Prediction of pancreatic neuroendocrine tumour grade with MR imaging features: added value of diffusion-weighted imaging

Differentiation of intrapancreatic accessory spleen from pancreatic neuroendocrine tumor using MRI R2

PURPOSE

To evaluate the performance of R2* in distinguishing intrapancreatic accessory spleens (IPASs) from pancreatic neuroendocrine tumors (PNETs).

METHODS

Two radiologists (R1 and R2) retrospectively reviewed the MRIs of 20 IPAS and 20 PNET patients. IPASs were diagnosed with uptake on 99mTc labeled heat-damaged red blood cell scintigraphy or characteristic findings on CT/MRI and ≥ 12 month-long-stability. PNETs were histopathologically diagnosed with resection. Using McNemar test, sensitivities and specificities of the diagnostic criterion based on R2* mass-to-spleen ratio (MSR) were compared with those of the other criteria using contrast-enhanced (CE) MRI and apparent diffusion coefficient (ADC) MSR.

RESULTS

The study included 40 patients (median age, 54; interquartile range, 43-65; 24 men, 16 women). IPASs exhibited spleen-isointensity on T2WI, late arterial and portal phases, and diffusion-weighted images more frequently than PNETs (p <.05). ADC MSRs were lower (p <.001) and R2* MSRs were higher (p <.001) in IPASs compared to PNETs. For R1, sensitivity and specificity were 45.0% and 100.0% for criterion 1 (spleen-isointensity on CE-MRI); 45.0% and 85.0% for criterion 2 (ADC MSR ≤ 1.08); 90.0% and 95.0% for criterion 3 (0.9 ≤ R2* MSR ≤ 1.7). For R2, 75.0% and 100.0%; 45.0% and 90.0%; 90.0% and 100.0%. Criterion 3 showed higher sensitivity than criterion 1 for R1 (p =.004), and criterion 2 for R1 and R2 (p =.012). There was no difference in specificity.

CONCLUSION

For differentiating IPAS from PNET, R2* showed higher sensitivity than, and similar specificity to CE-MRI and ADC.

[Imaging of pancreatic neuroendocrine tumors]

BACKGROUND

Neuroendocrine tumors of the pancreas have a broad biological spectrum. The treatment decision is based on an optimal diagnosis with regard to the local findings and possible locoregional and distant metastases. In addition to purely morphologic imaging procedures, functional parameters are playing an increasingly important role in imaging.

OBJECTIVES

Prerequisites for optimal imaging of the pancreas, technical principles are provided, and the advantages and disadvantages of common cross-sectional imaging techniques as well as clinical indications for these special imaging methods are discussed.

MATERIALS AND METHODS

Guidelines, basic and review papers will be analyzed.

RESULTS

Neuroendocrine tumors of the pancreas have a broad imaging spectrum. Therefore, there is a need for multimodality imaging in which morphologic and functional techniques support each other. While positron emission tomography/computed tomography (PET/CT) can determine the presence of one or more lesions and its/their functional status of the tumor, magnetic resonance imaging (MRI) efficiently identifies the location, relationship to the main duct and the presence of liver metastases. CT allows a better vascular evaluation, even in the presence of anatomical variants as well as sensitive detection of lung metastases.

CONCLUSIONS

Knowledge of the optimal combination of imaging modalities including clinical and histopathologic results and dedicated imaging techniques is essential to achieve an accurate diagnosis to optimize treatment decision-making and to assess therapy response.

Liver Imaging in Gastroenteropancreatic Neuroendocrine Neoplasms

ABSTRACT

The incidence of neuroendocrine neoplasms (NENs) has gradually increased over the past few decades with the majority of patients presenting with metastases on initial presentation. The liver is the most common site of initial metastatic disease, and the presence of liver metastasis is an independent prognostic factor associated with a negative outcome. Because NENs are heterogenous neoplasms with variable differentiation, grading, and risk of grade transformation over time, accurate diagnosis and management of neuroendocrine liver lesions are both important and challenging. This is particularly so with the multiple liver-directed treatment options available. In this review article, we discuss the diagnosis, treatment, and response evaluation of NEN liver metastases.

Synchronous neuroendocine liver metastases in comparison to primary pancreatic neuroendocrine tumors on MRI and SSR-PET/CT

Background

The study aimed to compare and correlate morphological and functional parameters in pancreatic neuroendocrine tumors (pNET) and their synchronous liver metastases (NELM), while also assessing prognostic imaging parameters.

Methods

Patients with G1/G2 pNET and synchronous NELM underwent pretherapeutic abdominal MRI with DWI and 68Ga-DOTATATE/TOC PET/CT were included. ADC (mean, min), SNR_art and SNT_T2 (SNR on arterial phase and on T2) and SUV (max, mean) for three target NELM and pNET, as well as tumor-free liver and spleen (only in PET/CT) were measured. Morphological parameters including size, location, arterial enhancement, cystic components, T2-hyperintensity, ductal dilatation, pancreatic atrophy, and vessel involvement were noted. Response evaluation used progression-free survival (PFS) with responders (R;PFS>24 months) and non-responders (NR;PFS ≤ 24 months).

Results

33 patients with 33 pNETs and 95 target NELM were included. There were no significant differences in ADC and SUV values between NELM and pNET. 70% of NELM were categorized as hyperenhancing lesions, whereas the pNETs exhibited significantly lower rate (51%) of hyperenhancement (p<0.01) and significant lower SNR_art. NELM were qualitatively and quantitatively (SNR_T2) significantly more hyperintense on T2 compared to pNET (p=0.01 and p<0.001). NELM of R displayed significantly lower ADCmean value in comparison to the ADC mean value of pNET (0.898 versus 1.037x10-3mm²/s,p=0.036). In NR, T2-hyperintensity was notably higher in NELM compared to pNET (p=0.017). The hepatic tumor burden was significantly lower in the R compared to the NR (10% versus 30%).

Conclusions

Arterial hyperenhancement and T2-hyperintensity differ between synchronous NELM and pNET. These findings emphasize the importance of a multifaceted approach to imaging and treatment planning in patients with these tumors as well as in predicting treatment responses.

Quantitative Magnetic Resonance Imaging for the Pancreas: Current Status

ABSTRACT

Magnetic resonance imaging (MRI) is important for evaluating pancreatic disorders, and anatomical landmarks play a major role in the interpretation of results. Quantitative MRI is an effective diagnostic modality for various pathologic conditions, as it allows the investigation of various physical parameters. Recent advancements in quantitative MRI techniques have significantly improved the accuracy of pancreatic MRI. Consequently, this method has become an essential tool for the diagnosis, treatment, and monitoring of pancreatic diseases. This comprehensive review article presents the currently available evidence on the clinical utility of quantitative MRI of the pancreas.

Prediction of the lymphatic, microvascular, and perineural invasion of pancreatic neuroendocrine tumors using preoperative magnetic resonance imaging

BACKGROUND

Significant correlation between lymphatic, microvascular, and perineural invasion (LMPI) and the prognosis of pancreatic neuroendocrine tumors (PENTs) was confirmed by previous studies. There was no previous study reported the relationship between magnetic resonance imaging (MRI) parameters and LMPI.

AIM

To determine the feasibility of using preoperative MRI of the pancreas to predict LMPI in patients with non-functioning PENTs (NFPNETs).

METHODS

A total of 61 patients with NFPNETs who underwent MRI scans and lymphadenectomy from May 2011 to June 2018 were included in this retrospective study. The patients were divided into group 1 (n = 34, LMPI negative) and group 2 (n = 27, LMPI positive). The clinical characteristics and qualitative MRI features were collected. In order to predict LMPI status in NF-PNETs, a multivariate logistic regression model was constructed. Diagnostic performance was evaluated by calculating the receiver operator characteristic (ROC) curve with area under ROC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.

