Staging accuracy of MR for pancreatic neuroendocrine tumor and imaging findings according to the tumor grade

Pancreatic neuroendocrine tumor: prediction of tumor grades by radiomics models based on ultrasound images

OBJECTIVE

We aimed to investigate whether the radiomics analysis based on B-mode ultrasound (BMUS) images could predict histopathological tumor grades in pancreatic neuroendocrine tumors (pNETs).

METHODS

A total of 64 patients with surgery and histopathologically confirmed pNETs were retrospectively included (34 male and 30 female, mean age 52.4 ± 12.2 years). Patients were divided into training cohort (n = 44) and validation cohort (n = 20). All pNETs were classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity according to WHO 2017 criteria. Maximum relevance minimum redundancy, least absolute shrinkage and selection operator were used for feature selection. Receiver operating characteristic curve analysis was used to evaluate the model performance.

RESULTS

Finally, 18 G1 pNETs, 35 G2 pNETs, and 11 G3 pNETs patients were included. The radiomic score derived from BMUS images to predict G2/G3 from G1 displayed a good performance with an area under the receiver operating characteristic curve of 0.844 in the training cohort, and 0.833 in the testing cohort. The radiomic score achieved an accuracy of 81.8% in the training cohort and 80.0% in the testing cohort, a sensitivity of 0.750 and 0.786, a specificity of 0.833 and 0.833 in the training/testing cohorts. Clinical benefit of the score also exhibited superior usefulness of the radiomic score, as shown by the decision curve analysis.

CONCLUSIONS

Radiomic data constructed from BMUS images have the potential for predicting histopathological tumor grades in patients with pNETs.

ADVANCES IN KNOWLEDGE

The radiomic model constructed from BMUS images has the potential for predicting histopathological tumor grades and Ki-67 proliferation indexes in patients with pNETs.

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.

Neuroendocrine neoplasm imaging: protocols by site of origin

With the relatively low incidence of neuroendocrine neoplasms (NEN), most radiologists are not familiar with their optimal imaging techniques. The imaging protocols for NENs should be tailored to the site of origin to accurately define local extension of NEN at time of staging. Patterns of spread and recurrence should be taken into consideration when choosing protocols for detection of recurrence and metastases. This paper will present the recommended CT and MRI imaging protocols for gastro-enteric and pancreatic NENs based on site of origin or predominant pattern of metastatic disease, and explain the rationale for MRI contrast type, contrast timing, as well as specific sequences in MRI. We will also briefly comment on PET/CT and PET/MRI imaging protocols.

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.

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.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Incidental 68Ga-DOTATATE uptake in the pancreatic head: A case report and a unique opportunity to improve clinical care

RATIONALE

Neuroendocrine tumors (NETs) are neoplasms that can arise from the neuroendocrine cells distributed widely throughout the body. Majority of NETs overexpress somatostatin receptors (SSTR) on their cell surface. This biologic characteristic is exploited by SSTR-based imaging such as In octreotide scintigraphy and Ga DOTATATE positron emission tomography (PET)/computed tomography (CT), which are considered standard for initial evaluation of NETs. Although highly sensitive and specific, recent reports demonstrate a concerning incidence of "false-positive" physiologic uptake of these tracers in the pancreatic head - a common site of neuroendocrine tumor (NET) involvement. We present false positive uptake on Ga DOTATATE PET/CT along with false positive CT findings. Role of other imaging modalities is discussed.

PATIENT CONCERNS

A 78-year-old woman presented with a year-long history of diarrhea.

DIAGNOSIS

Serum vasoactive intestinal peptide (VIP) levels were slightly elevated at 134.2 pg/mL (normal <75 pg/mL). CT showed a mildly enhancing 2.5 cm × 1.8 cm × 2.8 cm area in the pancreatic uncinate process which corresponded to focal uptake with Ga DOTATATE PET/CT. A presumptive diagnosis of pancreatic NET (vipoma) was made, and the patient was scheduled to undergo Whipple's surgery.

