associated with higher blood supply improving the identification of non-hypervascular PNENs.

Pancreatic Cancer and Its Mimics

Pancreatic ductal adenocarcinoma (PDAC) is the most common primary pancreatic malignancy, ranking fourth in cancer-related mortality in the United States. Typically, PDAC appears on images as a hypovascular mass with upstream pancreatic duct dilatation and abrupt duct cutoff, distal pancreatic atrophy, and vascular encasement, with metastatic involvement including lymphadenopathy. However, atypical manifestations that may limit detection of the underlying PDAC may also occur. Atypical PDAC features include findings related to associated conditions such as acute or chronic pancreatitis, a mass that is isointense to the parenchyma, multiplicity, diffuse tumor infiltration, associated calcifications, and cystic components. Several neoplastic and inflammatory conditions can mimic PDAC, such as paraduodenal "groove" pancreatitis, autoimmune pancreatitis, focal acute and chronic pancreatitis, neuroendocrine tumors, solid pseudopapillary neoplasms, metastases, and lymphoma. Differentiation of these conditions from PDAC can be challenging due to overlapping CT and MRI features; however, certain findings can help in differentiation. Diffusion-weighted MRI can be helpful but also can be nonspecific. Accurate diagnosis is pivotal for guiding therapeutic planning and potential outcomes in PDAC and avoiding biopsy or surgical treatment of some of these mimics. Biopsy may still be required for diagnosis in some cases. The authors describe the typical and atypical imaging findings of PDAC and features that may help to differentiate PDAC from its mimics. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Zins in this issue.

Comparison of [18F]-OC PET/CT and contrast-enhanced CT/MRI in the detection and evaluation of neuroendocrine neoplasms

OBJECTIVES

Gallium-68 (68Ga)-labeled somatostatin analog (SSA) PET imaging has been widely used in clinical practice of neuroendocrine neoplasms (NENs). Compared with 68Ga, 18F has a great practical and economic advantage. Although a few studies have shown the characteristics of [18F] AlF-NOTA-octreotide ([18F]-OC) in healthy volunteers and small NEN patient groups, its clinical value needs further investigation. Herein, this retrospective study aimed to evaluate the diagnostic accuracy of [18F]-OC PET/CT in detecting NENs, as well as to compare it with contrast-enhanced CT/MRI.

METHODS

We retrospectively reviewed the data of 93 patients who had undergone [18F]-OC PET/CT and CT or MRI scans. Of these patients, there were 45 patients with suspected NENs for diagnostic evaluation, and 48 patients with pathologically confirmed NENs for detecting metastasis or recurrence. [18F]-OC PET/CT images were evaluated visually and semi-quantitatively by measuring maximum standardized uptake value of tumor (SUVmax), tumor-to-background SUVmax ratio (TBR), and SUVmax of hypophysis (SUVhypophysis). A total of 276 suspected NEN lesions were found in these 93 patients. The results of histopathology or radiographic follow-up served as the reference standard for the final diagnosis.

RESULTS

Forty-five patients with suspected NENs were confirmed by histopathological examination via resection or biopsy. [18F]-OC PET/CT showed high radiotracer uptake in the lesions of G1-G3 NENs. [18F]-OC PET/CT showed superior performance with 96.3% of sensitivity, 77.8% of specificity, and 88.9% of accuracy in diagnosing NENs compared to CT/MRI. When cutoffs of SUVmax, TBR, and SUVhypophysis were 8.3, 3.1, and 15.4, [18F]-OC PET/CT had the best equilibrium between sensitivity and specificity for differentiating NEN from non-NEN lesions. For a total of 276 suspected NEN lesions, the sensitivity, specificity, and accuracy of [18F]-OC PET/CT for diagnosis of NENs were 90.5%, 82.1%, and 88.8%, respectively, and were higher than those of CT and MRI. G1 and G2 NENs had higher TBR and lower CT enhancement intensity than G3. The SUVmax and TBR had a positive correlation with CT enhancement intensity in G2 rather than in G1 or G3.

CONCLUSIONS

[18F]-OC PET/CT is a promising imaging modality for initial diagnosis and detecting metastasis or postoperative recurrence in NENs.

Histogram array and convolutional neural network of DWI for differentiating pancreatic ductal adenocarcinomas from solid pseudopapillary neoplasms and neuroendocrine neoplasms

PURPOSE

This study aimed to investigate the diagnostic performance of the histogram array and convolutional neural network (CNN) based on diffusion-weighted imaging (DWI) with multiple b-values under magnetic resonance imaging (MRI) to distinguish pancreatic ductal adenocarcinomas (PDACs) from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (PNENs).

METHODS

This retrospective study consisted of patients diagnosed with PDACs (n = 132), PNENs (n = 45) and SPNs (n = 54). All patients underwent 3.0-T MRI including DWI with 10 b values. The regions of interest (ROIs) of pancreatic tumor were manually drawn using ITK-SNAP software, which included entire tumor at DWI (b = 1500 s/m²). The histogram array was obtained through the ROIs from multiple b-value data. PyTorch (version 1.11) was used to construct a CNN classifier to categorize the histogram array into PDACs, PNENs or SPNs.

RESULTS

The area under the curves (AUCs) of the histogram array and the CNN model for differentiating PDACs from PNENs and SPNs were 0.896, 0.846, and 0.839 in the training, validation and testing cohorts, respectively. The accuracy, sensitivity and specificity were 90.22%, 96.23%, and 82.05% in the training cohort, 84.78%, 96.15%, and 70.0% in the validation cohort, and 81.72%, 90.57%, and 70.0% in the testing cohort. The performance of CNN with AUC of 0.865 for this differentiation was significantly higher than that of f with AUC = 0.755 (P = 0.0057) and α with AUC = 0.776 (P = 0.0278) in all patients.

CONCLUSION

The histogram array and CNN based on DWI data with multiple b-values using MRI provided an accurate diagnostic performance to differentiate PDACs from PNENs and SPNs.

Contrast-Enhanced Harmonic Endoscopic Ultrasound for Diagnosis of the Aggressiveness of Pancreatic Neuroendocrine Neoplasm

The purpose of this study is to clarify the associations between the enhancement patterns on contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) and the aggressiveness and prognosis of pancreatic neuroendocrine neoplasms (PanNENs). Patients who underwent CH-EUS and were pathologically diagnosed with PanNEN were included in this study. Patients were divided into three groups according to contrast-enhancement patterns on early-phase and late-phase imaging: “Group A”, vascular rich in both phases; “Group B”, vascular rich and vascular poor in early and late phases, respectively; “Group C”, vascular poor in both phases. Of 39 patients, 25 were assigned to Group A, 7 to Group B, and 7 to Group C. The median overall survival was not reached in Groups A and B and was 335 days in Group C (p < 0.001). The 1-year survival rates were 100% in Group A, 60% in Group B, and 43% in Group C. Patients in Group C showed the shortest overall survival among the three groups. The vascular-poor pattern on late-phase CH-EUS had the highest sensitivity, specificity, and accuracy for aggressive PanNENs among the patterns analyzed on CH-EUS and CECT (84.6%, 91.7%, and 89.2%, respectively). CH-EUS is useful for the diagnosis of and predicting the prognosis of PanNENs.

Aberrant transcription factors in the cancers of the pancreas

Transcription factors (TFs) are essential for proper activation of gene set during the process of organogenesis, differentiation, lineage specificity. Reactivation or dysregulation of TFs regulatory networks could lead to deformation of organs, diseases including various malignancies. Currently, understanding the mechanism of oncogenesis became necessity for the development of targeted therapeutic strategy for different cancer types. It is evident that many TFs go awry in cancers of the pancreas such as pancreatic ductal adenocarcinoma (PDAC) and pancreatic neuroendocrine neoplasms (PanNENs). These mutated or dysregulated TFs abnormally controls various signaling pathways in PDAC and PanNENs including RTK, PI3K-PTEN-AKT-mTOR, JNK, TGF-β/SMAD, WNT/β-catenin, SHH, NOTCH and VEGF which in turn regulate different hallmarks of cancer. Aberrant regulation of such pathways have been linked to the initiation, progression, metastasis, and resistance in pancreatic cancer. As of today, a number of TFs has been identified as crucial regulators of pancreatic cancer and a handful of them shown to have potential as therapeutic targets in pre-clinical and clinical settings. In this review, we have summarized the current knowledge on the role and therapeutic usefulness of TFs in PDAC and PanNENs.

