Differentiation between solid pseudopapillary neoplasm of the pancreas and hypovascular pancreatic neuroendocrine tumors by using computed tomography
Differentiation between solid pseudopapillary neoplasm of the pancreas and hypovascular pancreatic neuroendocrine tumors by using computed tomography
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CT 鉴别胰腺实性假乳头状肿瘤和缺血管胰腺神经内分泌肿瘤
DOI：
10.1177/0284185118823343
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发表时间：
2019-10-01
期刊：
影响因子：
1.3
通讯作者：
Wang, Zhongqiu
中科院分区：
文献类型：
--
作者：
Wang, Cheng;Cui, Wenjing;Wang, Zhongqiu
背景 胰腺实性假乳头状肿瘤（SPN）在对比增强计算机断层扫描（CE-CT）中通常显示轻微增强，这与低血管性胰腺神经内分泌肿瘤（hypo-PNET）相似。目的 探讨CT影像特征在鉴别hypo-PNETs和SPNs中的价值。材料和方法 44 例经组织学证实的 SPN 患者和 24 例低 PNET 患者均接受了术前动态 CE-CT 治疗。两名放射科医生回顾了 CT 成像结果和临床特征。进行多变量逻辑回归分析来识别区分 SPN 和 hyper-PNET 的相关特征。使用受试者工作特征（ROC）曲线来评估诊断性能。结果与低PNETs相比，SPNs多见于年轻女性（平均年龄 = 34.5岁vs. 49.08岁，P < 0.01）。与hypo-PNET相比，SPN通常呈椭圆形、“浮云”征、钙化和较低的转移频率（均P<0.05）。综合特征（较低年龄、“浮云”征和钙化）显示出区分 SPN 和 hyper-PNET 的可接受的诊断性能（曲线下面积 [AUC] = 0.865，灵敏度为 100%，特异性为 63.6%）。结论“浮云”征、较低年龄和钙化在鉴别SPNs和hypo-PNETs方面具有很大的潜力。
Background Solid pseudopapillary neoplasm of the pancreas (SPNs) usually showed slight enhancement in contrast-enhanced computed tomography (CE-CT), which is similar to hypovascular pancreatic neuroendocrine tumors (hypo-PNETs). Purpose To show the values of CT imaging features in the differentiation between hypo-PNETs and SPNs. Material and Methods Forty-four patients with histologically confirmed SPNs and 24 patients with hypo-PNETs who underwent preoperative dynamic CE-CT were included. Two radiologists reviewed CT imaging findings and clinical features. Multivariate logistic regression analysis was performed to identify relevant features to differentiate SPNs and hypo-PNETs. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance. Results SPNs usually occurred in young women compared with hypo-PNETs (mean age = 34.5 years vs. 49.08 years, P < 0.01). SPNs usually showed an oval shape, “floating cloud” sign, calcification, and lower frequencies of metastases compared with hypo-PNETs (P < 0.05 for all). The combined features (lower age, “floating cloud” sign, and calcification) showed acceptable diagnostic performance (area under the curve [AUC] = 0.865 with 100% sensitivity and 63.6% specificity) for differentiating SPNs from hypo-PNETs. Conclusion “Floating cloud” sign, lower age, and calcification have great potential in differentiating SPNs from hypo-PNETs.