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

BackgroundImaging 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.MethodsTwenty-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.ResultsMore 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).ConclusionsOur 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.

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.

BackgroundThe 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 methodsA 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.ResultsA 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.ConclusionMetastases and ADC value may have potential for differentiating PanNEC G3 from PanNETs G1/G2.