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2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution

机译:2D和3D纹理分析在MR图像上区分脑转移:谨慎行事

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Objective 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). Materials and methods 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. Results 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. Conclusion Our results indicate that the used textural analysis methods may help with discriminating between brain metastases of different primary tumors.
机译:目的鉴于肺癌和乳腺癌脑转移的结构差异,通过2D和3D纹理分析计算它们的异质性参数(TA)。通过MR成像检查了来自乳腺(26)和肺癌(32)的58例脑转移的患者的材料和方法。在对比度增强的T1加权(CET1)图像的切片上由2D ROIS手动描绘脑病变,并且从每个区域创建局部二进制模式(LBP)地图。基于直方图(最小,最大,平均值,标准偏差和方差),以及基于矩阵的(对比度,相关,能量,熵和同质性)2D,2D片的加权平均值,以及真正的3D TA是在CET1图像和LBP地图上获得。 LUNG和乳腺转移之间的LAP和2D TA对比度,相关性,能量和均匀性的结果鉴定为统计学上不同的异质性参数(SDHPS)。加权3D TA识别熵作为额外的SDHP。只有两个纹理指数(TI)与真正的3D TA有显着不同:熵和能量。通过ROC分析,所有这些都明显歧视两种肿瘤类型。对于CET1图像,通过3D TA没有SDHP。结论我们的结果表明,二手纹理分析方法可能有助于区分不同原发性肿瘤的脑转移。

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