INTERVENTIONS

She sought a second opinion and a subsequent magnetic resonance imaging (MRI) showed no lesion and the patient's surgery was deferred. Thereafter, her VIP levels spontaneously normalized. Endoscopic ultrasound (EUS) with fine needle aspiration cytology of the uncinate process showed normal pancreatic acini with no evidence of NET.

OUTCOMES

Patient is currently pursuing workup for alternative etiologies for chronic diarrhea.

LESSONS

Conspicuous physiological uptake has been reported in the pancreatic head on 16% to 70% of Ga DOTATATE or Ga DOTANOC PET/CT scans, and 26% of the In octreotide scintigraphy scans. Image-based quantitative attempts to distinguish physiologic from pathologic uptake using SUVmax have rendered mixed results. When evaluating SSTR-based imaging uptake in the pancreatic head, patients can benefit from a higher index of suspicion of false positive uptake. Such cases require additional confirmation by MRI or EUS. Interestingly, the patient described also had mild contrast enhancement on CT, but without an MRI correlate. Because of potential morbidity and mortality related to false positive uptake, a systematic review with evidence-based recommendations for imaging may benefit patient care.

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.

Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors

BACKGROUND

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

PURPOSE

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

STUDY TYPE

Retrospective, single-center study.

SUBJECTS

Patients with pathologically confirmed PNETs (139) were included.

FIELD STRENGTH/SEQUENCE

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

ASSESSMENT

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

STATISTICAL TESTS

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

RESULTS

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

DATA CONCLUSION

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

LEVEL OF EVIDENCE

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

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 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.

The role of multimodal imaging in guiding resectability and cytoreduction in pancreatic neuroendocrine tumors: focus on PET and MRI

Pancreatic neuroendocrine tumors (pNETs) are rare neoplasms that secrete peptides and neuro-amines. pNETs can be sporadic or hereditary, syndromic or non-syndromic with different clinical presentations and prognoses. The role of medical imaging includes locating the tumor, assessing its extent, and evaluating the feasibility of curative surgery or cytoreduction. Pancreatic NETs have very distinctive phenotypes on CT, MRI, and PET. PET have been demonstrated to be very sensitive to detect either well-differentiated pNETs using ⁶⁸Gallium somatostatin receptor (SSTR) radiotracers, or more aggressive undifferentiated pNETS using ¹⁸F-FDG. A comprehensive interpretation of multimodal imaging guides resectability and cytoreduction in pNETs. The imaging phenotype provides information on the differentiation and proliferation of pNETs, as well as the spatial and temporal heterogeneity of tumors with prognostic and therapeutic implications. This review provides a structured approach for standardized reading and reporting of medical imaging studies with a focus on PET and MR techniques. It explains which imaging approach should be used for different subtypes of pNET and what a radiologist should be looking for and reporting when interpreting these studies.

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

OBJECTIVE

To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs).

METHODS

One hundred thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use.

RESULTS

The fusion radiomic signature has significant association with histologic grade (p < 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950-0.998) in the training cohort and 0.902 (95% CI 0.798-1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram.

CONCLUSION

We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients.

KEY POINTS

• Radiomic signature has strong discriminatory ability for the histologic grade of PNETs. • Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading. • The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.

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.

The differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma: the values of CT imaging features and texture analysis

BACKGROUND

Imaging findings for pancreatic neuroendocrine carcinoma (PNEC) and pancreatic ductal adenocarcinoma (PDAC) often overlap. The aim of this study was to demonstrate the value of computed tomography (CT) imaging features and texture analysis to differentiate PNEC from PDAC.

METHODS

Twenty-eight patients with pathologically-proved PDAC and 14 patients with PNEC were included in this study. CT imaging findings, including tumor boundary, size, enhancement degree, duct dilatation and parenchymal atrophy were used to compare PDAC and PNEC. CT texture features were extracted from CT images at the arterial and portal phases.