RESULTS

There were significant differences in the lymph node metastasis stage, tumor grade, neuron-specific enolase levels, tumor margin, main pancreatic ductal dilatation, common bile duct dilatation, enhancement pattern, vascular and adjacent tissue involvement, synchronous liver metastases, the long axis of the largest lymph node, the short axis of the largest lymph node, number of the lymph nodes with short axis > 5 or 10 mm, and tumor volume between two groups (P < 0.05). Multivariate analysis showed that tumor margin (odds ratio = 11.523, P < 0.001) was a predictive factor for LMPI of NF-PNETs. The area under the receiver value for the predictive performance of combined predictive factors was 0.855. The sensitivity, specificity, PPV, NPV and accuracy of the model were 48.1% (14/27), 97.1% (33/34), 97.1% (13/14), 70.2% (33/47) and 0.754, respectively.

CONCLUSION

Using preoperative MRI, ill-defined tumor margins can effectively predict LMPI in patients with NF-PNETs.

Stability and repeatability of diffusion-weighted imaging (DWI) of normal pancreas on 5.0 Tesla magnetic resonance imaging (MRI)

To explore the stability and repeatability of diffusion-weighted imaging (DWI) of normal pancreas with different field of views (FOV) on 5.0 T magnetic resonance imaging (MRI) system. Twenty healthy subjects underwent two sessions of large FOV (lFOV) and reduced FOV (rFOV) DWI sequence scanning. Two radiologists measured the apparent diffusion coefficient (ADC) values and the signal-to-noise ratio (SNR) of the pancreatic head, body, and tail on DWI images, simultaneously, using a 5-point scale, evaluate the artifacts and image quality. One radiologist re-measured the ADC on DWI images again after a 4-week interval. The test-retest repeatability of two scan sessions were also evaluated. Intra-observer and inter-observer at lFOV and rFOV, the ADC values were not significantly different (P > 0.05), intraclass correlation coefficients (ICCs) and coefficient of variations were excellence (ICCs 0.85-0.99, CVs < 8.0%). The ADC values were lower with rFOV than lFOV DWI for the head, body, tail, and overall pancreas. The consistency of the two scan sessions were high. The high stability and repeatability of pancreas DWI has been confirmed at 5.0 T. Scan durations are reduced while resolution and image quality are improved with rFOV DWI, which is more preferable than lFOV for routine pancreas imaging.

Status of Surveillance and Nonsurgical Therapy for Small Nonfunctioning Pancreatic Neuroendocrine Tumors

Pancreatic neuroendocrine tumors (PNETs) occur in < 1/100,000 patients and most are nonfunctioning (NF). Approximately 5% occur as part of multiple endocrine neoplasia type 1. Anatomic and molecular imaging have a pivotal role in the diagnosis, staging and active surveillance. Surgery is generally recommended for nonfunctional pancreatic neuroendocrine tumors (NF-PNETs) >2 cm to prevent metastases. For tumors ≤2 cm, active surveillance is a viable alternative. Tumor size and grade are important factors to guide management. Assessment of death domain-associated protein 6/alpha-thalassemia/mental retardation X-linked and alternative lengthening of telomeres are promising novel prognostic markers. This review summarizes the status of surveillance and nonsurgical management for small NF-PNETs, including factors that can guide management.

Discrimination of Liver Metastases of Digestive System Neuroendocrine Tumors From Neuroendocrine Carcinoma by Computed Tomography-Based Radiomics Analysis

OBJECTIVE

The aim of the study is to investigate the value of computed tomography (CT) radiomics features to discriminate the liver metastases (LMs) of digestive system neuroendocrine tumors (NETs) from neuroendocrine carcinoma (NECs).

METHODS

Ninety-nine patients with LMs of digestive system neuroendocrine neoplasms from 2 institutions were included. Radiomics features were extracted from the portal venous phase CT images by the Pyradiomics and then selected by using the t test, Pearson correlation analysis, and least absolute shrinkage and selection operator method. The radiomics score (Rad score) for each patient was constructed by linear combination of the selected radiomics features. The radiological model was constructed by radiological features using the multivariable logistic regression. Then, the combined model was constructed by combining Rad score and the radiological model into logistic regression. The performance of all models was evaluated by the receiver operating characteristic curves with the area under curve (AUC).

RESULTS

In the radiological model, only the enhancement degree (odds ratio, 8.299; 95% confidence interval, 2.070-32.703; P = 0.003) was an independent predictor for discriminating the LMs of digestive system NETs from those of NECs. The combined model constructed by the Rad score in combination with the enhancement degree showed good discrimination performance, with AUCs of 0.893, 0.841, and 0.740 in the training, testing, and external validation groups, respectively. In addition, it performed better than radiological model in the training and testing groups (AUC, 0.893 vs 0.726; AUC, 0.841 vs 0.621).

CONCLUSIONS

The CT radiomics might be useful for discrimination LMs of digestive system NECs from NETs.

Spectral Computed Tomography-Derived Iodine Content and Tumor Response in the Follow-Up of Neuroendocrine Tumors-A Single-Center Experience

Spectral computed tomography (SCT) allows iodine content (IC) calculation for characterization of hypervascularized neoplasms and thus might help in the staging of neuroendocrine tumors (NETs). This single-center prospective study analyzed the association between SCT-derived IC and tumor response in the follow-up of metastasized NETs. Twenty-six patients with a median age of 70 years (range 51-85) with histologically proven NETs and a total of 78 lesions underwent SCT for staging. Because NETS are rare, no primary NET types were excluded. Lesions and intralesional hotspots were measured in virtual images and iodine maps. Tumor response was classified as progressive or nonprogressive at study endpoint. Generalized estimating equations served to estimate associations between IC and tumor response, additionally stratified by lesion location. Most commonly affected sites were the lymph nodes, liver, pancreas, and bones. Median time between SCT and endpoint was 64 weeks (range 5-260). Despite statistical imprecision in the estimate, patients with higher IC in lymphonodular metastases had lower odds for disease progression (adjusted OR = 0.21, 95% CI: 0.02-2.02). Opposite tendencies were observed in hepatic and pancreatic metastases in unadjusted analyses, which vanished after adjusting for therapy and primary tumor grade.

Prediction of Pathological Grades of Pancreatic Neuroendocrine Tumors Based on Dynamic Contrast-Enhanced Ultrasound Quantitative Analysis

Objective: To investigate whether the dynamic contrast-enhanced ultrasound (DCE-US) analysis and quantitative parameters could be helpful for predicting histopathologic grades of pancreatic neuroendocrine tumors (pNETs). Methods: This retrospective study conducted a comprehensive review of the CEUS database between March 2017 and November 2021 in Zhongshan Hospital, Fudan University. Ultrasound examinations were performed by an ACUSON Sequioa unit equipped with a 3.5 MHz 6C−1 convex array transducer, and an ACUSON OXANA2 unit equipped with a 3.5 MHz 5C−1 convex array transducer. SonoVue® (Bracco Inc., Milan, Italy) was used for all CEUS examinations. Time intensity curves (TICs) and quantitative parameters of DCE-US were created by Vuebox® software (Bracco, Italy). Inclusion criteria were: patients with histopathologically proved pNETs, patients who underwent pancreatic B-mode ultrasounds (BMUS) and CEUS scans one week before surgery or biopsy and had DCE-US imaging documented for more than 2 min, patients with solid or predominantly solid lesions and patients with definite diagnosis of histopathological grades of pNETs. Based on their prognosis, patients were categorized into two groups: pNETs G1/G2 group and pNETs G3/pNECs group. Results: A total of 42 patients who underwent surgery (n = 38) or biopsy (n = 4) and had histopathologically confirmed pNETs were included. According to the WHO 2019 criteria, all pNETs were classified into grade 1 (G1, n = 10), grade 2 (G2, n = 21), or grade 3 (G3)/pancreatic neuroendocrine carcinomas (pNECs) (n = 11), based on the Ki−67 proliferation index and the mitotic activity. The majority of the TICs (27/31) of pNETs G1/G2 were above or equal to those of pancreatic parenchyma in the arterial phase, but most (7/11) pNETs G3/pNECs had TICs below those of pancreatic parenchyma from arterial phase to late phase (p < 0.05). Among all the CEUS quantitative parameters of DCE-US, values of relative rise time (rPE), relative mean transit time (rmTT) and relative area under the curve (rAUC) were significantly higher in pNETs G1/G2 group than those in pNETs G3/pNECs group (p < 0.05). Taking an rPE below 1.09 as the optimal cut-off value, the sensitivity, specificity and accuracy for prediction of pNETs G3/pNECs from G1/G2 were 90.91% [58.70% to 99.80%], 67.64% [48.61% to 83.32%] and 85.78% [74.14% to 97.42%], respectively. Taking rAUC below 0.855 as the optimal cut-off value, the sensitivity, specificity and accuracy for prediction of pNETs G3/pNECs from G1/G2 were 90.91% [66.26% to 99.53%], 83.87% [67.37% to 92.91%] and 94.72% [88.30% to 100.00%], respectively. Conclusions: Dynamic contrast-enhanced ultrasound analysis might be helpful for predicting the pathological grades of pNETs. Among all quantitative parameters, rPE, rmTT and rAUC are potentially useful parameters for predicting G3/pNECs with aggressive behavior.

