Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003, China.
Department of Laboratory Medicine, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003, China.
Abdom Radiol (NY). 2019 Feb;44(2):576-585. doi: 10.1007/s00261-018-1763-1.
Grades of pancreatic neuroendocrine neoplasms (PNENs) are associated with the choice of treatment strategies. Texture analysis has been used in tumor diagnosis and staging evaluation. In this study, we aim to evaluate the potential ability of texture parameters in differentiation of PNENs grades.
37 patients with histologically proven PNENs and underwent pretreatment dynamic contrast-enhanced computed tomography examinations were retrospectively analyzed. Imaging features and texture features at contrast-enhanced images were evaluated. Receiver operating characteristic curves were used to determine the cut-off values and the sensitivity and specificity of prediction.
There were significant differences in tumor margin, pancreatic duct dilatation, lymph nodes invasion, size, portal enhancement ratio (PER), arterial enhancement ratio (AER), mean grey-level intensity, kurtosis, entropy, and uniformity among G1, G2, and pancreatic neuroendocrine carcinoma (PNEC) G3 (p < 0.01). Similar results were found between pancreatic neuroendocrine tumors (PNETs) G1/G2 and PNEC G3. AER and PER showed the best sensitivity (0.86-0.94) and specificity (0.92-1.0) for differentiating PNEC G3 from PNETs G1/G2. Mean grey-level intensity, entropy, and uniformity also showed acceptable sensitivity (0.73-0.91) and specificity (0.85-1.0). Mean grey-level intensity was also showed acceptable sensitivity (91% to 100%) and specificity (82% to 91%) in differentiating PNET G1 from PNET G2.
Our data indicated that texture parameters have potential in grading PNENs, in particular in differentiating PNEC G3 from PNETs G1/G2.
胰腺神经内分泌肿瘤（PNENs）的分级与治疗策略的选择有关。纹理分析已被用于肿瘤诊断和分期评估。本研究旨在评估纹理参数在PNENs 分级鉴别中的潜在能力。
回顾性分析了 37 例经组织学证实的 PNENs 患者的术前动态对比增强 CT 检查资料。评估了增强图像的影像学特征和纹理特征。使用受试者工作特征曲线确定预测的截断值、敏感性和特异性。
肿瘤边缘、胰管扩张、淋巴结侵犯、大小、门静脉增强率（PER）、动脉增强率（AER）、平均灰度强度、峰度、熵和均匀性在 G1、G2 和胰腺神经内分泌癌（PNEC）G3 之间存在显著差异（p<0.01）。PNETs G1/G2 和 PNEC G3 之间也存在类似的结果。AER 和 PER 对鉴别 PNEC G3 与 PNETs G1/G2 的敏感性（0.86-0.94）和特异性（0.92-1.0）最好。平均灰度强度、熵和均匀性也具有可接受的敏感性（0.73-0.91）和特异性（0.85-1.0）。平均灰度强度在鉴别 PNET G1 与 PNET G2 时也具有可接受的敏感性（91%至 100%）和特异性（82%至 91%）。
我们的数据表明，纹理参数在分级 PNENs 中具有潜在的应用价值，特别是在鉴别 PNEC G3 与 PNETs G1/G2 方面。