RESULTS

More PNEC than PDAC had well-defined margins (57.1% vs 25.0%, p = 0.04). Parenchymal atrophy was more common in PDAC than in PNEC (67.9% vs 28.1%, p = 0.02). CT attenuation values (HU) and contrast ratios of PNEC inthe arterial and portal phases were higher than those of PDAC (p < 0.05 or 0.01). Entropy was lower and uniformity was higher in PNEC compare to PDAC at the arterial phase (p < 0.05). Contrast ratio showed the highest area under curve (AUC) for differentiating PNEC from PDAC (AUC = 0.98-0.99). Entropy and uniformity also showed an acceptable AUC (0.71-0.72).

CONCLUSIONS

Our data indicate that CT imaging features, including tumor margin, enhanced degree and parenchymal atrophy, as well as texture parameters can aid in the differentiation of PNEC from PDAC.

Differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma using magnetic resonance imaging: The value of contrast-enhanced and diffusion weighted imaging

Pancreatic neuroendocrine carcinoma (PNEC) is often misdiagnosed as pancreatic ductal adenocarcinoma (PDAC). This retrospective study differentiated PNEC from PDAC using magnetic resonance imaging (MRI), including contrast-enhanced (CE) and diffusion-weighted imaging (DWI). Clinical data and MRI findings, including the T1/T2 signal, tumor boundary, size, enhancement degree, and apparent diffusion coefficient (ADC), were compared between 37 PDACs and 13 PNECs. Boundaries were more poorly defined in PDAC than PNEC (97.3% vs. 61.5%, p<0.01). Hyper-/isointensity was more common in PNEC than PDAC at the arterial (38.5% vs. 0.0), portal (46.2% vs. 2.7%) and delayed phases (46.2% vs. 5.4%) (all p<0.01). Lymph node metastasis (97.3% vs. 61.5%, p<0.01) and local invasion/distant metastasis (86.5% vs. 46.2%, p<0.01) were more common in PDAC than PNEC. Enhancement degree via CE-MRI was higher in PNEC than PDAC at the arterial and portal phases (p<0.01). PNEC ADC values were lower than those of normal pancreatic parenchyma (p<0.01) and PDAC (p<0.01). Arterial and portal phase signal intensity ratios and ADC values showed the largest areas under the receiver operating characteristic curve and good sensitivities (92.1%–97.2%) and specificities (76.9%–92.3%) for differentiating PNEC from PDAC. Thus the enhancement degree at the arterial and portal phases and the ADC values may be useful for differentiating PNEC from PDAC using MRI.

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

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

Grading of pancreatic neuroendocrine neoplasms using pharmacokinetic parameters derived from dynamic contrast-enhanced MRI

The present study aimed to evaluate the diagnostic efficacy of pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in prospective evaluation of pancreatic neuroendocrine neoplasms (pNENs) grading. A total of 25 histologically proven patients with pNENs (30 lesions in total) who underwent DCE-MRI were enrolled. Lesions were divided into G1, G2 neuroendocrine tumor (NET) and G3 NET/neuroendocrine carcinoma (NEC) groups based on their histological findings according to 2017 World Health Organization Neuroendocrine Tumor Classification Guideline. In addition, the same numbers of tumor-free regions were selected using as normal control group. For each group, pharmacokinetic DCE parameters: volume transfer constant (K^trans); contrast transfer rate constant (k_ep); extravascular extracellular space volume fraction (v_e); and plasma volume fraction (v_p) were calculated with Extended Tofts Linear model. Receiver operator characteristics analysis was conducted to assess the diagnostic efficacy of these parameters in pNENs grading. There were significant differences of K^trans, k_ep, v_e and v_p between tumor-free areas and G1, G2 NET (P<0.001). The K^trans and k_ep of G1 NET were significantly lower compared with those of G2 ones (P<0.005). The area under the curve of K^trans and k_ep in differentiating G2 from G1 NET were 0.767 and 0.846, respectively. When K^trans was >0.667 and k_ep >1.644, the sensitivity of diagnosing G2 NET was the lowest (53.85%), but the specificity was the highest (93.75%). When K^trans was >0.667 or k_ep >1.644, the sensitivity of diagnosing G2 NET was 92.31%, but the specificity was 75.00%. Pharmacokinetic parameters of DCE-MRI, particularly the quantitative values of K^trans and k_ep, are helpful for differentiating G2 NET from G1 ones.

