Ding Lin, Wu Sisi, Shen Yaqi, Hu Xuemei, Hu Daoyu, Kamel Ihab, Li Zhen
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China.
Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, MD 21105, USA.
Life (Basel). 2021 Mar 23;11(3):264. doi: 10.3390/life11030264.
To explore the potential role of computed tomography (CT) texture analysis and an imaging biomarker in differentiating primary gastro-intestinal lymphoma (PGIL) from gastro-intestinal adenocarcinoma (GIAC).
A total of 131 patients with surgical pathologically PGIL and GIAC were enrolled in this study. Histogram parameters of arterial and venous phases extracted from contrast enhanced modified discrete cosine transform (MDCT) images were compared between PGIL and GIAC by Mann-Whitney U tests. The optimal parameters for differentiating these two groups were obtained through receiver operating characteristic (ROC) curves and the area under the curve (AUC) was calculated.
Compared with GIAC, in arterial phase, PGIL had statistically higher 5th, 10th percentiles ( = 0.003 and 0.011) and statistically lower entropy ( = 0.001). In the venous phase, PGIL had statistically lower mean, median, 75th, 90th, 95th percentiles, and entropy ( = 0.036, 0.029, 0.007, 0.001 and 0.001, respectively). For differentiating PGIL from GIAC, V-median + A-5th percentile was an optimal parameter for combined diagnosis (AUC = 0.746, < 0.0001), and the corresponding sensitivity and specificity were 81.7 and 64.8%, respectively.
CT texture analysis could be useful for differential diagnosis of PGIL and GIAC.
探讨计算机断层扫描（CT）纹理分析及一种影像生物标志物在鉴别原发性胃肠道淋巴瘤（PGIL）与胃肠道腺癌（GIAC）中的潜在作用。
本研究共纳入131例经手术病理确诊为PGIL和GIAC的患者。通过Mann-Whitney U检验比较PGIL和GIAC在对比增强改良离散余弦变换（MDCT）图像中提取的动脉期和静脉期直方图参数。通过受试者操作特征（ROC）曲线获得区分这两组的最佳参数，并计算曲线下面积（AUC）。
与GIAC相比，在动脉期，PGIL的第5、第10百分位数在统计学上更高（分别为P = 0.003和0.011），熵在统计学上更低（P = 0.001）。在静脉期，PGIL的均值、中位数、第75、第90、第95百分位数及熵在统计学上更低（分别为P = 0.036、0.029、0.007、0.001和0.001）。对于鉴别PGIL与GIAC，V-中位数+A-第5百分位数是联合诊断的最佳参数（AUC = 0.746，P < 0.0001），相应的敏感度和特异度分别为81.7%和64.8%。
CT纹理分析有助于PGIL和GIAC的鉴别诊断。
