Differentiation of intrapancreatic accessory spleen from small hypervascular neuroendocrine tumor of the pancreas: textural analysis on contrast-enhanced computed tomography. (May 2019)

• Record Type: Journal Article
• Authors: Lin, Xubo; Xu, Lei; Wu, Aiqin; Guo, Chuangen; Chen, Xiao; Wang, Zhongqiu
• Is Part Of: Acta radiologica. Volume 60:Number 5(2019)
• Page Start: 553
• Page End: 560
• Publication Date: 2019-05
• Subjects: Intrapancreatic accessory spleens -- pancreatic neuroendocrine tumors -- texture analysis -- computed tomography; Radiology, Medical -- Periodicals; Radiography, Medical -- Periodicals; Radiotherapy -- Periodicals; 616.0757
• DOI: 10.1177/0284185118788895
• Languages: English
• ISSNs: 0284-1851

Abstract

Background: Intrapancreatic accessory spleens (IPASs) are usually misdiagnosed as pancreatic neuroendocrine tumors (PNETs). Texture analysis is valuable in tumor detection, diagnosis, and staging. Purpose: To identify the potential of texture features in differentiating IPASs from small hypervascular PNETs. Material and Methods: Twenty-one patients with PNETs and 13 individuals with IPASs who underwent pretreatment dynamic contrast-enhanced computed tomography (CT) were retrospectively analyzed. The routine imaging features—such as location, size, margin, cystic or solid appearance, enhancement degree and pattern, and lymph node enlargement—were recorded. Texture features, such as entropy, skewness, kurtosis, and uniformity, on contrast-enhanced images were analyzed. Receiver operating characteristic (ROC) analysis was performed to differentiate IPASs from PNETs. Results: No significant differences were observed in margin, enhancement degree (arterial and portal phase), lymph node enlargement, or size between PNETs and IPASs (all P > 0.05). However, IPASs usually showed heterogeneous enhancement at the arterial phase and the same degree of enhancement as the spleen at the portal phase, both of which were greater than those of PNETs (69% vs. 35%, P = 0.06; 100% vs. 29%, P = 0.04). Entropy and uniformity were significantly different between IPASs and PNETs at moderate (1.5) and high sigma values (2.5) (both P < 0.01). ROC analysis showed that uniformity at moderate and high sigma had the highest area under the curve (0.82 and 0.89) with better sensitivity (85.0–95.0%) and acceptable specificity (75.0–83.3%) for differentiating IPASs from PNETs. Conclusions: Texture parameters have potential in differentiating IPASs from PNETs.