Pancreatic neuroendocrine tumour: Correlation of apparent diffusion coefficient or WHO classification with recurrence-free survival

High predictive ability of apparent diffusion coefficient value for wall-invasion pattern of advanced gallbladder carcinoma

PURPOSE

The wall-invasion pattern classification of advanced gallbladder carcinoma (GBC) has been reported. However, its association with clinical findings remains unclear. We aimed to clarify relationships between clinicopathological characteristics, prognosis, and apparent diffusion coefficient (ADC) values of advanced GBC based on the wall-invasion pattern.

METHODS

We reviewed the data of 37 patients who had undergone advanced GBC cholecystectomy at our institution between 2009 and 2021. Clinicopathological findings, prognosis, and ADC values were retrospectively analyzed.

RESULTS

Based on the wall-invasion pattern, patients were classified into infiltrative growth (IG) type (n = 22) and destructive growth (DG) type (n = 15). In the DG-type, the incidence of venous invasion (P = 0.027), neural invasion (P = 0.008), and lymph node metastasis (P = 0.047) was significantly higher than in the IG-type, and recurrent-free survival (RFS) was significantly shorter (P = 0.015); the median RFS was 11.4 months (95% confidence interval, 6.3-16.5 months) in the DG-type and not reached in the IG-type. The ADC value in the DG-type was significantly lower than in the IG-type (median, 1.19 × 10^-3 mm²/s vs. 1.86 × 10^-3 mm²/s, P < 0.001). The area under the receiver operating characteristic curve for the ADC values to differentiate wall-invasion patterns was 0.95 (95% confidence interval, 0.87-1.00). The optimal cutoff ADC value was 1.45 × 10^-3 mm²/s (sensitivity, 92.9%; specificity, 90.9%).

CONCLUSIONS

The wall-invasion pattern of advanced GBC is associated with its aggressiveness and prognosis, and can be predicted by ADC values with high accuracy.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Measuring Perfusion in Pancreatic Ductal Adenocarcinoma and Different Tumor Grade: A Preliminary Single Center Study

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging is a noninvasive imaging modality that can supply information regarding the tumor anatomy and physiology. The aim of the study was to analyze DCE-MRI perfusion parameters in normal pancreatic parenchymal tissue and PDAC and to evaluate the efficacy of this diagnostic modality in determining the tumor grade.

METHODS

A single-center retrospective study was performed. A total of 28 patients with histologically proven PDAC underwent DCE-MRI; the control group enrolled 14 patients with normal pancreatic parenchymal tissue; the radiological findings were compared with histopathological data. The study patients were further grouped according to the differentiation grade (G value): well- and moderately differentiated and poorly differentiated PDAC.

RESULTS

The median values of K^trans, k_ep and iAUC were calculated lower in PDAC compared with the normal pancreatic parenchymal tissue (p < 0.05). The mean value of Ve was higher in PDAC, compared with the normal pancreatic tissue (p < 0.05). K^trans, k_ep and iAUC were lower in poorly differentiated PDAC, whereas Ve showed no differences between groups.

CONCLUSIONS

Ve and iAUC DCE-MRI perfusion parameters are important as independent diagnostic criteria predicting the probability of PDAC; the Ktrans and iAUC DCE-MRI perfusion parameters may serve as effective independent prognosticators preoperatively identifying poorly differentiated PDAC.

New Directions in Imaging Neuroendocrine Neoplasms

Purpose of Review

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

Recent Findings

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

Summary

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

Somatostatin Receptor Imaging and Theranostics: Current Practice and Future Prospects

A new era of precision diagnostics and therapy for patients with neuroendocrine neoplasms began with the approval of somatostatin receptor (SSTR) radiopharmaceuticals for PET imaging followed by peptide receptor radionuclide therapy (PRRT). With the transition from SSTR-based γ-scintigraphy to PET, the higher sensitivity of the latter raised questions regarding the direct application of the planar scintigraphy-based Krenning score for PRRT eligibility. Also, to date, the role of SSTR PET in response assessment and predicting outcome remains under evaluation. In this comprehensive review article, we discuss the current role of SSTR PET in all aspects of neuroendocrine neoplasms, including its relation to conventional imaging, selection of patients for PRRT, and the current understanding of SSTR PET-based response assessment. We also provide a standardized reporting template for SSTR PET with a brief discussion.

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

Background

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

Methods

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

Results

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

Conclusions

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

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.

Anatomic and Functional Imaging of Neuroendocrine Tumors

OPINION STATEMENT

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

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.

MRI-based nomogram estimates the risk of recurrence of primary nonmetastatic pancreatic neuroendocrine tumors after curative resection

BACKGROUND

Accurate estimation of the recurrence of pancreatic neuroendocrine tumors help with prognosis, guide follow-up, and avoid futile treatments.

PURPOSE

To investigate whether MRI features could preoperatively estimate the recurrence of pancreatic neuroendocrine tumors (PNETs) and to refine a novel prognostic model through developing a nomogram incorporating various MRI features.