Hepatic neuroendocrine tumour: Apparent diffusion coefficient as a potential marker of prognosis associated with tumour grade and overall survival

OBJECTIVES

To evaluate the correlation between grade of hepatic neuroendocrine tumours (NETs) according to the 2010 World Health Organization (WHO) classification and the apparent diffusion coefficient (ADC) and to assess whether ADC value can predict overall survival (OS) after diagnosis of hepatic NETs.

METHODS

The study included 63 patients who underwent magnetic resonance (MR) imaging with diffusion-weighted images for the evaluation of hepatic NETs. The correlation between qualitative and quantitative MR imaging findings, including ADC values, and WHO classifications was assessed. The association between ADC value and OS was analyzed.

RESULTS

The ADC values and WHO classification of hepatic NETs were moderately negatively correlated in a statistically significant manner (ρ = -0.57, p < 0.001). The OS rates were significantly different according to the ADC value (low ADC vs. high ADC, p = 0.006) as well as WHO classifications (G1+ G2 vs. G3, p = 0.038). However, multivariate analysis revealed that the only independent predictor for OS was a low ADC value (hazard ratio: 3.37, p = 0.010).

CONCLUSION

There was a significant correlation between the ADC value of hepatic NETs and the WHO tumour grade. Additionally, the ADC value of a hepatic NET might be more accurate than the current WHO tumour grade for predicting OS.

KEY POINTS

• ADC values of hepatic NET and WHO tumour grade were negatively correlated. • Lower ADC values of hepatic NET were significantly correlated with worse OS. • ADC value might be more accurate than WHO grade for predicting OS.

A small pancreatic hamartoma with an obstruction of the main pancreatic duct and avid FDG uptake mimicking a malignant pancreatic tumor: a systematic case review

BACKGROUND

Pancreatic hamartomas are extremely rare and may be misdiagnosed as malignant tumors. We report herein a case of a small, solid-type pancreatic hamartoma.

CASE PRESENTATION

A 72-year-old female was incidentally detected pancreatic lesion by ultrasonography. Computed tomography and magnetic resonance imaging revealed a 2.0-cm solid lesion. The main pancreatic duct (MPD) was obstructed by the lesion in the head of the pancreas, and the upstream MPD was dilated. ¹⁸F-fluorodeoxyglucose (FDG) accumulated avidly in the lesion and increased in FDG intensity from the early to the delayed images. The histopathological studies confirmed the diagnosis of pancreatic hamartoma. Immunohistochemically, the cell membrane of the accessory glands and ducts showed homogeneous expression of glucose transporter type I and hexokinase II.

CONCLUSION

Pancreatic hamartomas causing dilatation of the MPD are extremely rare, and this appears to be the first case of a hamartoma to take up FDG avidly. It was a rare occurrence and should be noted that pancreatic hamartomas can cause an obstruction of the MPD and show avid FDG uptake, thereby mimicking malignant pancreatic tumors.

Imaging and Screening of Pancreatic Cancer

Given the low disease prevalence of both exocrine and endocrine cancers in the general population, screening is not recommended. However, in as many as 25% of cases there is a precursor lesion or an identifiable genetic predisposition. For these patients at increased risk, screening with imaging is recommended. Multidetector computed tomography, MR imaging or magnetic resonance cholangiopancreatography, and endoscopic ultrasound examination can be used as screening modalities. Recent advances in dual energy CT and total body MR imaging have increased the suitability of these noninvasive modalities as first-line imaging screening options.