Establishment of a multi-parameters MRI model for predicting small lymph nodes metastases (<10 mm) in patients with resected pancreatic ductal adenocarcinoma

PURPOSE

To evaluate the potential role of MR findings and DWI parameters in predicting small regional lymph nodes metastases (with short-axis diameter < 10 mm) in pancreatic ductal adenocarcinomas (PDACs).

METHODS

A total of 127 patients, 82 in training group and 45 in testing group, with histopathologically diagnosed PDACs who underwent pancreatectomy were retrospectively analyzed. PDACs were divided into two groups of positive and negative lymph node metastases (LNM) based on the pathological results. Pancreatic cancer characteristics, short axis of largest lymph node, and DWI parameters of PDACs were evaluated.

RESULTS

Univariate and multivariate analyses showed that extrapancreatic distance of tumor invasion, short-axis diameter of the largest lymph node, and mean diffusivity of tumor were independently associated with small LNM in patients with PDACs. The combining MRI diagnostic model yielded AUCs of 0.836 and 0.873, and accuracies of 81.7% and 80% in the training and testing groups. The AUC of the MRI model for predicting LNM was higher than that of subjective MRI diagnosis in the training group (rater 1, P = 0.01; rater 2, 0.008) and in a testing group (rater 1, P = 0.036; rater 2, 0.024). Comparing the subjective diagnosis, the error rate of the MRI model was decreased. The defined LNM-positive group by the MRI model showed significantly inferior overall survival compared to the negative group (P = 0.006).

CONCLUSIONS

The MRI model showed excellent performance for individualized and noninvasive prediction of small regional LNM in PDACs. It may be used to identify PDACs with small LNM and contribute to determining an appropriate treatment strategy for PDACs.

Two nomograms for differentiating mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis

OBJECTIVES

To develop and validate a CT nomogram and a radiomics nomogram to differentiate mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC) in patients with chronic pancreatitis (CP).

METHODS

In this retrospective study, the data of 138 patients with histopathologically diagnosed MFCP or PDAC treated at our institution were retrospectively analyzed. Two radiologists analyzed the original cross-sectional CT images based on predefined criteria. Image segmentation, feature extraction, and feature reduction and selection were used to create the radiomics model. The CT and radiomics models were developed using data from a training cohort of 103 consecutive patients. The models were validated in 35 consecutive patients. Multivariable logistic regression analysis was conducted to develop a model for the differential diagnosis of MFCP and PDAC and visualized as a nomogram. The nomograms' performances were determined based on their differentiating ability and clinical utility.

RESULTS

The mean age of patients was 53.7 years, 75.4% were male. The CT nomogram showed good differentiation between the two entities in the training (area under the curve [AUC], 0.87) and validation (AUC, 0.94) cohorts. The radiomics nomogram showed good differentiation in the training (AUC, 0.91) and validation (AUC, 0.93) cohorts. Decision curve analysis showed that patients could benefit from the CT and radiomics nomograms, if the threshold probability was 0.05-0.85 and > 0.05, respectively.

CONCLUSIONS

The two nomograms reasonably accurately differentiated MFCP from PDAC in patients with CP and hold potential for refining the management of pancreatic masses in CP patients.

KEY POINTS

• A CT nomogram and a computed tomography-based radiomics nomogram reasonably accurately differentiated mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis (CP). • The two nomograms can monitor the cancer risk in patients with CP and hold promise to optimize the management of pancreatic masses in patients with CP.

Pancreatic adenocarcinoma and pancreatic high-grade neuroendocrine carcinoma: two sides of the moon

Pancreatic adenocarcinoma is the seventh leading cause of cancer death in the world and the most common type pf pancreatic cancer. Unfortunately, less than 20% of patients are surgically resectable and the great majority of cases are treated with palliative chemotherapy with unsatisfactory results. No targeted agents or personalized approaches have been validated in the last decades. On the other side, neuroendocrine neoplasms of the pancreas are generally considered indolent tumours. However, high-grade neuroendocrine carcinoma is a rare subtype of neuroendocrine neoplasm of the pancreas (accounting up to 10% of the neuroendocrine neoplasms of the pancreas), with particularly aggressive behaviour and poor prognosis. Even in this case, the treatment is represented by palliative chemotherapy with dismal results and no personalized therapies are available, so far. Notably, the quality of life of these patients is disappointingly low and the future perspectives of more personalized diagnostic and therapeutic strategies are scarce. In this review, we discuss relevant and current information on epidemiology, pathology, diagnosis, clinical presentation, treatment and ongoing clinical trials of these two entities, in order to illustrate the two sides of the moon.

Preoperative Prediction of Lymph Node Metastasis of Pancreatic Ductal Adenocarcinoma Based on a Radiomics Nomogram of Dual-Parametric MRI Imaging

Purpose

This study aims to uncover and validate an MRI-based radiomics nomogram for detecting lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) patients prior to surgery.

Materials and Methods

We retrospectively collected 141 patients with pathologically confirmed PDAC who underwent preoperative T2-weighted imaging (T2WI) and portal venous phase (PVP) contrast-enhanced T1-weighted imaging (T1WI) scans between January 2017 and December 2021. The patients were randomly divided into training (n = 98) and validation (n = 43) cohorts at a ratio of 7:3. For each sequence, 1037 radiomics features were extracted and analyzed. After applying the gradient-boosting decision tree (GBDT), the key MRI radiomics features were selected. Three radiomics scores (rad-score 1 for PVP, rad-score 2 for T2WI, and rad-score 3 for T2WI combined with PVP) were calculated. Rad-score 3 and clinical independent risk factors were combined to construct a nomogram for the prediction of LNM of PDAC by multivariable logistic regression analysis. The predictive performances of the rad-scores and the nomogram were assessed by the area under the operating characteristic curve (AUC), and the clinical utility of the radiomics nomogram was assessed by decision curve analysis (DCA).

Results

Six radiomics features of T2WI, eight radiomics features of PVP and ten radiomics features of T2WI combined with PVP were found to be associated with LNM. Multivariate logistic regression analysis showed that rad-score 3 and MRI-reported LN status were independent predictors. In the training and validation cohorts, the AUCs of rad-score 1, rad-score 2 and rad-score 3 were 0.769 and 0.751, 0.807 and 0.784, and 0.834 and 0.807, respectively. The predictive value of rad-score 3 was similar to that of rad-score 1 and rad-score 2 in both the training and validation cohorts (P &gt; 0.05). The radiomics nomogram constructed by rad-score 3 and MRI-reported LN status showed encouraging clinical benefit, with an AUC of 0.845 for the training cohort and 0.816 for the validation cohort.

Conclusions

The radiomics nomogram derived from the rad-score based on MRI features and MRI-reported lymph status showed outstanding performance for the preoperative prediction of LNM of PDAC.

Clinical significance of pancreatic calcifications: a 15-year single-center observational study

Objectives

Pancreatic calcifications (PC) are considered specific for chronic pancreatitis (CP), but PC may also be present in non-CP diseases. The aims are to understand the pattern of calcifications in different diseases and to determine they were related to malignant diseases.

Methods

A retrospective study was performed including patients with PC or CP undergoing surgery in the Department of General Surgery of Ruijin Hospital from January 2003 to June 2018.

Results

PC were observed in 168 (4.5%) of the 3755 patients with pancreatic lesions treated during the study period. The majority of patients with PC had three kinds of CP (73.2%) while 26.8% had other five kinds of non-CP diseases. In patients with non-CP diseases, the incidence of PC in malignant intraductal papillary mucinous neoplasm (IPMN) was significantly higher than benign IPMN (8.3% vs. 0.7%, p = 0.004). In patients of CP with pancreatic mass (n = 81), PC (Odds ratio = 28.6, p = 0.000), advanced age (> 55 years) and parenchymal atrophy were independent predictors for malignancy. In patients of CP without pancreatic mass (n = 110), there were 82 cases (74.5%) with PC and 5 cases (4.5%) with pancreatic ductal adenocarcinoma. The regression model of risk factors was not successful.