Histogram-based analysis of diffusion-weighted imaging for predicting aggressiveness in papillary thyroid carcinoma

BACKGROUND

To assess the potential of apparent diffusion coefficient (ADC) map in predicting aggressiveness of papillary thyroid carcinoma (PTC) based on whole-tumor histogram-based analysis.

METHODS

A total of 88 patients with PTC confirmed by pathology, who underwent neck magnetic resonance imaging, were enrolled in this retrospective study. Whole-lesion histogram features were extracted from ADC maps and compared between the aggressive and non-aggressive groups. Multivariable logistic regression analysis was performed for identifying independent predictive factors. Receiver operating characteristic curve analysis was used to evaluate the performances of significant factors, and an optimal predictive model for aggressiveness of PTC was developed.

RESULTS

The aggressive and non-aggressive groups comprised 67 (mean age, 44.03 ± 13.99 years) and 21 (mean age, 43.86 ± 12.16 years) patients, respectively. Five histogram features were included into the final predictive model. ADC_firstorder_TotalEnergy had the best performance (area under the curve [AUC] = 0.77). The final combined model showed an optimal performance, with AUC and accuracy of 0.88 and 0.75, respectively.

CONCLUSIONS

Whole-lesion histogram analysis based on ADC maps could be utilized for evaluating aggressiveness in PTC.

New frontiers in imaging including radiomics updates for pancreatic neuroendocrine neoplasms

OBJECTIVE

To illustrate the applications of various imaging tools including conventional MDCT, MRI including DWI, CT & MRI radiomics, FDG & DOTATATE PET-CT for diagnosis, staging, grading, prognostication, treatment planning and assessing treatment response in cases of pancreatic neuroendocrine neoplasms (PNENs).

BACKGROUND

Gastroenteropancreatic neuroendocrine neoplasms (GEP NENs) are very diverse clinically & biologically. Their treatment and prognosis depend on staging and primary site, as well as histological grading, the importance of which is also reflected in the recently updated WHO classification of GEP NENs. Grade 3 poorly differentiated neuroendocrine carcinomas (NECs) are aggressive & nearly always advanced at diagnosis with poor prognosis; whereas Grades-1 and 2 well-differentiated neuroendocrine tumors (NETs) can be quite indolent. Grade 3 well-differentiated NETs represent a new category of neoplasm with an intermediate prognosis. Importantly, the evidence suggest grade heterogeneity can occur within a given tumor and even grade progression can occur over time. Emerging evidence suggests that several non-invasive qualitative and quantitative imaging features on CT, dual-energy CT (DECT), MRI, PET and somatostatin receptor imaging with new tracers, as well as texture analysis, may be useful to grade, prognosticate, and accurately stage primary NENs. Imaging features may also help to inform choice of treatment and follow these neoplasms post-treatment.

CONCLUSION

GEP NENs treatment and prognosis depend on the stage as well as histological grade of the tumor. Traditional ways of imaging evaluation for diagnosis and staging does not yet yield sufficient information to replace operative and histological evaluation. Recognition of important qualitative imaging features together with quantitative features and advanced imaging tools including functional imaging with DWI MRI, DOTATATE PET/CT, texture analysis with radiomics and radiogenomic features appear promising for more accurate staging, tumor risk stratification, guiding management and assessing treatment response.

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.

Development and Validation of a Novel Radiomics-Based Nomogram With Machine Learning to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors

BACKGROUD

Tumor grade is the determinant of the biological aggressiveness of pancreatic neuroendocrine tumors (PNETs) and the best current tool to help establish individualized therapeutic strategies. A noninvasive way to accurately predict the histology grade of PNETs preoperatively is urgently needed and extremely limited.

METHODS

The models training and the construction of the radiomic signature were carried out separately in three-phase (plain, arterial, and venous) CT. Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) were applied for feature preselection and radiomic signature construction. SVM-linear models were trained by incorporating the radiomic signature with clinical characteristics. An optimal model was then chosen to build a nomogram.

RESULTS

A total of 139 PNETs (including 83 in the training set and 56 in the independent validation set) were included in the present study. We build a model based on an eight-feature radiomic signature (group 1) to stratify PNET patients into grades 1 and 2/3 groups with an AUC of 0.911 (95% confidence intervals (CI), 0.908-0.914) and 0.837 (95% CI, 0.827-0.847) in the training and validation cohorts, respectively. The nomogram combining the radiomic signature of plain-phase CT with T stage and dilated main pancreatic duct (MPD)/bile duct (BD) (group 2) showed the best performance (training set: AUC = 0.919, 95% CI = 0.916-0.922; validation set: AUC = 0.875, 95% CI = 0.867-0.883).

CONCLUSIONS

Our developed nomogram that integrates radiomic signature with clinical characteristics could be useful in predicting grades 1 and 2/3 PNETs preoperatively with powerful capability.

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.

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.

Imaging of Pancreatic Neuroendocrine Neoplasms

Pancreatic neuroendocrine neoplasms (panNENs) represent the second most common pancreatic tumors. They are a heterogeneous group of neoplasms with varying clinical expression and biological behavior, from indolent to aggressive ones. PanNENs can be functioning or non-functioning in accordance with their ability or not to produce metabolically active hormones. They are histopathologically classified according to the 2017 World Health Organization (WHO) classification system. Although the final diagnosis of neuroendocrine tumor relies on histologic examination of biopsy or surgical specimens, both morphologic and functional imaging are crucial for patient care. Morphologic imaging with ultrasonography (US), computed tomography (CT) and magnetic resonance imaging (MRI) is used for initial evaluation and staging of disease, as well as surveillance and therapy monitoring. Functional imaging techniques with somatostatin receptor scintigraphy (SRS) and positron emission tomography (PET) are used for functional and metabolic assessment that is helpful for therapy management and post-therapeutic re-staging. This article reviews the morphological and functional imaging modalities now available and the imaging features of panNENs. Finally, future imaging challenges, such as radiomics analysis, are illustrated.

Target Heterogeneity in Oncology: The Best Predictor for Differential Response to Radioligand Therapy in Neuroendocrine Tumors and Prostate Cancer

Simple Summary

In the era of precision medicine, novel targets have emerged on the surface of cancer cells, which have been exploited for the purpose of radioligand therapy. However, there have been variations in the way these receptors are expressed, especially in prostate cancers and neuroendocrine tumors. This variable expression of receptors across the grades of cancers led to the concept of ‘target heterogeneity’, which has not just impacted therapeutic decisions but also their outcomes. Radiopharmaceuticals targeting receptors need to be used when there are specific indicators—either clinical, radiological, or at molecular level—warranting their use. In addition, response to these radioligands can be assessed using different techniques, whereby we can prognosticate further outcomes. We shall also discuss, in this review, the conventional as well as novel approaches of detecting heterogeneity in prostate cancers and neuroendocrine tumors.