Pancreatic neuroendocrine neoplasms: Correlation between MR features and pathological tumor grades

This study investigated the accuracy of MRI features in differentiating the pathological grades of pancreatic neuroendocrine neoplasms (PNENs). A total of 31 PNENs patients were retrospectively evaluated, including 19 cases in grade 1, 5 in grade 2, and 7 in grade 3. Plain and contrastenhanced MRI was performed on all patients. MRI features including tumor size, margin, signal intensity, enhancement patterns, degenerative changes, duct dilatation and metastasis were analyzed. Chi square tests, Fisher's exact tests, one-way ANOVA and ROC analysis were conducted to assess the associations between MRI features and different tumor grades. It was found that patients with older age, tumors with higher TNM stage and without hormonal syndrome had higher grade of PNETs (all P<0.05). Tumor size, shape, margin and growth pattern, tumor pattern, pancreatic and bile duct dilatation and presence of lymphatic and distant metastasis as well as MR enhancement pattern and tumor-topancreas contrast during arterial phase were the key features differentiating tumors of all grades (all P<0.05). ROC analysis revealed that the tumor size with threshold of 2.8 cm, irregular shape, pancreatic duct dilatation and lymphadenopathy showed satisfactory sensitivity and specificity in distinguishing grade 3 from grade 1 and grade 2 tumors. Features of peripancreatic tissue or vascular invasion, and distant metastasis showed high specificity but relatively low sensitivity. In conclusion, larger size, poorlydefined margin, heterogeneous enhanced pattern during arterial phase, duct dilatation and the presence of metastases are common features of higher grade PNENs. Plain and contrast-enhanced MRI provides the ability to differentiate tumors with different pathological grades.

Value of diffusion-weighted magnetic resonance imaging in predicting World Health Organization grade in G1/G2 pancreatic neuroendocrine tumors

The present study aimed to investigate the value of diffusion-weighted magnetic resonance imaging (DWI) in the grading of well-differentiated pancreatic neuroendocrine tumors (PanNETs). A total of 44 patients with histologically proven well-differentiated PanNET [grade 1 (G1) and grade 2 (G2) according to the World Health Organization (WHO) criteria] underwent pretreatment magnetic resonance imaging (MRI), which was retrospectively analyzed. The location, size, cystic or solid appearance, boundary, presence or absence of tumor contrast enhancement, and MRI signal of the tumor were assessed. Apparent diffusion coefficients (ADCs) within the primary tumor were measured on ADC maps. Receiver operating characteristic curves were used to determine ADC cut-off values and the sensitivity and specificity of the grade prediction. Spearman's rank correlation was utilized to probe the association between ADC value and PanNET grade or pathological parameters. G1 tumors exhibited a well-circumscribed border more often than G2 tumors. No marked differences were observed between PanNET G1 and PanNET G2 for cystic or solid appearance, enhancement, and T1- and T2-weighted imaging signals. Marked hyperintensities were more common in PanNET G2 tumors than in PanNET G1 tumors (P<0.01). The mean ADC values were statistically different between the normal pancreas and G1 and G2 tumors (P<0.01), and between PanNET G1 and PanNET G2 (P<0.05). Correlation analysis showed that ADC value was negatively correlated with PanNET grade, mitotic count and Ki-67 proliferation index (P<0.05). The cut-off ADC value was 0.930×10^-3 mm²/sec, which identified G2 tumors with 82.4% sensitivity and 79.5% selectivity. ADC value therefore represents a non-invasive and valuable imaging parameter in predicting the WHO grade of tumors in well-differentiated PanNET.

Pancreatic neuroendocrine neoplasms at magnetic resonance imaging: comparison between grade 3 and grade 1/2 tumors

Background

The grading of pancreatic neuroendocrine neoplasms (PanNENs) is associated with the choice of treatment strategy. The aim of this study is to identify the magnetic resonance imaging (MRI) features in differentiating pancreatic neuroendocrine tumors (PanNETs) grade 1/2 (G1/G2) and pancreatic neuroendocrine carcinoma grade 3 (PanNEC G3).

Patients and methods

A total of 59 patients with histologically proven PanNENs and who underwent pretreatment MRI were retrospectively analyzed. Tumor location, size, boundary, cystic or solid appearance, enhancement degree, pancreatic duct dilatation, metastases and MRI signal were evaluated. Apparent diffusion coefficients (ADCs) were measured on ADC maps. Receiver operating characteristic curve was used to determine the cut off values and the sensitivity and specificity of prediction. Spearman correlation and logistic regression analysis were adopted to identify the association between MRI features and pathological parameters.

