Department of Radiology, G.B. Rossi Hospital - University of Verona, Verona, Italy.
Department of Radiology, Ospedale Civile Maggiore, Verona, Italy.
Sci Rep. 2019 Feb 18;9(1):2176. doi: 10.1038/s41598-018-38459-6.
To evaluate pancreatic neuroendocrine neoplasms (panNENs) grade prediction by means of qualitative and quantitative CT evaluation, and 3D CT-texture analysis. Patients with histopathologically-proven panNEN, availability of Ki67% values and pre-treatment CT were included. CT images were retrospectively reviewed, and qualitative and quantitative images analysis were done; for quantitative analysis four enhancement-ratios and three permeability-ratios were created. 3D CT-texture imaging analysis was done (Mean Value; Variance; Skewness; Kurtosis; Entropy). Subsequently, these features were compared among the three grading (G) groups. 304 patients affected by panNENs were considered, and 100 patients were included. At qualitative evaluation, frequency of irregular margins was significantly different between tumor G groups. At quantitative evaluation, for all ratios, comparisons resulted statistical significant different between G1 and G3 groups and between G2 and G3 groups. At 3D CT-texture analysis, Kurtosis resulted statistical significant different among three G groups and Entropy resulted statistical significant different between G1 and G3 and between G2 and G3 groups. Quantitative CT evaluation of panNENs can predict tumor grade, discerning G1 from G3 and G2 from G3 tumors. CT-texture analysis can predict panNENs tumor grade, distinguishing G1 from G3 and G2 from G3, and G1 from G2 tumors.
为了评估定性和定量 CT 评估以及 3D CT 纹理分析对胰腺神经内分泌肿瘤（panNENs）分级的预测能力，我们纳入了经组织病理学证实为 panNENs、Ki67% 值可获得以及治疗前 CT 可用的患者。回顾性分析 CT 图像，进行定性和定量图像分析；为定量分析，创建了四个增强比和三个渗透性比。进行了 3D CT 纹理成像分析（平均值；方差；偏度；峰度；熵）。随后，将这些特征在三个分级（G）组之间进行比较。共考虑了 304 例 panNENs 患者，其中 100 例纳入研究。在定性评估中，肿瘤 G 组之间不规则边缘的频率存在显著差异。在定量评估中，所有比值在 G1 和 G3 组之间以及 G2 和 G3 组之间的比较均具有统计学意义。在 3D CT 纹理分析中，峰度在三个 G 组之间存在统计学差异，而熵在 G1 和 G3 组以及 G2 和 G3 组之间存在统计学差异。panNENs 的定量 CT 评估可以预测肿瘤分级，区分 G1 与 G3 和 G2 与 G3 肿瘤。CT 纹理分析可以预测 panNENs 肿瘤分级，区分 G1 与 G3 和 G2 与 G3，以及 G1 与 G2 肿瘤。
