Technical Note: Identification of CT Texture Features Robust to Tumor Size Variations for Normal Lung Texture Analysis.
Technical Note: Identification of CT Texture Features Robust to Tumor Size Variations for Normal Lung Texture Analysis.
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DOI：
10.4236/ijmpcero.2018.73027
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发表时间：
2018-08-01
期刊：
影响因子：
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通讯作者：
Lu, Wei
中科院分区：
文献类型：
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作者：
Choi, Wookjin;Riyahi, Sadegh;Lu, Wei
正常肺部CT纹理特征已被用于预测放射性肺病（RILD）。为了使这些特征在临床上有用，它们应该对肿瘤大小变化是鲁棒的，并且不与感兴趣的正常肺体积相关，即，肿瘤周围区域（PTR）的体积。本文分析了14例肺癌的CT表现。模拟不同大小的大体肿瘤体积（GTV）并将其放置在肿瘤对侧的肺中。从PTR中提取了27个纹理特征[9个来自强度直方图，8个来自灰度共生矩阵（GLCM），10个来自灰度行程矩阵（GLRM）]。当GTV大小变化时，应用Bland-Altman分析来测量每个特征的标准化一致性范围（nRoA）。当特征的nRoA小于阈值（100%）时，该特征被认为是稳健的。16个纹理特征被确定为鲁棒的。没有一个稳健的特征与PTR的体积相关。无特征显示统计学显著差异（P
Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD). For these features to be clinically useful, they should be robust to tumor size variations and not correlated with the normal lung volume of interest, i.e., the volume of the peri-tumoral region (PTR). CT images of 14 lung cancer patients were studied. Different sizes of gross tumor volumes (GTVs) were simulated and placed in the lung contralateral to the tumor. 27 texture features [nine from intensity histogram, eight from the gray-level co-occurrence matrix (GLCM) and ten from the gray-level run-length matrix (GLRM)] were extracted from the PTR. The Bland-Altman analysis was applied to measure the normalized range of agreement (nRoA) for each feature when GTV size varied. A feature was considered as robust when its nRoA was less than the threshold (100%). Sixteen texture features were identified as robust. None of the robust features was correlated with the volume of the PTR. No feature showed statistically significant differences (P