STUDY TYPE

Retrospective.

POPULATION

In all, 81 patients with clinicopathologically confirmed nonmetastatic PNETs.

FIELD STRENGTH/SEQUENCES

1.5 T MR, including T₁ -weighted, T₂ -weighted, and diffusion-weighted imaging sequences.

ASSESSMENT

Qualitative and quantitative MRI features of PNET were assessed by three experienced radiologists.

STATISTICAL TESTS

Uni- and multivariable analyses for recurrence-free survival (RFS) were evaluated using a Cox proportional hazards model. The MRI-based nomogram was then designed based on multivariable logistic analysis in our study and the performance of the nomogram was validated according to C-index, calibration, and decision curve analyses.

RESULTS

MRI features, including tumor size (hazard ratio [HR]: 14.131; P = 0.034), enhancement pattern (HR: 21.821, P = 0.032), and the apparent diffusion coefficient (ADC) values (HR: 0.055, P = 0.038) were significant independent predictors of RFS at multivariable analysis. The performance of the nomogram incorporating various MRI features (with a C-index of 0.910) was improved compared with that based on tumor size, enhancement pattern, and ADC alone (with C-index values of 0.672, 0.851, and 0.809, respectively). The calibration curve of the nomogram exhibited perfect consistency between estimation and observation at 0.5, 1, and 2 years after surgery. The decision curve showed that a nomogram incorporating three features had more favorable clinical predictive usefulness than any single feature.

DATA CONCLUSION

MRI features can be considered effective recurrence predictors for PNETs after surgery. The preliminary nomogram incorporating various MRI features could assess the risk of recurrence in PNETs and may be used to optimize individual treatment strategies.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:397-409.

Hepatic neuroendocrine tumors: gadoxetic acid-enhanced magnetic resonance imaging findings with an emphasis on differentiation between primary and secondary tumors

PURPOSE

To describe the findings of magnetic resonance (MR) imaging of hepatic neuroendocrine tumors (hNET) and to identify the features that differentiate secondary from primary tumors.

METHODS

This retrospective study was approved by the institutional review board, and the requirement for informed consent was waived. Between August 2008 and December 2014, 50 patients with pathologically proven hNETs who underwent gadoxetic acid-enhanced MR imaging with diffusion-weighted images were included. The patients were divided into two groups according to whether they had primary (n = 17) or secondary (n = 33) hNETs. Qualitative values based on a consensus between two observers [morphologic findings, signal intensity, and enhancement pattern (poor or indeterminate; hepatocellular carcinoma-like or cholangiocarcinoma-like)], and quantitative values (apparent diffusion coefficient) were evaluated as predictors of secondary hNETs using multivariable logistic regression and receiver operating characteristic (ROC) analysis.

RESULTS

In multivariate analysis, the presence of multiple lesions (p = 0.011), a tumor size less than 6.3 cm (p = 0.001), and a hepatocellular carcinoma-like enhancement pattern (p = 0.031) were significant independent factors for differentiating secondary from primary hNETs, and achieved a sensitivity of 91%, a specificity of 82%, and an accuracy of 88%, with a value of the area under the ROC curve of 0.931.

CONCLUSION

Using these specific MR imaging criteria, secondary hNETs could be differentiated from primary hNETs with a high degree of accuracy in patients with histopathologically proven hNETs.

PET/MR Imaging of the Pancreas

PET/MR imaging has the potential to markedly alter pancreatic care in both the malignant, and premalignant states with the ability to perform robust, high-resolution, quantitative molecular imaging. The ability of PET/MR imaging to monitor disease processes, potentially correct for motion in the upper abdomen, and provide novel biomarkers that may be a combination of MR imaging and PET biomarkers, offers a unique, precise interrogation of the pancreatic milieu going forward.

Functional imaging in pancreatic disease

In addition to the classical morphological evaluation of pancreatic disease, the constant technological advances in imaging techniques based fundamentally on computed tomography and magnetic resonance imaging have enabled the quantitative functional and molecular evaluation of this organ. In many cases, this imaging-based information results in substantial changes to patient management and can be a fundamental tool for the development of biomarkers. The aim of this article is to review the role of emerging functional and molecular techniques based on computed tomography and magnetic resonance imaging in the evaluation of pancreatic disease.

Prediction and clinical implications of portal vein/superior mesenteric vein invasion in patients with resected pancreatic head cancer: the significance of preoperative CT parameters

AIM

To determine the preoperative computed tomography (CT) parameters that predict portal vein/superior mesenteric vein (PV-SMV) invasion in patients with pancreatic head cancer, and to assess whether PV-SMV invasion affects patient survival.