Abstract

Tumor or target heterogeneity (TH) implies presence of variable cellular populations having different genomic characteristics within the same tumor, or in different tumor sites of the same patient. The challenge is to identify this heterogeneity, as it has emerged as the most common cause of ‘treatment resistance’, to current therapeutic agents. We have focused our discussion on ‘Prostate Cancer’ and ‘Neuroendocrine Tumors’, and looked at the established methods for demonstrating heterogeneity, each with its advantages and drawbacks. Also, the available theranostic radiotracers targeting PSMA and somatostatin receptors combined with targeted systemic agents, have been described. Lu-177 labeled PSMA and DOTATATE are the ‘standard of care’ radionuclide therapeutic tracers for management of progressive treatment-resistant prostate cancer and NET. These approved therapies have shown reasonable benefit in treatment outcome, with improvement in quality of life parameters. Various biomarkers and predictors of response to radionuclide therapies targeting TH which are currently available and those which can be explored have been elaborated in details. Imaging-based features using artificial intelligence (AI) need to be developed to further predict the presence of TH. Also, novel theranostic tools binding to newer targets on surface of cancer cell should be explored to overcome the treatment resistance to current treatment regimens.

Ferumoxytol-enhanced MR imaging for differentiating intrapancreatic splenules from other tumors

Objectives

Ferumoxytol is an ultra-small superparamagnetic iron oxide (USPIO) agent that is taken up by splenic tissue. This study describes our initial institutional experience of ferumoxytol-enhanced MRI (feMRI) for differentiating intrapancreatic splenules (IPS) from other pancreatic lesions.

Methods

In this retrospective study, patients with computed tomographic imaging that identified small enhancing lesions in the tail of the pancreas subsequently underwent feMRI for further characterization. The feMRI protocol included T2-weighted (T2w) imaging with and without fat suppression (FS), R2* mapping, diffusion-weighted imaging (DWI), and T1-weighted (T1w) imaging with FS, prior to contrast injection. Immediately after slow intravenous infusion with 3 mg/kg body weight ferumoxytol, T1w was repeated. Delayed imaging with all sequences were obtained 24–72 h after ferumoxytol administration.

Results

Seven patients underwent feMRI. In two patients, the pancreatic lesions were presumed as pancreatic neuroendocrine tumor (PNET) from feMRI and in the remaining 5 IPS. One of the two patients with PNET was symptomatic for NET. In another symptomatic patient with pathologically proven duodenal NET and suspected PNET, the pancreatic lesion was proven to be an IPS on feMRI. IPS demonstrated strong negative enhancement in feMRI on T2w and increased R2* values consistent with splenic tissue, while the presumed PNETs did not enhance. T2w FS was helpful on the pre-contrast images to identify IPS, while R2* did on post-contrast images. Neither DWI nor T1w contributed to differentiating PNETs from IPS.

Conclusions

This study demonstrates the potential utility of feMRI as a helpful adjunct diagnostic tool for differentiating IPS from other pancreatic lesions. Further studies in larger patient cohorts are needed.

Reduced Field-of-View Diffusion-Weighted Magnetic Resonance Imaging of the Pancreas With Tilted Excitation Plane: A Preliminary Study

BACKGROUND

Reduced field-of-view diffusion-weighted imaging (rDWI) with tilted two-dimensional radiofrequency (RF) excitation planes has not yet been applied to the imaging of the pancreas although the utility of this technique which allows the acquisition of high-quality images without aliasing artifacts in the phase-encoding direction has been evaluated for brain and spinal cord imaging.

PURPOSE

To evaluate the visual image quality of the pancreas by tilting the excitation plane (tilted rDWI) in comparison to conventional DWI (cDWI) and rDWI without using the tilted excitation plane.

STUDY TYPE

Retrospective.

POPULATION

Thirty-two patients evaluated for suspected pancreatobiliary diseases.

FIELD STRENGTH/SEQUENCE

Echo-planar imaging DWI (cDWI, rDWI, and tilted rDWI) acquired at 3 T.

ASSESSMENT

Images from each DWI sequence were analyzed by five radiologists to compare image quality (conspicuity of pancreatic edges, interslice signal homogeneity, overall image quality, and conspicuity of focal pancreatic lesions) and artifacts (presence of blurring or ghosting artifacts, susceptibility artifacts, and aliasing artifact).

STATISTICAL TESTS

Shapiro-Wilk test was performed to assess whether data were normally distributed. Friedman test followed by Bonferroni-adjusted Wilcoxon signed-rank test for post hoc analysis was performed to compare image quality and artifact scores.

RESULTS

The mean scores for conspicuity of pancreatic edges (3.36 vs. 2.37), interslice signal homogeneity (3.14 vs. 2.81), presence of ghosting artifacts (3.32 vs. 2.66), susceptibility artifacts (3.06 vs. 2.30), and aliasing artifacts (3.90 vs. 2.34), and overall image quality (3.49 vs. 2.36) were significantly higher in the tilted rDWI than in the rDWI (P < 0.017 for all parameters). The conspicuity score for focal pancreatic lesions tended to be higher in tilted rDWI than in rDWI (2.44 vs. 2.00, P = 0.07).

DATA CONCLUSION

Tilted rDWI had better image quality and reduced artifacts relative to cDWI and rDWI techniques in the pancreas.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Correlation between contrast enhancement, standardized uptake value (SUV), and diffusion restriction (ADC) with tumor grading in patients with therapy-naive neuroendocrine neoplasms using hybrid 68Ga-DOTATOC PET/MRI

OBJECTIVES

To investigate a correlation between ⁶⁸Ga-DOTATOC PET/MR imaging parameters such as arterial and venous contrast enhancement, diffusion restriction, and maximum standardized uptake value (SUVmax) with histopathological tumor grading in patients with neuroendocrine neoplasms (NEN).

MATERIAL AND METHODS

A total of 26 patients with newly diagnosed, therapy-naive neuroendocrine neoplasms (NEN) were enrolled in this prospective study and underwent ⁶⁸Ga-DOTATOC PET/MRI. Images were evaluated regarding NEN lesion number and location, predominant tumor signal intensity on precontrast T1w and T2w images and on postcontrast arterial and portal venous phase T1w images, apparent diffusion coefficient (ADC) and SUVmax. Histopathological tumor grading was assessed and related to PET/MRI features using Pearson's correlation coefficient and Fisher's exact t-test. A binary logistic regression analysis was performed to evaluate a potential relation with an aggressive tumor biology and odds ratios (OR) were calculated.

RESULTS

There was a moderate negative correlation between arterial contrast enhancement and tumor grading (r=-0.35, p = 0.005), while portal venous enhancement showed a weak positive correlation with the Ki-67 index (r = 0.28, p = 0.008) and a non-significant positive correlation with tumor grading (r = 0.19, p = 0.063). Features that were significantly associated with an aggressive tumor biology were the presence of liver metastases (OR 2.6, p = 0.042), T1w hyperintensity in comparison to muscle (OR 12.7, p = 0.0001), arterial phase hyperenhancement (OR 1.4, p = 0.001), diffusion restriction (OR 2.8, p = 0.02) and SUVmax above the hepatic level (OR 7.0, p = 0.001).

CONCLUSION

The study reveals that PET/MRI features might be useful for prediction of NEN grading and thus provide a preliminary assessment of tumor aggressiveness.

