Li Jiali, Liang Lili, Yu Hao, Shen Yaqi, Hu Yao, Hu Daoyu, Tang Hao, Li Zhen
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Radiology, The first affiliated hospital of Nanyang Medical College, China.
Magn Reson Imaging. 2019 Jan;55:52-59. doi: 10.1016/j.mri.2018.09.017. Epub 2018 Sep 18.
To evaluate the utility of volumetric histogram analysis of monoexponential and non-Gaussian distribution DWI models for discriminating pancreatic ductal adenocarcinoma (PDAC) and neuroendocrine tumor (pNET).
A total of 340 patients were retrospectively reviewed. Finally, 62 patients with histopathological confirmed PDAC (n = 42) and pNET (n = 20) were enrolled in the study. All the patients accepted magnetic resonance imaging (MRI) at 3 T (including multi-b value DWI, 0-1000 s/mm). Isotropic apparent diffusion coefficient (ADC), true molecular diffusion (Dt), perfusion-related diffusion (Dp), perfusion fraction (f), distributed diffusion coefficient (DDC) and alpha (α) were obtained from different DWI models. Then, mean value, median value, 10th and 90th percentiles were obtained from histogram analysis of each DWI parameter.
Histogram metrics derived from ADC, Dp, f and DDC were significantly lower in PDAC than pNET group (P < 0.05). In contrast, histogram metrics derived from α were observed significantly higher in the PDAC than pNET group (P < 0.05). No significant difference was found in Dt (P ≥ 0.05) between PDAC and pNET patients. Among all parameters, f-median had the highest diagnostic performance (AUC 0.91, cutoff value 0.188, sensitivity 97.62%, specificity 80%).
f-Median derived from IVIM DWI model may be potentially more valuable parameter than ADC, Dp, DDC and α for discriminating PDAC and pNET. Histogram analysis based on the entire tumor was an emerging and valuable tool.
评估单指数和非高斯分布扩散加权成像（DWI）模型的体积直方图分析在鉴别胰腺导管腺癌（PDAC）和神经内分泌肿瘤（pNET）中的应用价值。
回顾性分析340例患者。最终，62例经组织病理学确诊为PDAC（n = 42）和pNET（n = 20）的患者纳入研究。所有患者均接受3T磁共振成像（MRI）检查（包括多b值DWI，0 - 1000 s/mm²）。从不同的DWI模型中获取各向同性表观扩散系数（ADC）、真实分子扩散系数（Dt）、灌注相关扩散系数（Dp）、灌注分数（f）、分布扩散系数（DDC）和α值。然后，通过对每个DWI参数进行直方图分析，获得其平均值、中位数、第10和第90百分位数。
PDAC组中，由ADC、Dp、f和DDC得出的直方图指标显著低于pNET组（P < 0.05）。相反，PDAC组中由α得出的直方图指标显著高于pNET组（P < 0.05）。PDAC和pNET患者之间的Dt无显著差异（P ≥ 0.05）。在所有参数中，f中位数具有最高的诊断性能（曲线下面积[AUC]为0.91，截断值为0.188，灵敏度为97.62%，特异性为80%）。
对于鉴别PDAC和pNET，基于体素内不相干运动（IVIM）DWI模型得出的f中位数可能是比ADC、Dp、DDC和α更有潜在价值的参数。基于整个肿瘤的直方图分析是一种新兴且有价值的工具。