Results

A total of 47 lesions were PanNETs G1/G2 and 12 lesions were PanNEC G3. G1/G2 tumors were more common with well-circumscribed border compared with PanNEC G3. Ill- defined boundary, big size, necrosis, low-moderate enhancement, pancreatic duct dilatation, metastases and high diffusion-weighted imaging (DWI) intensity were more common in PanNEC G3 than in PanNETs G1/G2. The ADC values of PanNEC G3 were also significantly lower compared with the PanNETs G1/G2 and normal pancreatic parenchyma. The cut off value of ADC was 0.95×10⁻³ mm²/s for differentiating PanNEC G3 from PanNETs G1/G2 with 72.3% sensitivity and 91.6% specificity, respectively. Ki-67 index and mitosis count positively correlated with tumor size, pancreatic duct dilatation and metastases (P<0.05) and negatively correlated with ADC values (P<0.01), respectively. Regression analysis further showed that metastases and ADC value were associated with PanNENs grade.

Conclusion

Metastases and ADC value may have potential for differentiating PanNEC G3 from PanNETs G1/G2.

Is the combination of MR and CT findings useful in determining the tumor grade of pancreatic neuroendocrine tumors?

PURPOSE

To retrospectively elucidate the findings useful in determining the tumor grade of pancreatic neuroendocrine tumors (PNETs) by combined assessment of magnetic resonance (MR) and dynamic computed tomography (CT) images.

MATERIALS AND METHODS

Eighty-nine patients with PNETs (96 lesions) were included, and classified as G1, 59; G2, 29; and G3, 8 lesions. Image analysis included lesion diameter, shape, enhancement pattern on arterial phase (AP) and delayed phase CT images, calcification, cystic portion, main pancreatic duct dilatation, signal-intensity on T1-, T2-weighted MR images, and appearance of apparent diffusion coefficient (ADC).

RESULTS

Significant differences among G1, G2, and G3 groups were noted in tumor maximal diameter (p < 0.0001), shape (p < 0.0001), enhancement pattern on AP image (p < 0.0001), cystic portion (p = 0.012), and ADC finding. In multivariate analysis, ADC finding was the independent factor (p = 0.002). The combination findings of low ADC ratio (ADC value of the lesion/ADC value of the parenchyma <0.94), not homogeneous hyper-attenuation, lobulated shape, and hyper-intensity on T2-weighted image were suggestive of G2 or G3 with a probability of 100%. Conversely, all lesions with high ADC ratio and small size (≤25 mm) belonged to the G1 group.

CONCLUSION

Combined assessment of MR and CT findings could improve the prediction of tumor grading in PNETs.

Contrast-enhanced MDCT in patients with pancreatic neuroendocrine tumours: correlation with histological findings and diagnostic performance in differentiation between tumour grades

AIM

To identify the multidetector computed tomography (MDCT) features of pancreatic neuroendocrine tumours (pNETs), which correlate with tumour histology and enable preoperative grading.

MATERIALS AND METHODS

Thirty-nine patients with histologically confirmed pNET who underwent preoperative contrast-enhanced MDCT were included in this study. Nineteen tumours were classified as Grade 1 (G1) and 20 as Grade 2 (G2). Histopathology slides were reviewed to assess the intratumoural microvascular density (MVD) and the amount of tumour stroma. Computed tomography (CT) image analysis included tumour size, margin delineation, calcifications, homogeneity, contrast enhancement (CE) pattern, tumour absolute and relative enhancement, presence of cystic changes, pancreatic duct dilatation, regional and distant metastases. The diagnostic ability to predict tumour grade was measured for each MDCT finding and their combinations.

