Toshima Fumihito, Inoue Dai, Komori Takahiro, Yoshida Kotaro, Yoneda Norihide, Minami Tetsuya, Matsui Osamu, Ikeda Hiroko, Gabata Toshifumi
Department of Radiology, Kanazawa University Graduate School of Medical Science, 13-1 Takara-machi, Kanazawa, Ishikawa, 920-8641, Japan.
Department of Pathology, Kanazawa University Graduate School of Medical Science, 13-1 Takara-machi, Kanazawa, Ishikawa, 920-8641, Japan.
Jpn J Radiol. 2017 May;35(5):242-253. doi: 10.1007/s11604-017-0627-x. Epub 2017 Mar 3.
To retrospectively elucidate the findings useful in determining the tumor grade of pancreatic neuroendocrine tumors (PNETs) by combined assessment of magnetic resonance (MR) and dynamic computed tomography (CT) images.
Eighty-nine patients with PNETs (96 lesions) were included, and classified as G1, 59; G2, 29; and G3, 8 lesions. Image analysis included lesion diameter, shape, enhancement pattern on arterial phase (AP) and delayed phase CT images, calcification, cystic portion, main pancreatic duct dilatation, signal-intensity on T1-, T2-weighted MR images, and appearance of apparent diffusion coefficient (ADC).
Significant differences among G1, G2, and G3 groups were noted in tumor maximal diameter (p < 0.0001), shape (p < 0.0001), enhancement pattern on AP image (p < 0.0001), cystic portion (p = 0.012), and ADC finding. In multivariate analysis, ADC finding was the independent factor (p = 0.002). The combination findings of low ADC ratio (ADC value of the lesion/ADC value of the parenchyma <0.94), not homogeneous hyper-attenuation, lobulated shape, and hyper-intensity on T2-weighted image were suggestive of G2 or G3 with a probability of 100%. Conversely, all lesions with high ADC ratio and small size (≤25 mm) belonged to the G1 group.
Combined assessment of MR and CT findings could improve the prediction of tumor grading in PNETs.
通过磁共振(MR)和动态计算机断层扫描(CT)图像的联合评估,回顾性阐明有助于确定胰腺神经内分泌肿瘤(PNETs)肿瘤分级的影像学表现。
纳入89例PNETs患者(96个病灶),分为G1级59例、G2级29例、G3级8例。图像分析包括病灶直径、形态、动脉期(AP)和延迟期CT图像上的强化方式、钙化、囊性成分、主胰管扩张、T1加权和T2加权MR图像上的信号强度以及表观扩散系数(ADC)表现。
G1、G2和G3组在肿瘤最大直径(p<0.0001)、形态(p<0.0001)、AP图像强化方式(p<0.0001)、囊性成分(p=0.012)和ADC表现方面存在显著差异。多因素分析显示,ADC表现是独立因素(p=0.002)。ADC比值低(病灶ADC值/实质ADC值<0.94)、非均匀性高密度、分叶状形态和T2加权图像上高信号强度的联合表现提示为G2或G3级,概率为100%。相反,所有ADC比值高且体积小(≤25mm)的病灶均属于G1组。
MR和CT表现的联合评估可提高PNETs肿瘤分级的预测能力。