Béresová Monika, Larroza Andrés, Arana Estanislao, Varga József, Balkay László, Moratal David
Division of Radiology, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98, Debrecen, 4032, Hungary.
Division of Nuclear Medicine, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
MAGMA. 2018 Apr;31(2):285-294. doi: 10.1007/s10334-017-0653-9. Epub 2017 Sep 22.
To find structural differences between brain metastases of lung and breast cancer, computing their heterogeneity parameters by means of both 2D and 3D texture analysis (TA).
Patients with 58 brain metastases from breast (26) and lung cancer (32) were examined by MR imaging. Brain lesions were manually delineated by 2D ROIs on the slices of contrast-enhanced T1-weighted (CET1) images, and local binary patterns (LBP) maps were created from each region. Histogram-based (minimum, maximum, mean, standard deviation, and variance), and co-occurrence matrix-based (contrast, correlation, energy, entropy, and homogeneity) 2D, weighted average of the 2D slices, and true 3D TA were obtained on the CET1 images and LBP maps.
For LBP maps and 2D TA contrast, correlation, energy, and homogeneity were identified as statistically different heterogeneity parameters (SDHPs) between lung and breast metastasis. The weighted 3D TA identified entropy as an additional SDHP. Only two texture indexes (TI) were significantly different with true 3D TA: entropy and energy. All these TIs discriminated between the two tumor types significantly by ROC analysis. For the CET1 images there was no SDHP at all by 3D TA.
Our results indicate that the used textural analysis methods may help with discriminating between brain metastases of different primary tumors.
通过二维和三维纹理分析（TA）计算肺癌和乳腺癌脑转移瘤的异质性参数，以发现它们之间的结构差异。
对58例来自乳腺癌（26例）和肺癌（32例）脑转移瘤患者进行磁共振成像检查。在对比增强T1加权（CET1）图像切片上通过二维感兴趣区域（ROI）手动勾勒脑病变，并从每个区域创建局部二值模式（LBP）图。在CET1图像和LBP图上获得基于直方图的（最小值、最大值、平均值、标准差和方差）以及基于共生矩阵的（对比度、相关性、能量、熵和均匀性）二维、二维切片的加权平均值以及真正的三维TA。
对于LBP图和二维TA，对比度、相关性、能量和均匀性被确定为肺癌和乳腺癌转移之间具有统计学差异的异质性参数（SDHP）。加权三维TA确定熵为另一个SDHP。真正的三维TA仅有两个纹理指数（TI）存在显著差异：熵和能量。通过ROC分析，所有这些TI均能显著区分这两种肿瘤类型。对于CET1图像，三维TA根本没有SDHP。
我们的结果表明，所使用的纹理分析方法可能有助于区分不同原发肿瘤的脑转移瘤。
