Correlation between radiologic features on contrast-enhanced CT and pathological tumor grades in pancreatic neuroendocrine neoplasms |
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| Authors: | Xu Wenbin Yan Han Xu Lulu Li Mingna Gao Wentao Jiang Kuirong Wu Junli Miao Yi |
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| Affiliation: | Pancreas Center,the First Affiliated Hospital of Nanjing Medical University,Nanjing,Jiangsu 210029,China;Pancreas Institute of Nanjing Medical University,Nanjing,Jiangsu 210029,China;Department of Radiology,the First Affiliated Hospital of Nanjing Medical University,Nanjing,Jiangsu 210029,China;Department of Pathology,the First Affiliated Hospital of Nanjing Medical University,Nanjing,Jiangsu 210029,China |
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| Abstract: | Contrast-enhanced computed tomography (CT) contributes to the increasing detection of pancreatic neuroendocrine neoplasms (PNENs). Nevertheless, its value for differentiating pathological tumor grades is not well recognized. In this report, we have conducted a retrospective study on the relationship between the 2017 World Health Organization (WHO) classification and CT imaging features in 94 patients. Most of the investigated features eventually provided statistically significant indicators for discerning PNENs G3 from PNENs G1/G2, including tumor size, shape, margin, heterogeneity, intratumoral blood vessels, vascular invasion, enhancement pattern in both contrast phases, enhancement degree in both phases, tumor-to-pancreas contrast ratio in both phases, common bile duct dilatation, lymph node metastases, and liver metastases. Ill-defined tumor margin was an independent predictor for PNENs G3 with the highest area under the curve (AUC) of 0.906 in the multivariable logistic regression and receiver operating characteristic curve analysis. The portal enhancement ratio (PER) was shown the highest AUC of 0.855 in terms of quantitative features. Our data suggest that the traditional contrast-enhanced CT still plays a vital role in differentiation of tumor grades and heterogeneity analysis prior to treatment. |
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| Keywords: | pancreatic neuroendocrine neoplasm computed tomography tumor grade heterogeneity analysis |
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