Conclusions

The disease spectrum with PC was very diverse. PC may be related to malignant IPMN in non-CP diseases and is related to malignancy in the patients of CP with pancreatic mass and indications for resection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-022-00725-9.

MRI Feature-Based Nomogram Model for Discrimination Between Non-Hypervascular Pancreatic Neuroendocrine Tumors and Pancreatic Ductal Adenocarcinomas

This study aimed to investigate whether magnetic resonance imaging (MRI) features could differentiate non-hypervascular pancreatic neuroendocrine tumors (PNETs) from pancreatic ductal adenocarcinomas (PDACs). In this study, 131 patients with surgically and pathologically proven non-hypervascular PNETs (n = 44) or PDACs (n = 87) were enrolled. Two radiologists independently analyzed MRI imaging findings and clinical features. Relevant features in differentiating non-hypervascular PNETs from PDACs were identified via univariate and multivariate logistic regression models. The MRI feature-based nomogram was constructed based on multivariable logistic analysis and the reliability of the constructed nomogram was further validated. The results showed that tumor margin (P = 0.012; OR: 6.622; 95% CI: 1.510, 29.028), MPD dilation (P = 0.047; OR: 4.309; 95% CI: 1.019, 18.227), and signal in the portal phase (P &lt; 0.001; OR: 53.486; 95% CI: 10.690, 267.618) were independent discriminative MRI features between non-hypervascular PNETs and PDACs. The discriminative performance of the developed nomogram was optimized compared with single imaging features. The calibration curve, C-index, and DCA validated the superior practicality and usefulness of the MRI-based nomogram. In conclusion, the radiologically discriminative model integrating various MRI features could be preoperatively and easily utilized to differentiate non-hypervascular PNETs from PDACs.

The diagnostic value of intravoxel incoherent motion diffusion-weighted imaging for distinguishing nonhypervascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas

PURPOSE

To primarily evaluate the diagnostic performance of the monoexponential and intravoxel incoherent motion (IVIM) diffusion weighted imaging (DWI) models for differentiating between nonhypervascular pancreatic neuroendocrine tumors (PNETs) and pancreatic ductal adenocarcinomas (PDACs).

METHODS

63 patients with PNETs (35 nonhypervascular PNETs and 28 hypervascular PNETs) and 164 patients with PDACs were retrospectively enrolled in the study and underwent multiple b-value DWI. Intraobserver and interobserver reliabilities of DWI parameters were assessed by using the intraclass correlation coefficient (ICC). The parameters of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) of nonhypervascular PNETs were compared with PDACs and hypervascular PNETs using the independent sample t test or the Mann-Whitney U test. The diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis.

RESULTS

All DWI parameters values showed good to excellent intra- and interobserver agreements (ICC = 0.743-0.873). Nonhypervascular PNETs had significantly lower ADC and D, but significantly higher f than PDACs (P = 0.005, P < 0.001 and P < 0.001, respectively). ADC, D and f of nonhypervascular PNETs were lower than hypervascular PNETs (P = 0.001, <0.001 and 0.093, respectively). D* of nonhypervascular PNETs showed no statistically significant differences with PDACs and hypervascular PNETs (P = 0.809 and 0.420). D showed a higher area under the curve (AUC), followed by ADC and f (AUC = 0.885, 0.665 and 0.740, respectively) in differentiating nonhypervascular PNETs from PDACs.

CONCLUSION

Monoexponential and IVIM diffusion models are valuable to differentiate nonhypervascular PNETs from PDACs. D showed better performance than f and ADC.

Prediction of residual tumor and overall survival after first-line surgery in patients with pancreatic ductal adenocarcinoma using preoperative magnetic resonance imaging findings

BACKGROUND

Complete resection is the only potentially curative treatment in patients with pancreatic ductal adenocarcinoma (PDA) and is associated with a longer overall survival (OS) than incomplete resection of tumor. Hence, prediction of the resection status after surgery would help predict the prognosis of patients with PDA.

PURPOSE

To predict residual tumor (R) classification and OS in patients who underwent first-line surgery for PDA using preoperative magnetic resonance imaging (MRI).

MATERIAL AND METHODS

In this study, 210 patients with PDA who underwent MRI and first-line surgery were randomly categorized into a test group (n=150) and a validation group (n=60). The R classification was divided into R0 (no residual tumor) and R1/R2 (microscopic/macroscopic residual tumor). Preoperative MRI findings associated with R classification and OS were assessed by using logistic regression and Cox proportional hazard models. In addition, the prediction models for the R classification and OS were validated using calibration plots and C statistics.

RESULTS

On preoperative MRI, portal vein encasement (odds ratio 4.755) was an independent predictor for R1/R2 resection (P=0.040). Tumor size measured on MRI (hazard ratio [HR] per centimeter 1.539) was a predictor of OS, along with pathologic N1 and N2 stage (HR 1.944 and 3.243, respectively), R1/R2 resection (HR 3.273), and adjuvant chemoradiation therapy (HR 0.250) (P<0.050). Calibration plots demonstrated satisfactory predictive performance.

CONCLUSION

Preoperative MRI was valuable for predicting R1/R2 resection using portal vein encasement. Tumor size measured on MRI was useful for the prediction of OS after first-line surgery for PDA.

Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR

Background

Pancreatic neuroendocrine tumors (PNETs) grade is very important for treatment strategy of PNETs. The present study aimed to find the quantitative radiomic features for predicting grades of PNETs in MR images.

Materials and Methods

Totally 48 patients but 51 lesions with a pathological tumor grade were subdivided into low grade (G1) group and intermediate grade (G2) group. The ROI was manually segmented slice by slice in 3D-T1 weighted sequence with and without enhancement. Statistical differences of radiomic features between G1 and G2 groups were analyzed using the independent sample t-test. Logistic regression analysis was conducted to find better predictors in distinguishing G1 and G2 groups. Finally, receiver operating characteristic (ROC) was constructed to assess diagnostic performance of each model.

Results

No significant difference between G1 and G2 groups (P > 0.05) in non-enhanced 3D-T1 images was found. Significant differences in the arterial phase analysis between the G1 and the G2 groups appeared as follows: the maximum intensity feature (P = 0.021); the range feature (P = 0.039). Multiple logistic regression analysis based on univariable model showed the maximum intensity feature (P=0.023, OR = 0.621, 95% CI: 0.433-0.858) was an independent predictor of G1 compared with G2 group, and the area under the curve (AUC) was 0.695.

Conclusions

The maximum intensity feature of radiomic features in MR images can help to predict PNETs grade risk.

Neuroendocrine pancreatic tumor in a patient with dual diagnosis of tuberous sclerosis complex and basement membrane disease: A case report and review of the literature

Tuberous Sclerosis is a complex genetic disease that has well-defined clinical criteria. These criteria don't include pancreatic neuroendocrine tumors. We represent a rare case of a patient, with a non-functioning pancreatic neuroendocrine tumor and concomitant diagnosis of tuberous sclerosis complex, and basement membrane disease. The patient was diagnosed based on typical radiologic findings. We have suggested close monitoring and during follow-up studies, the disease was stable. Interestingly the patient tested negative for Tuberous Sclerosis Complex (TSC), which suggests that she might be a somatic mosaic and the mutation level in blood lymphocytes was below the detection level. Moreover, a heterozygous pathogenic variant p.(Gly774Arg) and a heterozygous likely pathogenic variant p.(Gly1465Asp) were identified in the COL4A4 gene. COL4A4 gene is responsible for causing autosomal dominant basement membrane disease. In this case report, we discuss clinical, radiologic, and genetic aspects of these diseases, as well as optimal treatment and follow-up strategies. Thus, by presenting this case we would like to increase awareness of pancreatic neuroendocrine tumors in TSC and emphasize the need for follow-up monitoring.