Magnetic Resonance Imaging May Be Able to Identify the Origin of Neuroendocrine Tumor Liver Metastases

OBJECTIVES

The aim of the study was to discriminate hepatic metastases from pancreatic neuroendocrine tumors (pNET) and hepatic metastases from midgut neuroendocrine tumors (mNET) with magnetic resonance imaging (MRI).

METHODS

MRI examinations of 24 patients with hepatic metastases from pNET were quantitatively and qualitatively assessed by 2 blinded readers and compared to those obtained in 23 patients with hepatic metastases from mNET. Inter-reader agreement was calculated with kappa and intraclass correlation coefficient (ICC). Sensitivity, specificity, and accuracy of each variable for the diagnosis of hepatic metastasis from pNET were calculated. Associations between variables and primary tumor (i.e., pNET vs. mNET) were assessed by univariate and multivariate analyses. A nomogram was developed and validated using an external cohort of 20 patients with pNET and 20 patients with mNET.

RESULTS

Interobserver agreement was strong to perfect (k = 0.893-1) for qualitative criteria and excellent for quantitative variables (ICC: 0.9817-0.9996). At univariate analysis, homogeneity on T1-weighted images was the most discriminating variable for the diagnosis of pNET (OR: 6.417; p = 0.013) with greatest sensitivity (88%; 21/24; 95% CI: 68-97%). At multivariate analysis, tumor homogeneity on T1-weighted images (p = 0.007; OR: 17.607; 95% CI: 2.179-142.295) and target sign on diffusion-weighted images (p = 0.007; OR: 19.869; 95% CI: 2.305-171.276) were independently associated with pNET. Nomogram yielded a corrected AUC of 0.894 (95% CI: 0.796-0.992) for the diagnosis of pNET in the training cohort and 0.805 (95% CI: 0.662-0.948) in the validation cohort.

CONCLUSIONS

MRI provides qualitative features that can help discriminate between hepatic metastases from pNET and those from mNET.

Prediction of the World Health Organization Grade of rectal neuroendocrine tumors based on CT histogram analysis

OBJECTIVES

To investigate the diagnostic value of contrast-enhanced computed tomography (CECT) histogram analysis in predicting the World Health Organization (WHO) grade of rectal neuroendocrine tumors (R-NETs).

MATERIALS AND METHODS

A total of 61 (35 G1, 12 G2, 10 G3, and 4 NECs) patients who underwent preoperative CECT and treated with surgery to be confirmed as R-NETs were included in this study from January 2014 to May 2019. We depicted ROIs and measured the CECT texture parameters (mean, median, 10th, 25th, 75th, 90th percentiles, skewness, kurtosis, and entropy) from arterial phase (AP) and venous phase (VP) images by two radiologists. We calculated intraclass correlation coefficient (ICC) and compared the histogram parameters between low-grade (G1) and higher grade (HG) (G2/G3/NECs) by applying appropriate statistical method. We obtained the optimal parameters to identify G1 from HG using receiver operating characteristic (ROC) curves.

RESULTS

The capability of AP and VP histogram parameters for differentiating G1 from HG was similar in several histogram parameters (mean, median, 10th, 25th, 75th, and 90th percentiles) (all p < 0.001). Skewness, kurtosis, and entropy on AP images showed no significant differences between G1 and HG (p = 0.853, 0.512, 0.557, respectively). Entropy on VP images was significantly different (p = 0.017) between G1 and HG, however, skewness and kurtosis showed no significant differences (p = 0.654, 0.172, respectively). ROC analysis showed a good predictive performance between G1 and HG, and the 75th (AP) generated the highest area under the curve (AUC = 0.871), followed by the 25th (AP), mean (VP), and median (VP) (AUC = 0.864). Combined the size of tumor and the 75th (AP) generated the highest AUC.

CONCLUSIONS

CECT histogram parameters, including arterial and venous phases, can be used as excellent indicators for predicting G1 and HG of rectal neuroendocrine tumors, and the size of the tumor is also an important independent predictor.

Prospective Comparison of Reduced Field-of-View (rFOV) and Full FOV (fFOV) Diffusion-Weighted Imaging (DWI) in the Assessment of Insulinoma: Image Quality and Lesion Detection

RATIONALE AND OBJECTIVES

To prospectively compare the image quality (IQ) and lesion detection performance of reduced field-of-view (rFOV) and full FOV (fFOV) diffusion-weighted imaging (DWI) sequences in detecting insulinomas.

MATERIALS AND METHODS

From October 2017 to September 2018, 67 patients with suspected insulinomas were prospectively enrolled and underwent imaging with both types of DWI sequences. The slice thickness (4 mm) and slice gaps (1 mm) were the same for the two DWI sequences, and the TR/TE was 2235/56 ms for the rFOV sequence and 1892/63 ms for the fFOV sequence. Three radiologists independently assessed the imaging quality (IQ) subjectively with a 5-point scale and objectively with signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements. The IQ scores, CNR, SNR, lesion detection rates, and ADC values were compared. Receiver operating characteristic curves were generated, and the area under the curve (AUC) was used to compare the diagnostic performance.

RESULTS

Fifty patients were tumor positive, with 65 tumors (size: 1.31 ± 0.77 cm, range: 0.6-5.8 cm). The IQ score, SNR, and CNR were significantly higher for rFOV DWI than for fFOV DWI (IQ: 3.64 ± 0.487 vs 3.310 ± 0.577, SNR: 22.520 ± 8.690 vs 10.284 ± 3.321, CNR: 3.454 ± 2.642 vs 1.327 ± 2.801, and all p < 0.05). For lesions less than 1.5 cm (n = 55), the lesion detection rates of the rFOV were statistically improved compared to those of the fFOV (90.7% vs. 75.9%, p = 0.039). The sensitivity of lesion detection was significantly improved with the rFOV-DWI sequences compared to that with the fFOV-DWI sequences (0.924 vs. 0.773, p = 0.013). The ADC values of the two DWI sequences were consistent for insulinomas and normal parenchyma.

CONCLUSION

Considering the improvements in overall IQ and lesion detection and the consistency of ADC measurements, we suggest that rFOV DWI is a reliable auxiliary alternative to fFOV DWI for clinical practice in the detection of pancreatic insulinomas.

Usefulness of texture analysis for grading pancreatic neuroendocrine tumors on contrast-enhanced computed tomography and apparent diffusion coefficient maps

PURPOSE

To determine whether texture analysis of contrast-enhanced computed tomography (CECT) and apparent diffusion coefficient (ADC) maps could predict tumor grade (G1 vs G2-3) in patients with pancreatic neuroendocrine tumor (PNET).

MATERIALS AND METHODS

Thirty-three PNETs (22 G1 and 11 G2-3) were retrospectively reviewed. Fifty features were individually extracted from the arterial and portal venous phases of CECT and ADC maps by two radiologists. Diagnostic performance was assessed by receiver operating characteristic curves while inter-observer agreement was determined by calculating intraclass correlation coefficients (ICCs).

RESULTS

G2-G3 tumors were significantly larger than G1. Seventeen features significantly differed among the two readers on univariate analysis, with ICCs > 0.6; the largest area under the curve (AUC) for features of each CECT phase and ADC map was log-sigma 1.0 joint-energy = 0.855 for the arterial phase, log-sigma 1.5 kurtosis = 0.860 for the portal venous phase, and log-sigma 1.0 correlation = 0.847 for the ADC map. The log-sigma 1.5 kurtosis of the portal venous phase showed the largest AUC in the CECT and ADC map, and its sensitivity, specificity, and accuracy were 95.5%, 72.7%, and 87.9%, respectively.

CONCLUSION

Texture analysis may aid in differentiating between G1 and G2-3 PNET.

Diffusion-Weighted MRI Predicts Lymph Node Metastasis and Tumor Aggressiveness in Resectable Pancreatic Neuroendocrine Tumors

OBJECTIVES

The aim of this study was to identify whether diffusion-weighted magnetic resonance imaging (DW-MRI) can predict the malignant behavior of preoperative well-differentiated pancreatic neuroendocrine tumors (PanNETs).