MATERIALS AND METHODS

Sixty patients with PV-SMV invasion, and 60 randomly selected patients without it, who had undergone preoperative CT and subsequent surgery for pancreatic head cancer were enrolled. The following CT parameters were evaluated using multivariate logistic regression and receiver operating characteristic analyses to predict vessel invasion (tumour size and margin, length of involved vessel, distance from the tumour to the vessel, vessel irregularity, the teardrop sign, and tumour-vein interface [TVI]). The Cox proportional hazard model was used to evaluate the effects of PV-SMV invasion on survival.

RESULTS

In multivariate analysis, tumour size (odds ratio [OR]=1.99) and TVI (OR=3.79 [≤90°], 20.66 [>90°, ≤180°], and 47.24 [>180°]) were independent CT predictors of PV-SMV invasion (p<0.05); they achieved a sensitivity of 87%, a specificity of 75%, and an accuracy of 81%; however, PV-SMV invasion did not affect patient survival after surgery (p=0.374).

CONCLUSION

In patients with pancreatic head cancer, preoperative CT parameters can predict PV-SMV invasion with high accuracy. PV-SMV invasion did not affect treatment outcome after surgery.

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.

Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors?

OBJECTIVES

To evaluate MRI derived whole-tumour histogram analysis parameters in predicting pancreatic neuroendocrine neoplasm (panNEN) grade and aggressiveness.

METHODS

Pre-operative MR of 42 consecutive patients with panNEN >1 cm were retrospectively analysed. T1-/T2-weighted images and ADC maps were analysed. Histogram-derived parameters were compared to histopathological features using the Mann-Whitney U test. Diagnostic accuracy was assessed by ROC-AUC analysis; sensitivity and specificity were assessed for each histogram parameter.

RESULTS

ADC_entropy was significantly higher in G2-3 tumours with ROC-AUC 0.757; sensitivity and specificity were 83.3 % (95 % CI: 61.2-94.5) and 61.1 % (95 % CI: 36.1-81.7). ADC_kurtosis was higher in panNENs with vascular involvement, nodal and hepatic metastases (p= .008, .021 and .008; ROC-AUC= 0.820, 0.709 and 0.820); sensitivity and specificity were: 85.7/74.3 % (95 % CI: 42-99.2 /56.4-86.9), 36.8/96.5 % (95 % CI: 17.2-61.4 /76-99.8) and 100/62.8 % (95 % CI: 56.1-100/44.9-78.1). No significant differences between groups were found for other histogram-derived parameters (p >.05).

CONCLUSIONS

Whole-tumour histogram analysis of ADC maps may be helpful in predicting tumour grade, vascular involvement, nodal and liver metastases in panNENs. ADC_entropy and ADC_kurtosis are the most accurate parameters for identification of panNENs with malignant behaviour.

KEY POINTS

• Whole-tumour ADC histogram analysis can predict aggressiveness in pancreatic neuroendocrine neoplasms. • ADC entropy and kurtosis are higher in aggressive tumours. • ADC histogram analysis can quantify tumour diffusion heterogeneity. • Non-invasive quantification of tumour heterogeneity can provide adjunctive information for prognostication.

Pancreatic neuroendocrine tumor: Correlations between MRI features, tumor biology, and clinical outcome after surgery

PURPOSE

To assess which magnetic resonance imaging (MRI) features are associated with pNETs (pancreatic neuroendocrine tumors) grade based on the WHO classification, as well as identify MRI features related to disease progression after surgery.

MATERIALS AND METHODS

In this Institutional Review Board (IRB)-approved study, 1.5T and 3.0T MRI scans of 80 patients with surgically verified pNETs were assessed. The images were evaluated for tumor location; size; pattern; predominant signal intensity on precontrast T₁ - and T₂ -weighted images, as well as on postcontrast arterial and portal venous phase T₁ -weighted sequences; presence of pancreatic duct dilatation; pancreatic atrophy; restricted diffusion; vascular involvement by the tumor; extrapancreatic tumor spread; and synchronous liver metastases. Tumors were graded based on the WHO classification and patients were followed-up with computed tomography (CT) or MRI after surgical resection. Data were analyzed with Student's t and chi-square tests, logistic regression, and Kaplan-Meier curves.

RESULTS

The MRI features that were associated with aggressive tumors were: size >2.0 cm (odds ratio [OR] = 4.8, P = 0.002), "T₂ nonbright lesions" on T₂ -weighted images (OR = 4.6, P = 0.008), presence of pancreatic ductal dilatation (OR = 4.9, P = 0.024), and restricted diffusion within the lesion (OR = 4.9, P = 0.013). Differences in progression-free survival distribution were found for patients whose pNETs were associated with the following MRI features: size >2.0 cm (χ² (1) = 6.0, P = 0.014), "nonbright lesions" on T₂ -weighted images (χ² (1) = 6.8, P = 0.009), and presence of pancreatic duct dilatation (χ² (1) = 10.9, P = 0.001).

CONCLUSION

MRI features can be used to assess pNETs aggressiveness and identify patients at risk for early disease progression after surgical resection.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:425-432.