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

3D quantitative analysis of diffusion-weighted imaging for predicting the malignant potential of intraductal papillary mucinous neoplasms of the pancreas

Purpose: To investigate the predictors of intraductal papillary mucinous neoplasms of the pancreas (IPMNs) with high-grade dysplasia, using 2-dimensional (2D) analysis and 3-dimensional (3D) volume-of-interest-based apparent diffusion coefficient (ADC) histogram analysis. Material and methods: The data of 45 patients with histopathologically confirmed IPMNs with high-grade or lowgrade dysplasia were retrospectively assessed. The 2D analysis included lesion-to-spinal cord signal intensity ratio (LSR), minimum ADC value ($ADC_{min}$), and mean ADC value ($ADC_{mean}$). The 3D analysis included the overall mean ($ADC_{overall mean}$), mean of the bottom 10th percentile ($ADC_{mean0-10}$), mean of the bottom 10-25th percentile ($ADC_{mean10-25}$), mean of the bottom 25-50th percentile ($ADC_{mean25-50}$), skewness ($ADC_{skewness}$), kurtosis ($ADC_{kurtosis}$), and entropy ($ADC_{entropy}$). Diagnostic performance was compared by analysing the area under the receiver operating characteristic curve (AUC). Inter-rater reliability was assessed by blinded evaluation using the intraclass correlation coefficient. Results: There were 16 and 29 IPMNs with high- and low-grade dysplasia, respectively. The LSR, $ADC_{overall mean}$, $ADC_{mean0-10}$, $ADC_{mean10-25}$, $ADC_{mean25-50}$, and $ADC_{entropy}$ showed significant between-group differences (AUC = 72-93%; p < 0.05). Inter-rater reliability assessment showed almost perfect agreement for LSR and substantial agreement for $ADC_{overall mean}$ and $ADC_{entropy}$. Multivariate logistic regression showed that $ADC_{overall mean}$ and $ADC_{entropy}$ were significant independent predictors of malignancy (p < 0.05), with diagnostic accuracies of 80% and 73%, respectively. Conclusion: $ADC_{overall mean}$ and $ADC_{entropy}$ from 3D analysis may assist in predicting IPMNs with high-grade dysplasia