Apparent Diffusion Coefficient Values for Neuroendocrine Liver Metastases

RATIONALE AND OBJECTIVES

We aimed to investigate whether there are any differences in apparent diffusion coefficient (ADC) values obtained from liver metastases due to gastroenteropancreatic neuroendocrine tumors (GEP-NET) and adenocarcinomas.

MATERIALS AND METHODS

We included 54 patients with 167 liver metastases due to gastroenteropancreatic tumors. We divided the patients into two groups as liver metastases due to GEP-NETs (seven patients with 51 lesions, mean age: 48) and adenocarcinomas (47 patients with 116 lesions, mean age: 61.2). We used the independent samples t-test to compare the ADC and ADC_mean values of the two groups and performed a receiver-operating characteristic analysis.

RESULTS

ADC and ADC_mean values were significantly lower in the GEP-NET group compared with the adenocarcinoma group. Receiver-operating characteristic curve analysis showed a significant difference for ADC and ADC_mean values, and area under the curve values were 0.733 and 0.790, respectively. The cut-off values were 933x10^-6 mm²/s for ADC and 801x10^-6 mm²/s for ADC_mean. Diagnostic accuracies of ADC (Sensitivity = 80.2, Specificity = 64.7, PPV = 83.8, NPV = 58.9) and ADC_mean (Sensitivity = 63.8, Specificity = 82.4, PPV = 89.2, NPV = 50) were calculated in differentiating adenocarcinoma metastases from GEP-NET metastases.

CONCLUSION

The lower ADC and ADC_mean values of liver metastases suggest GEP-NET rather than adenocarcinomas. ADC and ADC_mean values obtained from liver metastases may be used to differentiate NETs from adenocarcinomas.

Current update on imaging for pancreatic neuroendocrine neoplasms

Pancreatic neuroendocrine neoplasms (panNEN) are a heterogeneous group of tumors with differing pathological, genetic, and clinical features. Based on clinical findings, they may be categorized into functioning and nonfunctioning tumors. Adoption of the 2017 World Health Organization classification system, particularly its differentiation between grade 3, well-differentiated pancreatic neuroendocrine tumors (panNET) and grade 3, poorly-differentiated pancreatic neuroendocrine carcinomas (panNEC) has emphasized the role imaging plays in characterizing these lesions. Endoscopic ultrasound can help obtain biopsy specimen and assess tumor margins and local spread. Enhancement patterns on computed tomography (CT) and magnetic resonance imaging (MRI) may be used to classify panNEN. Contrast enhanced MRI and diffusion-weighted imaging have been reported to be useful for characterization of panNEN and quantifying metastatic burden. Current and emerging radiotracers have broadened the utility of functional imaging in evaluating panNEN. Fluorine-18 fluorodeoxyglucose positron emission tomography (PET)/CT and somatostatin receptor imaging such as Gallium-68 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid–octreotate PET/CT may be useful for improved identification of panNEN in comparison to anatomic modalities. These new techniques can also play a direct role in optimizing the selection of treatment for individuals and predicting tumor response based on somatostatin receptor expression. In addition, emerging methods of radiomics such as texture analysis may be a potential tool for staging and outcome prediction in panNEN, however further investigation is required before clinical implementation.

Non-gaussian models of 3-Tesla diffusion-weighted MRI for the differentiation of pancreatic ductal adenocarcinomas from neuroendocrine tumors and solid pseudopapillary neoplasms

PURPOSE

To assess the MRI performance in differentiating pancreatic ductal adenocarcinomas (PDACs), from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine tumors (PNETs) using non-gaussian diffusion-weighted imaging models.

METHODS

This was a retrospective study of patients diagnosed with PDACs (01/2015-06/2019) or with PNETs or SPNs diagnosed (01/2011-12/2019) at our hospital. The lesions were randomized 1:1 to the primary and validation cohorts. The regions of interest (ROIs) were manually drawn on each slice at DWI (b = 1500 s/mm²) from 3 T MRI. D (diffusion coefficient), D* (pseudodiffusion coefficient), f (perfusion fraction), distributed diffusion coefficient (DDC), α (diffusion heterogeneity index), mean diffusivity (MD) and mean kurtosis (MK) were obtained. The parameters with largest performance for differentiation were used to establish a diagnostic model.

RESULTS

There were 148, 56, and 60 patients with PDAC, PNET, and SPN, respectively. For differentiating PDACs from SPNs, f and MK values were used to establish a diagnostic model with areas under the receiver operating characteristic curves (AUCs) of 0.92 and 0.89 in the primary and validation groups, respectively. For distinguishing PDACs from PNETs, α and MK values were used to establish a diagnostic model with AUCs of 0.87 and 0.86 in the primary and validation groups, respectively. The accuracy rate of the subjective evaluation with the assistance of non-gaussian DWI models for differentiating PDAC from SPNs and PNETs were higher than that of subjective diagnosis alone (P < 0.05).

CONCLUSIONS

The non-gaussian DWI models could assist radiologists in accurately differentiating PDACs from PNETs and SPNs.

Utility of contrast-enhanced harmonic endoscopic ultrasonography for predicting the prognosis of pancreatic neuroendocrine neoplasms

BACKGROUND AND AIMS

Pancreatic neuroendocrine neoplasms (PanNENs), including Grade 1 (G1) or G2 tumors, can have a poor prognosis. This study investigated the value of contrast-enhanced harmonic endoscopic ultrasonography (CH-EUS) for predicting the prognosis of PanNENs.

METHODS

This single-center, retrospective study included 47 consecutive patients who underwent CH-EUS and were diagnosed with PanNEN by surgical resection or EUS-guided fine needle aspiration between December 2011 and February 2016. Patients were divided into aggressive and non-aggressive groups according to the degree of clinical malignancy. CH-EUS was assessed regarding its capacity for diagnosing aggressive PanNEN, the correspondence between contrast patterns and pathological features, and its ability to predict the prognosis of PanNEN.

RESULTS

There were 19 cases of aggressive PanNEN and 28 cases of non-aggressive PanNEN. The aggressive group included three G1, four G2, three G3 tumors, three mixed neuroendocrine non-neuroendocrine neoplasms, and six neuroendocrine carcinomas. CH-EUS was superior to contrast-enhanced computed tomography for the diagnosis of aggressive PanNEN (P < 0.001): hypo-enhancement on CH-EUS was an indicator of aggressive PanNEN, with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94.7%, 100%, 100%, 96.6%, and 97.9%, respectively. Among G1/G2 PanNENs, cases with hypo-enhancement on CH-EUS had a poorer prognosis than those with hyper/iso-enhancement (P = 0.0009). Assessment of 36 resected specimens showed that hypo-enhancement on CH-EUS was associated with smaller and fewer vessels and greater degree of fibrosis.

CONCLUSION

Contrast-enhanced harmonic endoscopic ultrasonography may be useful for predicting the prognosis of PanNENs.

Radiological Imaging of Gastro-Entero-Pancreatic Neuroendocrine Tumors. The Review of Current Literature Emphasizing the Diagnostic Value of Chosen Imaging Methods

Despite development of radiologic imaging, detection and follow-up of neuroendocrine neoplasms (NENs) still pose a diagnostic challenge, due to the heterogeneity of NEN, their relatively long-term growth, and small size of primary tumor. A set of information obtained by using different radiological imaging tools simplifies a choice of the most appropriate treatment method. Moreover, radiological imaging plays an important role in the assessment of metastatic lesions, especially in the liver, as well as, tumor response to treatment. This article reviews the current, broadly in use imaging modalities which are applied to the diagnosis of GEP-NETs, (the most common type of NENs) and put emphasis on the strengths and limitations of each modality.

Investigating difficult to detect pancreatic lesions: Characterization of benign pancreatic islet cell tumors using multiparametric pancreatic 3-T MRI

Introduction

Pancreatic islet-cell tumors (PICT) often present with atypical signal-characteristics and are often missed on preoperative imaging. The aim of this study is to provide a multiparametric PICT characterization and investigate factors impeding PICT detection.