RESULTS

The mean arterial enhancement ratio had a mean±standard deviation of 1.53±0.45 in G1 and 1.01±0.33 in G2 pNETs (p=0.0003) and correlated with intratumoural microvascular density (MVD; r=0.55, p=0.0002). Tissue stroma percentage did not correlate with imaging findings. Late CE of the tumour (the peak attenuation observed in the venous phase) was significantly associated with G2. Tumour size >20 mm, arterial enhancement ratio <1.1, and late CE showed 74.4%, 79.5%, and 74.4% accuracy, respectively, in diagnosing G2 tumours, while the accuracy of at least two of these criteria used in combination was 82%. Based on these results, a diagnostic algorithm was proposed, which showed high interobserver agreement (k=0.82) in the prediction of tumour grade.

CONCLUSION

Contrast-enhanced MDCT features correlate with histological findings and enable the differentiation between G1 and G2 pNETs during preoperative examination.

Imaging of neuroendocrine tumors of the pancreas

Pancreatic neuroendocrine tumors (PNETs) are rare and represent a heterogeneous disease. PNET can be functioning or non-functioning with different clinical presentations and different prognosis based on WHO and pTNM classifications. The role of imaging includes the localization of small functioning tumor, differentiation of these tumors from adenocarcinoma, identification of signs of malignancy and evaluation of extent. PNETs have a broad spectrum of appearance. On CT and MRI, most of functioning PNETs are well defined small tumors with intense and homogeneous enhancement on arterial and portal phases. However, some PNETs with a more fibrous content may have a more delayed enhancement that is best depicted on the delayed phase. Other PNETs can present as purely cystic, complex cystic and solid tumors and calcified tumors. Non-functioning PNETs are larger with less intense and more heterogeneous enhancement. Functional imaging is useful for disease staging, to detect disease recurrence or the primary but also to select patient candidate for peptide receptor radiometabolic treatment. Somatostatin receptor scintigraphy (SRS) (Octreoscan^®) is still the most available technique. Gallium 68-SST analogue PET have been demonstrated to be more sensitive than SRS-SPEC and it will be the future of functional imaging for NET. Finally, ¹⁸FDG PET/CT is indicated for more aggressive PNET as defined either by negative SRS and huge tumor burden or ki67 above 10% or poorly differentiated PNEC tumors.

Neuroendocrine liver metastases: Value of apparent diffusion coefficient and enhancement ratios for characterization of histopathologic grade

PURPOSE

To assess the value of apparent diffusion coefficient (ADC) measured with diffusion-weighted imaging (DWI) and enhancement ratios (ER) measured with contrast-enhanced T1-weighted imaging (CE-T1WI) for the characterization of histopathologic tumor grade of neuroendocrine tumor liver metastases (NETLM).

MATERIALS AND METHODS

Twenty-two patients with pathology-proven NETLM and pretreatment 1.5 Tesla (T) and 3T MRI including DWI were included in this Institutional Review Board-approved retrospective study. ADC histogram parameters, including mean, minimum (min), skewness, and kurtosis as well as ER, were computed for all lesions. Tumor grading was based on the World Health Organization 2010 classification. Kruskal-Wallis and Mann-Whitney test were used to assess for differences in ADC and ER between different tumor grades. MRI parameters were correlated with pathologic findings using Spearman correlation test. Receiver operating characteristic analysis was performed to determine optimum thresholds for predicting tumor grade.

RESULTS

Forty-eight NETLM (mean size 3.5 cm) were analyzed with the following grade distribution: G1 (n = 25), G2 (n = 16), and G3 (n = 7). ADC-mean (×10^-3 mm² /s) of G3 tumors (0.87 ± 0.43) was significantly lower than that of G1 (1.47 ± 0.63) and G2 (1.27 ± 0.63; P = 0.042). A weak significant negative correlation was observed between ADC and tumor grade (ADC-mean: r = -0.33, P = 0.02; ADC-min: r = -0.37, P = 0.01) and Ki-67 (ADC-mean: r = -0.31, P = 0.03; ADC-min: r = -0.39, P = 0.007). AUROC, sensitivity and specificity of ADC-mean/ADC-min/ER (measured at the early arterial phase) for differentiation of G3 versus G1-G2 were 0.80/0.76/0.67, 100%/50%/70%, and 68.4%/84.2%/66.6%, respectively.