METHOD

Forty patients with PanNETs who underwent pancreatectomy were enrolled in this study. The apparent diffusion coefficient (ADC) values were measured. Clinicopathological factors were compared in patients with high ADC and low ADC values and in patients with and without lymph node metastasis (LNM).

RESULT

The low ADC group was significantly associated with higher Ki-67 index, higher mitotic count, larger tumor size, higher rate of LNM, and venous invasion. In patients with low ADC values, the incidence of LNMs was 33.3%. In patients with high ADC values, there were no patients with LNM being 0%. A significant negative correlation was found between the mean ADC values and the Ki-67 index and between the mean ADC values and the mitotic count. In multivariate analysis, neural invasion and mean ADC values ≤ 1458 were independent predictors of LNM.

CONCLUSION

ADC values obtained using DW-MRI in the preoperative assessment of patients with PanNETs might be a useful predictor of malignant potential, especially LNM.

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

PURPOSE

To compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).

MATERIALS AND METHODS

Patients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.

RESULTS

Thirty-seven patients (21 men, 16 women; mean age, 56±13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60±46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70±51 [SD] mm [range: 18 - 196mm] vs. 42±24 [SD] mm [range: 8 - 94mm], respectively; P=0.039), with more tumor necrosis (75% vs. 33%, respectively; P=0.030) and lower attenuation on precontrast (30±4 [SD] HU [range: 25-39 HU] vs. 37±6 [SD] [range: 25-45 HU], respectively; P=0.002) and on portal venous phase CT images (75±18 [SD] HU [range: 43 - 108 HU] vs. 92±19 [SD] HU [range: 46 - 117 HU], respectively; P=0.014). Hemorrhagic content on MRI was only observed in NEC (P=0.007). The mean ADC value was lower in NEC ([1.1±0.1 (SD)]×10^-3 mm²/s [range: (0.91 - 1.3)×10^-3 mm²/s] vs. [1.4±0.2 (SD)]×10^-3 mm²/s [range: (1.1 - 1.6)×10^-3 mm²/s]; P=0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7±0.2 [SD] [range: 4.2-5.1] vs. 4.5±0.4 [SD] [range: 3.7-4.9]; P=0.023).

CONCLUSION

Pancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.

The combination of hepatobiliary phase with Gd-EOB-DTPA and DWI is highly accurate for the detection and characterization of liver metastases from neuroendocrine tumor

OBJECTIVES

To compare the diagnostic accuracy of dynamic contrast-enhanced phases, hepatobiliary phase (HBP), and diffusion-weighted imaging (DWI) for the detection of liver metastases from neuroendocrine tumor (NET).

METHODS

Sixty-seven patients with suspected NET liver metastases underwent gadoxetic acid-enhanced MRI. Three radiologists read four imaging sets separately and independently: DWI, T2W+dynamic, T2WI+HBP, and DWI+HBP. Reference standard included all imaging, histological findings, and clinical data. Sensitivity and specificity were calculated and compared for each imaging set. Interreader agreement was evaluated by intraclass correlation coefficient (ICC). Univariate logistic regression was performed to evaluate lesion characteristics (size, ADC, and enhancing pattern) associated to false positive and negative lesions.

RESULTS

Six hundred twenty-five lesions (545 metastases, 80 benign lesions) were identified. Detection rate was significantly higher combining DWI+HBP than the other imaging sets (sensitivity 86% (95% confidence interval (CI) 0.845-0.878), specificity 94% (95% CI 0.901-0.961)). The sensitivity and specificity of the other sets were 82% and 65% for DWI, 88% and 69% for T2WI, and 90% and 82% for HBP+T2WI, respectively. The interreader agreement was statistically higher for both HBP sets (ICC = 0.96 (95% CI 0.94-0.97) for T2WI+HBP and ICC = 0.91 (95% CI 0.87-0.94) for DWI+HBP, respectively) compared with that for DWI (ICC = 0.76 (95% CI 0.66-0.83)) and T2+dynamic (ICC = 0.85 (95% CI 0.79-0.9)). High ADC values, large lesion size, and hypervascular pattern lowered the risk of false negative.

CONCLUSION

Given the high diagnostic accuracy of combining DWI+HBP, gadoxetic acid-enhanced MRI is to be considered in NET patients with suspected liver metastases. Fast MRI protocol using T2WI, DWI, and HBP is of interest in this population.

KEY POINTS

• The combined set of diffusion-weighted (DW) and hepatobiliary phase (HBP) images yields the highest sensitivity and specificity for neuroendocrine liver metastasis (NELM) detection. • Gadoxetic acid should be the contrast agent of choice for liver MRI in NET patients. • The combined set of HBP and DWI sequences could also be used as a tool of abbreviated MRI in follow-up or assessment of treatment such as somatostatin analogs.

Pancreatic Neuroendocrine Tumors in Patients with Multiple Endocrine Neoplasia Type 1: Diagnostic Value of Different MRI Sequences

BACKGROUND

MRI is a useful imaging modality to assess the presence of pancreatic neuroendocrine tumors (PNETs), allowing repeat monitoring examinations in multiple endocrine neoplasia type 1 (MEN-1) patients.

OBJECTIVES

We aimed to compare the diagnostic accuracy of conventional MRI sequences to identify which sequence better depicts the presence of PNETs in MEN-1 patients.

METHOD

We performed a retrospective analysis of consecutive MEN-1 patients who underwent a conventional MRI protocol to monitor previously proven PNETs. MRI sequences T1-w chemical shift (CS), T2-w HASTE, fat-suppressed (FS) T2-w HASTE, diffusion-weighted imaging (DWI), and pre- and post-contrast FS T1-w sequences were independently analyzed by 2 experienced radiologists using a 3-grade score (no lesion, uncertain lesion, and certain lesion); lesion size and signal intensity were recorded. A Friedman ANOVA and a Wilcoxon pairwise test for the post hoc analysis were used. The sensitivity of each sequence was measured, and the results were analyzed with the χ2 test.

RESULTS

We included 21 patients with a total of 45 PNETs proven by histology, endoscopic ultrasonography-guided fine-needle aspiration, CT, and nuclear medicine studies. A statistically significant (p < 0.01) difference was observed in the detection performance of each MRI sequence, particularly between DWI (91%) and T2-w FS (85%) sequences in comparison to the others (T1-w CS, T2-w, and pre- and post-contrast FS T1-w, ≤56% for all); no significant (p = 0.5) difference was found between the detection performance of DWI and T2-w FS sequences. No correlation was observed between the qualitative score of each sequence and lesion tumor size.

CONCLUSIONS

DWI and T2-w FS sequences proved to be the most accurate in the detection of PNETs, thus suggesting a role for an abbreviated MRI protocol without contrast medium administration for monitoring MEN-1 patients.

CD56 Expression Is Associated with Biological Behavior of Pancreatic Neuroendocrine Neoplasms

Purpose

CD56 is a neural cell adhesion molecule that plays a role in the cohesiveness of neuroendocrine cells. The aim of this study was to explore the biological values of CD56 expression in pancreatic neuroendocrine neoplasms (PNENs) and its role in predicting PNENs grades.

Patients and Methods

A total of 138 patients with histological-proven PNENs was included (66 G1, 46 G2 and 26 G3). The clinicopathological characteristics, including mitosis count, ki67 index, chromogranin A (CgA), synaptophysin (Syn) and CD56 expression, were evaluated. We assessed the diagnostic performance of markers in predicting PNEN G3 and the association between CD56 expression and risk of G3 or organs invasion.