Material and methods

This is a detailed MRI analysis of a prospective, monocenter study, including 49 consecutive patients (37 female, 12 male; median age 50) with symptoms due to endogenous hyperinsulinemic hypoglycemia (EHH) and mostly negative prior-imaging. All patients received a 3-T MRI and a ⁶⁸Ga-DOTA-exendin-4-PET/CT. Pooled accuracy, sensitivity, specificity and inter-reader agreement were calculated. Reference-standard was histopathology and ⁶⁸Ga-DOTA-Exendin-4-PET/CT in one patient who refused surgery. For PICT analyses, 34 patients with 49 PICTs (48 histologically proven; one ⁶⁸Ga-DOTA-exendin-4-PET/CT positive) were assessed. Dynamic contrast-enhanced (DCE) Magnetic Resonance Images (MRI) with Golden-Angle-Radial-Sparse-Parallel (GRASP) reconstruction, enabling imaging at high spatial and temporal resolution, was used to assess enhancement-patterns of PICTs. Tumor-to-background (T2B) ratio for each sequence and the employed quantitative threshold for conspicuity of PICTs were analyzed in regard to prediction of true-positive PICTs.

Results

Evaluation of 49 patients revealed a pooled lesion-based accuracy, sensitivity and specificity of 70.3%, 72.9% and 62.5%, respectively. Mean PICT size was 12.9±5.3mm for detected, 9.0±2.9mm for undetected PICTs (p-value 0.0112). In-phase T1w detected the most PICT (67.3%). Depending on the sequence, PICTs were isointense and poorly visible in 29–68%. Only 2/41(4.9%) PICTs showed typical signal-characteristics across T1w, T2w, DWI and ceT1w combined. 66.6% of PICTs enhanced simultaneously to the parenchyma, 17.8% early and 15.6% late. Predictor screening analysis showed number of sequences detecting a PICT, lesion size and in-phase T1w T2B ratio had the highest contribution for detecting a true-positive PICT.

Conclusion

The majority of PICTs enhance simultaneously to surrounding parenchyma, present with atypical signal-characteristics and thus are poorly visible. In non-enhancing PICTs, radiologists should search for small lesions most likely conspicuous on unenhanced T1w or DWI.

Hypovascular pancreatic neuroendocrine tumor with hepatic metastases: A case report and literature review

Hypovascular pancreatic neuroendocrine tumors are uncommon pancreatic tumors and commonly misdiagnosed as pancreatic ductal adenocarcinoma or chronic mass-forming pancreatitis. The liver is the organ most commonly affected by neuroendocrine tumor metastases but hepatic neuroendocrine tumor metastases are quite difficult to discriminate from other hepatic metastases and primary hepatic tumors. We describe a case of a 47-year-old man with incidentally detected multiple hepatic lesions on ultrasound. On further imaging technique including computed tomography and magnetic resonance imaging, the patient had an abnormal hypoenhancing lesion at the pancreatic tail and multiple hyperenhancing hepatic metastases that were diagnosed as hypovascular pancreatic well-differentiated neuroendocrine tumor Grade 2 with multiple hypervascular hepatic metastases after liver biopsy and surgery. Neuroendocrine tumor is a rare etiology among hypoenhancing pancreatic tumors, and must be considered to discriminate from pancreatic adenocarcinomas in cases there are multiple hyperenhancing hepatic metastases on the arterial phase without typical washout on the portal venous phase.

Hyperdense Pancreatic Ductal Adenocarcinoma: Clinical Characteristics and Proteomic Landscape

Purpose

Hypodensity of pancreatic ductal adenocarcinoma (PDAC) during contrast-enhanced computed tomography (CECT) examination is common, but a minority of PDAC patients exhibit hyperdense images. The present study examined the clinical characteristics and protein landscape of PDAC with hyperdensity.

Materials and Methods

A total of 844 pathologically confirmed PDAC patients who underwent CECT before surgery were included. During the parenchymal phase of CECT, patients were assigned to the hyperdense or hypodense group based on CT values. Clinical and CT characteristics for predicting relapse-free survival (RFS) and overall survival (OS) were analyzed using the Kaplan–Meier method and Cox proportional hazards model. The expression of the tumor angiogenesis marker CD31 and stroma-related protein CTHRC1 were analyzed using immunohistochemistry (IHC) assay to evaluate differences between the two groups. Proteomics was performed to compare the possible mechanisms underlying the differential enhancement on CT scans.

Results

Based on CECT, 43 and 801 PDAC patients had hyperdense and hypodense lesions, respectively. All 43 patients presented a hyperdense lesion in the parenchymal phase. The mean CECT values of the hyperdense group were higher than the hypodense group (102.5 ± 17.4 and 53.7 ± 18.7, respectively, P< 0.001). The hyperdense group had a better prognosis than the hypodense group (median RFS, 19.97 vs. 12.34 months, P = 0.0176; median OS, 33.6 vs. 20.3 months, P = 0.047). Multivariate analysis showed that age, higher CA19-9 levels (> 300 U/ml), tumor stage, tumor differentiation, tumor CT density, and adjuvant chemotherapy were significant independent prognostic factors for OS. CD31 immunohistochemical staining showed that the hyperdense PDACs had a higher microvessel density than the hypodense group (P< 0.001). CTHRC1 expression was higher in the hypodense group (P = 0.019). Sixty-eight differentially expressed proteins were found using the tandem mass tag labeling-based quantification of the proteomes of PDAC tissue samples, and 7 proteins (POFUT1, PKP2, P0DOX4, ITPR1, HBG2, IGLC3, SAA2) were related to angiogenesis.

Conclusion

Patients who presented with a hyperdense mass on CECT had a higher microvessel density and better prognosis. Anti-angiogenic therapy may be suitable for these patients.

Differentiation between non-hypervascular pancreatic neuroendocrine tumors and mass-forming pancreatitis using contrast-enhanced computed tomography

BACKGROUND

Non-hypervascular pancreatic neuroendocrine tumors (PNETs) showed slight or iso-enhancement in contrast-enhanced computed tomography (CE-CT), which shared similar imaging findings with mass-forming pancreatitis (MFPs).

PURPOSE

To explore the value of CT imaging features in differentiating the two diseases.

MATERIAL AND METHODS

Fifty-one patients with histologically proved MFPs (n = 27) or non-hypervascular PNETs (n = 24) were included. Two radiologists reviewed CT imaging findings and clinical features. Logistic regression analysis was performed to identify relevant features in differentiating non-hypervascular PNETs and MFPs. Receiver operating characteristic (ROC) curve analysis was used to show the performance of the optimal parameters in differentiating non-hypervascular PNETs and MFPs.

RESULTS

A well-defined margin was more common in non-hypervascular PNETs (P < 0.05) than that in MFPs. MFPs often occurred in older people (P < 0.01) and the head-neck of the pancreas compared with non-hypervascular PNETs (P < 0.05). Metastases only presented in non-hypervascular PNETs (P < 0.05). CT values at venous phase and delay phase of MFPs were higher (P = 0.010 and P = 0.029) than those in non-hypervascular PNETs. Logistic analysis showed gender, tumor margin, CT values at venous phase, and tumor components were independent predictors in differentiating the two lesions. The area under the curve (AUC) was 0.938 with a sensitivity of 87.5% and specificity of 92.6% for combined predicators.

CONCLUSION

Gender, tumor margin, CT values at venous phase, and tumor components were useful predicators in differentiating non-hypervascular PNETs and MFPs.

Imaging neuroendocrine tumors: Characterizing the spectrum of radiographic findings

Neuroendocrine tumors (NET) are a group of neoplasms with neuroendocrine differentiation affecting a wide range of organs. Functional NETs present with symptoms due to the particular hormone produced. Functional NETs are usually small at diagnosis and therefore can be challenging to diagnose. In contrast, non-functioning NETs are generally larger and present with mass effect. Imaging plays an indispensable role in diagnosis, staging and management of patients with NETs. The optimal modality and technique for imaging of NETs depend on the location of primary and metastatic lesions. Regardless of the imaging modality, dynamic contrast-enhanced imaging is essential for evaluation of NETs. In general, CT scan is typically the primary imaging modality for evaluating NETs. MRI is used as a complementary modality, being superior to other modalities to assess liver metastasis. Nuclear medicine imaging is also widely used in NET assessment.