CONCLUSION

ADC is a promising marker for characterization of histopathologic grade of NETLM. These results should be confirmed in a prospective study. J. Magn. Reson. Imaging 2016;44:1432-1441.

Evaluation of the Added Value of Diffusion-Weighted Imaging to Conventional Magnetic Resonance Imaging in Pancreatic Neuroendocrine Tumors and Comparison With 68Ga-DOTANOC Positron Emission Tomography/Computed Tomography

OBJECTIVES

The aims of this study were to investigate the added value of diffusion-weighted imaging (DWI) in pancreatic neuroendocrine tumor (pNET) evaluation and to compare magnetic resonance imaging (MRI) to Ga-DOTANOC positron emission tomography/computed tomography (PET/CT) results.

METHODS

Morphological MRI (T2-weighted [T2-w] + contrast-enhanced [CE] T1-w) and DWI (T2-w + DWI) and Ga-DOTANOC PET/CT in 25 patients/30 pNETs were retrospectively evaluated. Per-patient and per-lesion detection rates (pDR and lDR, respectively) were calculated. Apparent diffusion coefficient values were compared among pNET and surrounding and normal pancreas (control group, 18 patients). Apparent diffusion coefficient and standardized uptake value (SUV) values were compared among different grading and staging groups.

RESULTS

No statistically significant differences in PET/CT and MRI session detection rates were found (morphological MRI and DW-MRI, 88% pDR and 87% lDR; combined evaluation, 92% pDR and 90% lDR; Ga-DOTANOC PET/CT, 88% pDR and 80% lDR). Consensus reading (morphological/DW-MRI + PET/CT) improved pDR and lDR (100%). Apparent diffusion coefficient mean value was significantly lower compared with surrounding and normal parenchyma (P < 0.01). The apparent diffusion coefficient and SUV values of pNETs among different grading and staging groups were not statistically different.

CONCLUSIONS

Conventional MRI, DW-MRI + T2-w sequences, and Ga-DOTANOC PET/CT can be alternative tools in pNET detection. Diffusion-weighted MRI could be valuable in patients with clinical suspicion but negative conventional imaging findings. However, the consensus reading of the 3 techniques seems the best approach.

Overestimated Oncologic Significance of Lymph Node Metastasis in G1 Nonfunctioning Neuroendocrine Tumor in the Left Side of the Pancreas

Recent studies have expounded on the oncologic significance of lymph node metastasis in nonfunctioning (NF) neuroendocrine tumors (NETs) of the pancreas and suggest regional lymph node dissection for treating pancreatic NET. We tested this recommendation in NF pancreatic NET-G1, as these tumors are generally small and suitable for function-preserving minimally invasive pancreatectomy.From January 2005 to December 2014, medical records of patients who underwent pancreatectomy for pathologically confirmed NF NET-G1 of the left side of the pancreas were retrospectively reviewed. Oncologic outcomes were compared between limited pancreatectomy and distal pancreatosplenectomy.Thirty-five patients (14 males and 21 females) with a mean age of 55.9 ± 11.4 years were enrolled in this study. Six patients (17.1%) underwent distal pancreatosplenectomy. Limited pancreatectomies comprised 15 spleen-preserving distal pancreatectomies (42.8%), 10 enucleations (28.6%), and 4 central pancreatectomies (11.4%). Lymph node metastasis was not found in 6 patients who underwent distal pancreatectomy with a splenectomy; meanwhile, the others were regarded as pNx since no lymph node retrieval was attempted during the limited pancreatectomy. Overall disease-free survival was 36.5 months (95% confidence interval [CI]: 25.9-47.1) and no tumor-related mortality was noted. Minimally invasive pancreatectomy (P = 0.557) and limited pancreatectomy (P = 0.758) showed no adverse impact in treating NF NET-G1 of the left side of the pancreas.The oncologic significance of lymph node metastasis is overestimated in NF NET-G1 of the left side of the pancreas. Routine conventional distal pancreatosplenectomy to retrieve regional lymph nodes may be too excessive in treating NF NET-G1 of the distal pancreas.