Results

Lack of CD56 immunoreaction (CD56-) was more common in PNEN G3 than G1/G2 (31% vs 0–2%, p < 0.01). The sizes of CD56- tumors were larger than CD56 positive tumors in PNEN G3 (p < 0.01). The odds ratio (OR) of CD56- expression was 13.6 [95% confidence interval (CI): 2.1–88.1] in predicting PNEN G3. The OR of CD56- expression was 6.5 (95% CI: 1.1–38.6) and 31.9 (95% CI: 1.09–938.3) in predicting organs invasion and neuroendocrine carcinoma in PNEN G3, respectively. Tumor size (area under the curve [AUC] = 0.77 and size+CD56- expression [AUC = 0.84]) had acceptable performance in predicating PNEN G3.

Conclusion

Lack of CD56 immunoreaction may be a predictor and biological behavior marker for PNEN G3.

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.

Correlation Between Apparent Diffusion Coefficient Value on MRI and Histopathologic WHO Grades of Neuroendocrine Tumors

Background

The correlation of diffusion-weighted MRI and tumor aggressiveness has been established for different tumor types, which leads to the question if it could also apply for neuroendocrine tumors (NET).

Purpose

To investigate the possible correlation between apparent diffusion coefficient (ADC) value on magnetic resonance imaging (MRI) and histopathologic WHO-grades of NET.

Material and Methods

Electronic patient records from patients presented at the multidisciplinary neuro-endocrine tumor board between November 2017 and April 2019 were retrospectively reviewed. Patients with both available MR imaging (primary tumor or metastasis) and known WHO tumor grade were included (n = 47). Average and minimum ADC values (avgADC; minADC) were measured by drawing a freehand ROI excluding only the outermost border of the lesion. The largest axial size (primary tumor) or most clearly delineated lesion (metastasis) was used.

Results

Forty seven patients met the inclusion criteria (mean age 59 ± 12 SD; 24F/23M). Twenty one patients (45%) were diagnosed with WHO G1 tumor, 17 seventeen with G2 (36%) and nine with G3 (19%) tumor. Twenty eight primary tumors and 19 metastases were measured. A significant difference was found between low-grade (G1+G2) and high-grade (G3) tumors (Mann-Whitney; avgADC: p < 0,001; minADC: p = 0,001). There was a moderate negative correlation between WHO-grade and avgADC/minADC (Spearman; avgADC: -0,606; 95% CI [-0,773; -0,384]; minADC: -0,581; 95% CI [-0.759; -0.353]).

Conclusion

Our data show a significant difference in both average and minimum ADC values on MRI between low and high grade NET. A moderate negative correlation was found between histopathologic WHO grade and ADC value.

The North American Neuroendocrine Tumor Society Consensus Paper on the Surgical Management of Pancreatic Neuroendocrine Tumors

This manuscript is the result of the North American Neuroendocrine Tumor Society consensus conference on the surgical management of pancreatic neuroendocrine tumors from July 19 to 20, 2018. The group reviewed a series of questions of specific interest to surgeons taking care of patients with pancreatic neuroendocrine tumors, and for each, the available literature was reviewed. What follows are these reviews for each question followed by recommendations of the panel.

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.

Differentiation between non-hypervascular pancreatic neuroendocrine tumour and pancreatic ductal adenocarcinoma on dynamic computed tomography and non-enhanced magnetic resonance imaging

Purpose

To determine the differentiating features between non-hypervascular pancreatic neuroendocrine tumour (PNET) and pancreatic ductal adenocarcinoma (PDAC) on dynamic computed tomography (CT) and non-enhanced magnetic resonance imaging (MRI).

Material and methods

We enrolled 102 patients with non-hypervascular PNET (n = 15) or PDAC (n = 87), who had undergone dynamic CT and non-enhanced MRI. One radiologist evaluated all images, and the results were subjected to univariate and multivariate analyses. To investigate reproducibility, a second radiologist re-evaluated features that were significantly different between PNET and PDAC on multivariate analysis.

Results

Tumour margin (well-defined or ill-defined) and enhancement ratio of tumour (ERT) showed significant differences in univariate and multivariate analyses. Multivariate analysis revealed a predominance of well-defined tumour margins in non-hypervascular PNET, with an odds ratio of 168.86 (95% confidence interval [CI]: 10.62-2685.29; p < 0.001). Furthermore, ERT was significantly lower in non-hypervascular PNET than in PDAC, with an odds ratio of 85.80 (95% CI: 2.57-2860.95; p = 0.01). Sensitivity, specificity, and accuracy were 86.7%, 96.6%, and 95.1%, respectively, when the tumour margin was used as the criteria. The values for ERT were 66.7%, 98.9%, and 94.1%, respectively. In reproducibility tests, both tumour margin and ERT showed substantial agreement (margin of tumour, κ = 0.6356; ERT, intraclass correlation coefficients (ICC) = 0.6155).

Conclusions

Non-hypervascular PNET showed well-defined margins and lower ERT compared to PDAC, with significant differences. Our results showed that non-hypervascular PNET can be differentiated from PDAC via dynamic CT and non-enhanced MRI.

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

PURPOSE

The purpose of this study was to evaluate the performance of magnetic resonance imaging (MRI) findings and texture parameters for prediction of the histopathologic grade of pancreatic neuroendocrine tumors (PNETs) with 3-T magnetic resonance.

PATIENTS AND METHODS

PNETs are classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity. A total of 77 patients with pathologically confirmed PNETs met the inclusion criteria. Texture analysis (TA) was applied to T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) maps. Patient demographics, MRI findings, and texture parameters were compared among three different histopathologic subtypes by using Fisher's exact tests or Kruskal-Wallis test. Then, logistic regression analysis was adopted to predict tumor grades. ROC curves and AUCs were calculated to assess the diagnostic performance of MRI findings and texture parameters in prediction of tumor grades.

RESULTS

There were 31 G1, 29 G2, and 17 G3 patients. Compared with G1, G2/G3 tumors showed higher frequencies of an ill-defined margin, a predominantly solid tumor type, local invasion or metastases, hypo-enhancement at the arterial phase, and restriction diffusion. Four T2-based (inverse difference moment, energy, correlation, and differenceEntropy) and five DWI-based (correlation, contrast, inverse difference moment, maxintensity, and entropy) TA parameters exhibited statistical significance among PNETs (P<0.001). The AUCs of six predicting models on T2WI and DWI ranged from 0.703-0.989.

CONCLUSION

Our data indicate that MRI findings, including tumor margin, texture, local invasion or metastases, tumor enhancement, and diffusion restriction, as well as texture parameters can aid the prediction of PNETs grading.

MR imaging features of pancreatic acinar cell carcinoma

PURPOSE

This study aimed to report the magnetic resonance imaging (MRI) features of acinar cell carcinoma (ACC) of the pancreas including diffusion-weighted MRI findings.

MATERIALS AND METHODS

The MRI examinations of five patients (3 men, 2 women; median age, 61years) with histopathologically proven ACC of the pancreas were retrospectively reviewed. MR images were analyzed qualitatively (location, shape, homogeneity, signal intensity, vascular involvement and extrapancreatic extent of ACC) and quantitatively (tumor size, apparent diffusion coefficient [ADC] and normalized ADC of ACC).

RESULTS

All ACC were visible on MRI, presenting as an oval pancreatic mass (5/5; 100%), with moderate and heterogeneous enhancement (5/5; 100%), with a median transverse diameter of 43mm (Q₁, 35; Q₃, 82mm; range: 30-91mm). Tumor capsule was visible in 4/5 ACC (80%) and Wirsung duct enlargement in 2/5 ACC (40%). On diffusion-weighted MRI, all ACC (5/5; 100%) were hyperintense on the 3 b value images. Median ADC value of ACC was 1.061×10^-3mm²/s (Q₁, 0.870×10^-3mm²/s; Q₃, 1.138×10^-3mm²/s; range: 0.834-1.195×10^-3mm²/s). Median normalized ADC ratio of ACC was 1.127 (Q₁, 1.071; Q₃, 1.237; range: 1.054-1.244).