Hybrid imaging of the abdomen and pelvis

Accurate imaging is crucial for lesion detection in abdominal organs, for the noninvasive characterization of focal and diffuse abnormalities, and for surgical planning. To accomplish these tasks, several imaging modalities such as multidetector computer tomography (MDCT), magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) are used for abdominal imaging, providing important morphological, functional or metabolic information. More recently, PET/MRI has been gaining attention due to the possibility of combining high-resolution imaging with metabolic imaging. PET/MRI is a novel hybrid imaging technology that in the near future might play a pivotal role in the clinical management of oncologic and inflammatory abdominopelvic diseases. Despite the still limited number of published clinical studies, PET/MRI has been proven to be at least equivalent to PET/CT and to standalone MRI in a variety of oncologic disease. Moreover, in selected and focused clinical studies, it has been proven to outperform current standard of care imaging, for example, in evaluating cholangiocarcinomas, liver metastases, untreated and treated rectal cancer. This has also had an impact on therapeuticmanagement in some studies. Therefore in some institutions, including those of the authors, PET/MRI is becoming the new standard imaging modality in staging treatment-naïve intrahepatic massforming cholangiocarcinomas and prior to complicated hepatic surgery.

Dynamic Enhancement Pattern on CT for Predicting Pancreatic Neuroendocrine Neoplasms with Low PAX6 Expression: A Retrospective Observational Study

Paired box 6 (PAX6) is a transcription factor that plays a critical role in tumor suppression, implying that the downregulation of PAX6 promotes tumor growth and invasiveness. This study aimed to examine dynamic computed tomography (CT) features for predicting pancreatic neuroendocrine neoplasms (Pan-NENs) with low PAX6 expression. We retrospectively evaluated 51 patients with Pan-NENs without synchronous liver metastasis to assess the pathological expression of PAX6. Two radiologists analyzed preoperative dynamic CT images to determine morphological features and enhancement patterns. We compared the CT findings between low and high PAX6 expression groups. Pathological analysis identified 11 and 40 patients with low and high PAX6 expression, respectively. Iso- or hypoenhancement types in the arterial and portal phases were significantly associated with low PAX6 expression (p = 0.009; p = 0.001, respectively). Low PAX6 Pan-NENs showed a lower portal enhancement ratio than high PAX6 Pan-NENs (p = 0.044). The combination based on enhancement types (iso- or hypoenhancement during arterial and portal phases) and portal enhancement ratio (≤1.22) had 54.5% sensitivity, 92.5% specificity, and 84.3% accuracy in identifying low PAX6 Pan-NENs. Dynamic CT features, including iso- or hypoenhancement types in the arterial and portal phases and lower portal enhancement ratio may help predict Pan-NENs with low PAX6 expression.

Evaluation of contrast-enhanced computed tomography for the differential diagnosis of hypovascular pancreatic neuroendocrine tumors from chronic mass-forming pancreatitis

PURPOSE

To assess the role of contrast-enhanced computed tomography (CECT) for differentiation of hypovascular pancreatic neuroendocrine tumors (hypo-PNETs) from chronic mass-forming pancreatitis (CMFP).

METHODS

A retrospective study of 59 patients (27 hypo-PNETs patients vs 32 CMFP patients) who underwent preoperative CECT between July 2012 and July 2019 was performed. Qualitative and quantitative analysis was performed, including mass location, size, margin, cystic changes, calcification, pancreatic or bile duct dilatation, pancreatic atrophy, local vessels involvement, mass contrast enhancement and mass-to-pancreas enhancement ratio. Multivariate logistic regression analyses were used to identify relevant CT imaging findings in differentiation between hypo-PNETs and CMFP.

RESULTS

When compared to CMFP, hypo-PNETs more frequently had a well-defined margin and cystic changes and less frequently had a history of pancreatitis and calcification. CMFP had higher mass contrast enhancement and mass-to-pancreas enhancement ratio in the portal and delayed phases than hypo-PNETs. After multivariate logistic regression analyses, areas under the curve (AUCs) were 0.795 (95 % CI: 0.652-0.899), 0.752 (95 % CI: 0.604-0.866), 0.859 (95 % CI: 0.726-0.943), and 0.929 (95 % CI: 0.814-0.983) for Model 1(clinical factors), Model 2 (qualitative parameters), Model 3 (quantitative parameters), and their combinations, respectively.

CONCLUSION

Combined assessment of clinical factors, qualitative, and quantitative imaging characteristics can improve the differentiation between hypo-PNETs and CMFP at CECT.

Multimodality Imaging of Abdominopelvic Tumors with Venous Invasion

A broad range of abdominal and pelvic tumors can manifest with or develop intraluminal venous invasion. Imaging features at cross-sectional modalities and contrast-enhanced US that allow differentiation of tumor extension within veins from bland thrombus include the expansile nature of tumor thrombus and attenuation and enhancement similar to those of the primary tumor. Venous invasion is a distinctive feature of hepatocellular carcinoma and renal cell carcinoma with known prognostic and treatment implications; however, this finding remains an underrecognized characteristic of multiple other malignancies-including cholangiocarcinoma, adrenocortical carcinoma, pancreatic neuroendocrine tumor, and primary venous leiomyosarcoma-and can be a feature of benign tumors such as renal angiomyolipoma and uterine leiomyomatosis. Recognition of tumor venous invasion at imaging has clinical significance and management implications for a range of abdominal and pelvic tumors. For example, portal vein invasion is a strong negative prognostic indicator in patients with hepatocellular carcinoma. In patients with rectal cancer, diagnosis of extramural venous invasion helps predict local and distant recurrence and is associated with worse survival. The authors present venous invasion by vascular distribution and organ of primary tumor origin with review of typical imaging features. Common pitfalls and mimics of neoplastic thrombus, including artifacts and anatomic variants, are described to help differentiate these findings from tumor in vein. By accurately diagnosing tumor venous invasion, especially in tumors where its presence may not be a typical feature, radiologists can help referring clinicians develop the best treatment strategies for their patients. ^©RSNA, 2020.

Radiomics Analysis Based on Diffusion Kurtosis Imaging and T2 Weighted Imaging for Differentiation of Pancreatic Neuroendocrine Tumors From Solid Pseudopapillary Tumors

Objective

To develop and validate a radiomics model of diffusion kurtosis imaging (DKI) and T2 weighted imaging for discriminating pancreatic neuroendocrine tumors (PNETs) from solid pseudopapillary tumors (SPTs).

Materials and Methods

Sixty-six patients with histopathological confirmed PNETs (n = 31) and SPTs (n = 35) were enrolled in this study. ROIs of tumors were manually drawn on each slice at T2WI and DWI (b = 1,500 s/mm²) from 3T MRI. Intraclass correlation coefficients were used to evaluate the interobserver agreement. Mean diffusivity (MD) and mean kurtosis (MK) were derived from DKI. The least absolute shrinkage and selection operator regression were used for feature selection.

Results

MD and MK had a moderate diagnostic performancewith the area under curve (AUC) of 0.71 and 0.65, respectively. A radiomics model, which incorporated sex and age of patients and radiomics signature of the tumor, showed excellent discrimination performance with AUC of 0.97 and 0.86 in the primary and validation cohort. Moreover, the new model had better diagnostic performance than that of MD (P = 0.023) and MK (P = 0.004), and showed excellent differentiation with a sensitivity of 95.00% and specificity of 91.67% in primary cohort, and the sensitivity of 90.91% and specificity of 81.82% in the validation cohort. The accuracy of radiomics analysis, radiologist 1, and radiologist 2 for diagnosing SPTs and PNETs were 92.42, 77.27, and 78.79%, respectively. The accuracy of radiomics analysis was significantly higher than that of subjective diagnosis (P < 0.05).

Conclusions

Radiomics model could improve the diagnostic accuracy of SPTs and PNETs and contribute to determining an appropriate treatment strategy for pancreatic tumors.

Pancreatic Neuroendocrine Neoplasms: 2020 Update on Pathologic and Imaging Findings and Classification

Pancreatic neuroendocrine neoplasms (panNENs) are heterogeneous neoplasms with neuroendocrine differentiation that show characteristic clinical, histomorphologic, and prognostic features; genetic alterations; and biologic behavior. Up to 10% of panNENs develop in patients with syndromes that predispose them to cancer, such as multiple endocrine neoplasia type 1, von Hippel-Lindau disease, tuberous sclerosis complex, neurofibromatosis type 1, and glucagon cell adenomatosis. PanNENs are classified as either functioning tumors, which manifest early because of clinical symptoms related to increased hormone production, or nonfunctioning tumors, which often manifest late because of mass effect. PanNENs are histopathologically classified as well-differentiated pancreatic neuroendocrine tumors (panNETs) or poorly differentiated pancreatic neuroendocrine carcinomas (panNECs) according to the 2010 World Health Organization (WHO) classification system. Recent advances in cytogenetics and molecular biology have shown substantial heterogeneity in panNECs, and a new tumor subtype, well-differentiated, high-grade panNET, has been introduced. High-grade panNETs and panNECs are two distinct entities with different pathogenesis, clinical features, imaging findings, treatment options, and prognoses. The 2017 WHO classification system and the eighth edition of the American Joint Committee on Cancer staging system include substantial changes. Multidetector CT, MRI, and endoscopic US help in anatomic localization of the primary tumor, local-regional spread, and metastases. Somatostatin receptor scintigraphy and fluorine 18-fluorodeoxyglucose PET/CT are helpful for functional and metabolic assessment. Knowledge of recent updates in the pathogenesis, classification, and staging of panNENs and familiarity with their imaging findings allow optimal patient treatment. ^©RSNA, 2020.

Imaging Features of Sclerosing Epithelioid Fibrosarcoma of the Pancreas: A Case Report

Background: Sclerosing epithelioid fibrosarcoma (SEF) is an extremely rare fibrosarcoma variant. There is no complete imaging data on SEF involving the pancreas. Herein we report the computed tomography (CT) and magnetic resonance imaging (MRI) data of a patient with SEF that primarily involved the pancreas.

Case Presentation: A 64-year-old man was found to have a solid mass in the tail of the pancreas on unenhanced CT. He had no constitutional symptoms. Contrast-enhanced abdominal CT and MRI were performed, and the results of the latter provided the diagnosis of a pancreatic neuroendocrine tumor. Laparoscopic distal pancreatectomy and splenectomy were performed. Anatomopathological examination and immunohistochemistry confirmed that the tumor was a SEF of the pancreas. The patient had no signs of recurrence or metastasis during a 12-months follow-up.

Conclusion: We report an extremely rare case of SEF in the pancreas and its characterization with CT and MRI.

Grade 3 Pancreatic Neuroendocrine Tumors on MDCT: Establishing a Diagnostic Model and Comparing Survival Against Pancreatic Ductal Adenocarcinoma

OBJECTIVE. The purpose of this study is to establish a diagnostic model for differentiating grade 3 (G3) pancreatic neuroendocrine tumors (PNETs) from pancreatic ductal adenocarcinomas (PDACs) and to analyze survival outcomes. MATERIALS AND METHODS. Twenty patients with G3 PNETs and 58 patients with PDACs confirmed by surgery or biopsy were retrospectively included. Demographic and radiologic information was collected. Univariate analyses and binary logistic regression analyses were performed to identify independent factors and establish a diagnostic model. An ROC curve was created to determine diagnostic ability. Kaplan-Meier survival analysis was performed. RESULTS. Patients with G3 PNETs were more likely to present with normal carbohydrate antigen (CA) 19-9 levels, normal pancreatic ducts, and round tumors with well-defined margins and higher portal enhancement ratios than were patients with PDAC (p < 0.05). After multivariate analysis, a normal CA 19-9 level (odds ratio, 0.0125; 95% CI, 0.0008-0.2036), round tumor shape (odds ratio, 0.0143; 95% CI, 0.0004-0.5461), and pancreatic duct dilation of 4 mm or less (odds ratio, 17.9804; 95% CI, 1.0098-320.1711) were independent predictors of G3 PNETs. The AUC of the ROC curve was 0.916, and sensitivity and specificity were 90.0% and 81.0%, respectively. Furthermore, patients with G3 PNETs had better overall survival than patients with PDACs. Among patients in the G3 PNET subgroup, patients with liver or lymph node metastases had worse overall survival than patients without metastases. CONCLUSION. A diagnostic model was established to differentiate G3 PNETs from PDACs. A normal CA 19-9 level, round tumor shape, and pancreatic duct dilation of 4 mm or less were factors that were strongly predictive of G3 PNET.

Differentiating hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma based on CT texture analysis

Background

Hypovascular pancreatic neuroendocrine tumor is usually misdiagnosed as pancreatic ductal adenocarcinoma.

Purpose

To investigate the value of texture analysis in differentiating hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma on contrast-enhanced computed tomography (CT) images.

Material and Methods

Twenty-one patients with hypovascular pancreatic neuroendocrine tumors and 63 patients with pancreatic ductal adenocarcinomas were included in this study. All patients underwent preoperative unenhanced and dynamic contrast-enhanced CT examinations. Two radiologists independently and manually contoured the region of interest of each lesion using texture analysis software on pancreatic parenchymal and portal phase CT images. Multivariate logistic regression analysis was performed to identify significant features to differentiate hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas. Receiver operating characteristic curve analysis was performed to ascertain diagnostic ability.

Results

The following CT texture features were obtained to differentiate hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: RMS (root mean square) (odds ratio [OR] = 0.50, P<0.001), Quantile50 (OR = 1.83, P<0.001), and sumAverage (OR = 0.92, P=0.007) in parenchymal images and “contrast” in portal phase images (OR = 6.08, P<0.001). The areas under the curves were 0.76 for RMS (sensitivity = 0.75, specificity = 0.67), 0.73 for Quantile50 (sensitivity = 0.60, specificity = 0.77), 0.70 for sumAverage (sensitivity = 0.65, specificity = 0.82), 0.85 for the combined texture features (sensitivity = 0.77, specificity = 0.85).

Conclusion

CT texture analysis may be helpful to differentiate hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas. The three combined texture features showed acceptable diagnostic performance.

Imaging diagnosis and staging of pancreatic ductal adenocarcinoma: a comprehensive review

Pancreatic ductal adenocarcinoma (PDAC) has continued to have a poor prognosis for the last few decades in spite of recent advances in different imaging modalities mainly due to difficulty in early diagnosis and aggressive biological behavior. Early PDAC can be missed on CT due to similar attenuation relative to the normal pancreas, small size, or hidden location in the uncinate process. Tumor resectability and its contingency on the vascular invasion most commonly assessed with multi-phasic thin-slice CT is a continuously changing concept, particularly in the era of frequent neoadjuvant therapy. Coexistent celiac artery stenosis may affect the surgical plan in patients undergoing pancreaticoduodenectomy. In this review, we discuss the challenges related to the imaging of PDAC. These include radiological and clinical subtleties of the tumor, evolving imaging criteria for tumor resectability, preoperative diagnosis of accompanying celiac artery stenosis, and post-neoadjuvant therapy imaging. For each category, the key imaging features and potential pitfalls on cross-sectional imaging will be discussed. Also, we will describe the imaging discriminators of potential mimickers of PDAC.

Differential Diagnosis of Nonhypervascular Pancreatic Neuroendocrine Neoplasms From Pancreatic Ductal Adenocarcinomas, Based on Computed Tomography Radiological Features and Texture Analysis

RATIONALE AND OBJECTIVES

To determine computed tomography (CT) radiological features and texture features that are rewarding in differentiating nonhypervascular pancreatic neuroendocrine neoplasms (PNENs) from pancreatic ductal adenocarcinomas (PDACs).