CONCLUSIONS

On MRI, ACC of the pancreas presents as a large, oval pancreatic mass with moderate and heterogeneous enhancement after intravenous administration of a gadolinium chelate, with restricted diffusion and a median ADC value of 1.061×10^-3mm²/s on diffusion-weighted MRI. Further studies however are needed to confirm our findings obtained in a limited number of patients.

Pancreatic neuroendocrine tumors: MR imaging features preoperatively predict lymph node metastasis

PURPOSES

Predictive factors of lymph node metastasis (LNM) in pancreatic neuroendocrine tumors (pNETs) are not well established. We sought to identify the value of MR imaging features in preoperatively predicting the lymph node metastasis of pNETs.

MATERIALS AND METHODS

In this study, we enrolled 108 consecutive patients with pNETs between January 2009 and June 2018. MR morphologic features and quantitative data were evaluated. Predictors of LNM were evaluated using univariate and multivariate logistic regression models.

RESULTS

A total of 108 patients with pNETs were finally enrolled, including 82 LNM-negative and 26 LNM-positive patients. Features significantly related to the LNM of pNETs at univariate analysis were tumor size > 2 cm (P = 0.003), Ki-67 > 5% (P = 0.002), non-enhancement pattern (P < 0.001), apparent diffusion coefficient value (P < 0.001), main pancreatic duct dilation (P < 0.001) and pancreatic atrophy (P = 0.032) and extrapancreatic tumor spread (P = 0.001), CNRs during arterial, portal and delay phase (P = 0.005, 0.047, and 0.045, respectively), and histological classification (P = 0.006). At multivariate analysis, non-enhancement pattern (P = 0.019; odds ratio, 6.652; 95% CI 1.369, 32.321) and main pancreatic duct dilation (P = 0.018; odds ratio, 6.745; 95% CI 1.379, 32.991) were independent risk factors for predicting the LNM of pNETs.

CONCLUSION

The non-enhancement characteristic and main pancreatic duct dilation appear to be linked with LNM in pNETs. These radiological predictors can be easily obtained preoperatively, and may help to avoid missing pNETs with a high risk of LNM.

Advanced MR Imaging Techniques for Pancreas Imaging

Advances in MR imaging with optimization of hardware, software, and techniques have allowed for an increased role of MR in the identification and characterization of pancreatic disorders. Diffusion-weighted imaging improves the detection and staging of pancreatic neoplasms and aides in the evaluation of acute, chronic and autoimmune pancreatitis. The use of secretin-enhanced MR cholangiography improves the detection of morphologic ductal anomalies, and assists in the characterization of pancreatic cystic lesions and evaluation of acute and chronic pancreatitis. Emerging MR techniques such as MR perfusion, T1 mapping/relaxometry, and MR elastography show promise in further evaluating pancreatic diseases.

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.

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

OBJECTIVES

The aim of this study was to evaluate the diagnostic value of apparent diffusion coefficient (ADC) for the World Health Organization grade of pancreatic neuroendocrine tumors (pNETs).

METHODS

The MEDLINE, Google Scholar, PubMed, and Embase databases were searched to identify relevant original articles investigating the ADC value in predicting the grade of pNETs. The pooled sensitivity (SE), specificity (SP), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated by using random effects models. Subgroup analysis was performed to discover heterogeneity effects.

RESULTS

Nine studies with 386 patients met our inclusion criteria. For identifying G1 from G2/3, the pooled SE, SP, PLR, NLR, and area under the curve of the summary receiver operating characteristic curve were 0.84 (95% confidence interval [95% CI], 0.73-0.91), 0.87 (95% CI, 0.72-0.94), 6.3 (95% CI, 2.7-14.6), 0.19 (95% CI, 0.10-0.34), and 0.91 (95% CI, 0.89-0.94), respectively. The summary estimates for ADC in distinguishing G3 from G1/2 were as follows: SE, 0.93 (95% CI, 0.66-0.99); SP, 0.92 (95% CI, 0.86-0.95); PLR, 11.1 (95% CI, 6.6-18.6); NLR, 0.08 (95% CI, 0.01-0.45); and area under the curve, 0.92 (95% CI, 0.85-0.96).

CONCLUSIONS

Diffusion-weighted imaging is a reliable tool for predicting the grade of pNETs, especially for G3. Moreover, the combination of 3.0-T device and higher b value can slightly help improve SE and SP.

Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade

PURPOSE

Grades of pancreatic neuroendocrine neoplasms (PNENs) are associated with the choice of treatment strategies. Texture analysis has been used in tumor diagnosis and staging evaluation. In this study, we aim to evaluate the potential ability of texture parameters in differentiation of PNENs grades.

MATERIALS AND METHODS

37 patients with histologically proven PNENs and underwent pretreatment dynamic contrast-enhanced computed tomography examinations were retrospectively analyzed. Imaging features and texture features at contrast-enhanced images were evaluated. Receiver operating characteristic curves were used to determine the cut-off values and the sensitivity and specificity of prediction.

RESULTS

There were significant differences in tumor margin, pancreatic duct dilatation, lymph nodes invasion, size, portal enhancement ratio (PER), arterial enhancement ratio (AER), mean grey-level intensity, kurtosis, entropy, and uniformity among G1, G2, and pancreatic neuroendocrine carcinoma (PNEC) G3 (p < 0.01). Similar results were found between pancreatic neuroendocrine tumors (PNETs) G1/G2 and PNEC G3. AER and PER showed the best sensitivity (0.86-0.94) and specificity (0.92-1.0) for differentiating PNEC G3 from PNETs G1/G2. Mean grey-level intensity, entropy, and uniformity also showed acceptable sensitivity (0.73-0.91) and specificity (0.85-1.0). Mean grey-level intensity was also showed acceptable sensitivity (91% to 100%) and specificity (82% to 91%) in differentiating PNET G1 from PNET G2.

CONCLUSIONS

Our data indicated that texture parameters have potential in grading PNENs, in particular in differentiating PNEC G3 from PNETs G1/G2.

Intravoxel incoherent motion diffusion-weighted MR imaging of solid pancreatic masses: reliability and usefulness for characterization

PURPOSE

IVIM-DW imaging has shown potential usefulness in the study of pancreatic lesions. Controversial results are available regarding the reliability of the measurements of IVIM-derived parameters. The aim of this study was to evaluate the reliability and the diagnostic potential of IVIM-derived parameters in differentiation among focal solid pancreatic lesions and normal pancreas (NP).

METHODS

Fifty-seven patients (34 carcinomas-PDACs, 18 neuroendocrine neoplasms-panNENs, and 5 autoimmune pancreatitis-AIP) and 50 subjects with NP underwent 1.5-T MR imaging including IVIM-DWI. Images were analyzed by two independent readers. Apparent diffusion coefficient (ADC), slow component of diffusion (D), incoherent microcirculation (Dp), and perfusion fraction (f) were calculated. Interobserver reliability was assessed with intraclass correlation coefficient (ICC). A Kruskal-Wallis H test with Steel-Dwass post hoc test was used for comparison. The diagnostic performance of each parameter was evaluated through receiver operating characteristic (ROC) curve analysis.

RESULTS

Overall interobserver agreement was excellent (ICC = 0.860, 0.937, 0.968, and 0.983 for ADC, D, Dp, and f). D, Dp, and f significantly differed among PDACs and panNENs (p = 0.002, < 0.001, and < 0.001), albeit without significant difference at the pairwise comparison of ROC curves (p = 0.08-0.74). Perfusion fraction was higher in AIP compared with PDACs (p = 0.024; AUC = 0.735). Dp and f were higher in panNENs compared with AIP (p = 0.029 and 0.023), without differences at ROC analysis (p = 0.07).

CONCLUSIONS

IVIM-derived parameters have excellent reliability and could help in differentiation among solid pancreatic lesions and NP.