MATERIALS AND METHODS

We compared patients to pathologically proven nonhypervascular PNENs and age-matched controls with pathologically proven PDACs in a 1:2 ratio. Preoperative CT images in the arterial phase (AP) and portal vein phase (PVP) were obtained. Two radiologists independently reviewed the morphological characteristics of each tumor. Three-dimensional regions of interest (ROIs), drawn using ITK-SNAP software, were input into AK software (Artificial Intelligent Kit, GE) to extract texture features from AP and PVP images. Differences between PNENs and PDACs were analyzed with the chi-squared test, least absolute shrinkage and selection operator, kappa statistics, and uni- and multivariate logistic regression analyses.

RESULTS

In total, 40 nonhypervascular PNENs and 80 PDACs were evaluated. Maximum diameter on axial section, margin, calcification, vascularity in the tumor, and tumor heterogeneity were significantly different between PDACs and nonhypervascular PNENs. Multivariate analysis showed well-defined tumor margin (odds ratio: 21.0) and presence of calcification (odds ratio: 4.4) were significant predictors of nonhypervascular PNENs. The area under the receiver operating characteristic curve of the radiological feature model, AP texture model, and PVP texture model were 0.780, 0.855, and 0.929, respectively, based on logistic regression.

CONCLUSION

A well-defined margin and calcification in the tumor were helpful in discriminating nonhypervascular PNENs from PDACs. Texture analysis of contrast-enhanced CT images could be beneficial in differentially diagnosing nonhypervascular PNENs and PDACs.

The prognostic value of multidetector CT features in predicting overall survival outcomes in patients with pancreatic neuroendocrine tumors

PURPOSE

To assess the prognostic value of multidetector CT in predicting overall survival outcomes in patients with pancreatic neuroendocrine tumors (PNETs).

METHOD

Seventy-one patients pathologically diagnosed with PNETs were retrospectively included. The clinical and imaging information was evaluated by two radiologists. The difference between well-differentiated and poorly differentiated PNETs was analyzed. Cox proportional hazards models were created to determine the risk factors for overall survival. Kaplan-Meier survival analyses with log-rank tests were used among different subgroups of patients with PNETs.

RESULTS

In the whole cohort, the median survival was 36 months, and the 5-year survival rate was 84.8 %. Patients with poorly differentiated PNETs were more likely to present with symptoms, abnormal tumor markers, larger diameters, irregular shapes, ill-defined margins, invasion into nearby tissues, liver and lymph node metastases, and lower enhancement ratio than those with well-differentiated PNETs (P < 0.05). In the multivariate analysis, lymph node metastases (hazard ratio: 21.52, P = 0.009) and a portal enhancement ratio less than 1.02 (hazard ratio: 30.89, P = 0.024) were significant factors for overall survival. Overall survival decreased with an ill-defined margin, irregular shape, poor differentiation, grade 3 disease, nonfunctional status, abnormal tumor marker levels, invasion into nearby tissues, lymph node and liver metastases, and lower enhancement ratio (log-rank P < 0.05).

CONCLUSIONS

Poorly differentiated PNETs were more aggressiveness than well-differentiated PNETs. Lymph node metastases and a portal enhancement ratio < 1.02 were independent prognostic factors for worse overall survival outcomes in patients with PNETs.

Quantitative 3D Assessment of 68Ga-DOTATOC PET/MRI with Diffusion-Weighted Imaging to Assess Imaging Markers for Gastroenteropancreatic Neuroendocrine Tumors: Preliminary Results

⁶⁸Ga-DOTATOC PET/MRI combines the advantages of PET in the acquisition of metabolic-functional information with the high soft-tissue contrast of MRI. SUVs in tumors have been suggested to be a measure of somatostatin receptor expression. A challenge with receptor ligands is that the distribution volume is confined to tissues with tracer uptake, potentially limiting SUV quantification. In this study, various functional 3-dimensional SUV apparent diffusion coefficient (ADC) parameters and arterial tumor enhancement were tested for ability to characterize gastroenteropancreatic (GEP) neuroendocrine tumors (NETs). Methods: For this single-center, cross-sectional study, 22 patients with 24 histologically confirmed GEP NET lesions (15 men and 7 women; median age, 61 y; range, 43-81 y) who underwent hybrid ⁶⁸Ga-DOTA PET/MRI at 3 T between January 2017 and July 2019 met the eligibility criteria. SUV, tumor-to-background ratio, total functional tumor volume, and mean and minimum ADC were measured on the basis of volumes of interest and examined with receiver-operating-characteristic analysis to determine cutoffs for differentiation between low- and intermediate-grade GEP NETs. The Spearman rank correlation coefficient was used to assess correlations between functional imaging parameters. Results: The ratio of PET-derived SUV_mean and diffusion-weighted imaging-derived minimum ADC was introduced as a combined variable to predict tumor grade, outperforming single predictors. On the basis of a threshold ratio of 0.03, tumors could be classified as grade 2 with a sensitivity of 86% and a specificity of 100%. SUV and functional ADCs, as well as arterial contrast enhancement parameters, showed nonsignificant and mostly negligible correlations. Conclusion: Because receptor density and tumor cellularity appear to be independent, potentially complementary phenomena, the combined ratio of PET/MRI and SUV_mean/ADC_min may be used as a novel biomarker allowing differentiation between grade 1 and grade 2 GEP NETs.

Evaluation of Texture Analysis for the Differential Diagnosis of Mass-Forming Pancreatitis From Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced CT Images

Purpose: To investigate the potential of computed tomography (CT) imaging features and texture analysis to differentiate between mass-forming pancreatitis (MFP) and pancreatic ductal adenocarcinoma (PDAC).

Materials and Methods: Thirty patients with pathologically proved MFP and 79 patients with PDAC were included in this study. Clinical data and CT imaging features of the two lesions were evaluated. Texture features were extracted from arterial and portal phase CT images using commercially available software (AnalysisKit). Multivariate logistic regression analyses were used to identify relevant CT imaging and texture parameters to discriminate MFP from PDAC. Receiver operating characteristic curves were performed to determine the diagnostic performance of predictions.

Results: MFP showed a larger size compared to PDAC (p = 0.009). Cystic degeneration, pancreatic ductal dilatation, vascular invasion, and pancreatic sinistral portal hypertension were more frequent and duct penetrating sign was less frequent in PDAC compared to MFP. Arterial CT attenuation, arterial, and portal enhancement ratios of MFP were higher than PDAC (p < 0.05). In multivariate analysis, arterial CT attenuation and pancreatic duct penetrating sign were independent predictors. Texture features in arterial phase including SurfaceArea, Percentile40, InverseDifferenceMoment_angle90_offset4, LongRunEmphasis_angle45_offset4, and uniformity were independent predictors. Texture features in portal phase including LongRunEmphasis_angle135_offset7, VoxelValueSum, LongRunEmphasis_angle135_offset4, and GLCMEntropy_angle45_offset1 were independent predictors. Areas under the curve of imaging feature-based, texture feature-based in arterial and portal phases, and the combined models were 0.84, 0.96, 0.93, and 0.98, respectively.

Conclusions: CT texture analysis demonstrates great potential to differentiate MFP from PDAC.

Unusual Case of a Pancreatic Neuroendocrine Tumor Containing a Central Scar

We report a previously unreported case of a pancreatic neuroendocrine tumor with a central scar mimicking a serous neoplasm. To our knowledge, this atypical imaging morphology of pancreatic neuroendocrine tumor has not been described before. Our report adds to the body literature that describes atypical imaging variants of neuroendocrine tumors and highlights that clinicians should be aware of the broad imaging characteristics of neuroendocrine tumors.

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

PURPOSE

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

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.

Interesting pancreatic tumour in the background of Tuberous Sclerosis

Tuberous Sclerosis Complex (TSC) is easily discernible by a myriad of manifestations, most notably dermatological. It is associated with well known and recognised intra-abdominal tumours like angiomyolipoma of the kidney. However, rarer tumours like pancreatic neuroendocrine tumours can occur in the setting of TSC. A high index of suspicion is necessary to identify and treat these lesions early in their natural course. Early identification augurs well with complete surgical excision and excellent survival.