Imaging and Modeling in Neurobiology and Cancerology, Paris 11 University, Orsay, France.
J Nucl Med. 2014 Mar;55(3):414-22. doi: 10.2967/jnumed.113.129858. Epub 2014 Feb 18.
Texture indices are of growing interest for tumor characterization in (18)F-FDG PET. Yet, on the basis of results published in the literature so far, it is unclear which indices should be used, what they represent, and how they relate to conventional indices such as standardized uptake values (SUVs), metabolic volume (MV), and total lesion glycolysis (TLG). We investigated in detail 31 texture indices, 5 first-order statistics (histogram indices) derived from the gray-level histogram of the tumor region, and their relationship with SUV, MV, and TLG in 3 different tumor types.
Three patient groups corresponding to 3 cancer types at baseline were studied independently: patients with metastatic colorectal cancer (72 lesions), non-small cell lung cancer (24 lesions), and breast cancer (54 lesions). Thirty-one texture indices were studied in addition to SUVs, MV, and TLG, and 5 indices extracted from histogram analysis were also investigated. The relationships between indices were studied as well as the robustness of the various texture indices with respect to the parameters involved in the calculation method (sampling schemes and tumor volume of interest).
Regardless of the patient group, many indices were highly correlated (Pearson correlation coefficient |r| ≥ 0.80), making it desirable to focus on only a few uncorrelated indices. Three histogram indices were highly correlated with SUVs (|r| ≥ 0.84). Four texture indices were highly correlated with MV, and none was highly correlated with SUVs (|r| ≤ 0.55). The resampling formula used to calculate texture indices had a substantial impact, and resampling using at least 32 discrete values should be used for texture indices calculation. The texture indices change as a function of the segmentation method was higher than that of peak and maximum SUVs but less than mean SUV for 5 texture indices and was larger than that of MV for 14 texture indices and for the 5 histogram indices. All these results were extremely consistent across the 3 tumor types and explained many of the observations reported in the literature so far.
None of the histogram indices and only 17 of 31 texture indices were robust with respect to the tumor-segmentation method. An appropriate resampling formula with at least 32 gray levels should be used to avoid introducing a misleading relationship between texture indices and SUV. Some texture indices are highly correlated or strongly correlate with MV whatever the tumor type. Such correlation should be accounted for when interpreting the usefulness of texture indices for tumor characterization, which might call for systematic multivariate analyses.
纹理指数越来越多地用于（18）F-FDG PET 中的肿瘤特征描述。然而，基于目前文献中发表的结果，尚不清楚应该使用哪些指数，它们代表什么，以及它们与标准化摄取值（SUV）、代谢体积（MV）和总肿瘤糖酵解（TLG）等常规指数有何关系。我们详细研究了 31 种纹理指数、肿瘤区域灰度直方图衍生的 5 种一阶统计量（直方图指数），以及它们与 3 种不同肿瘤类型中的 SUV、MV 和 TLG 的关系。
在基线时，独立研究了 3 个患者组，对应于 3 种癌症类型：转移性结直肠癌患者（72 个病灶）、非小细胞肺癌患者（24 个病灶）和乳腺癌患者（54 个病灶）。除了 SUV、MV 和 TLG 之外，还研究了 31 种纹理指数和 5 种从直方图分析中提取的指数，同时还研究了指数之间的关系以及各种纹理指数对计算方法中涉及的参数（采样方案和感兴趣的肿瘤体积）的稳健性。
无论患者组如何，许多指数高度相关（Pearson 相关系数|r|≥0.80），因此最好仅关注几个不相关的指数。3 个直方图指数与 SUV 高度相关（|r|≥0.84）。4 个纹理指数与 MV 高度相关，而与 SUV 无关（|r|≤0.55）。用于计算纹理指数的重采样公式有很大影响，应使用至少 32 个离散值进行重采样。纹理指数随分割方法的变化大于峰值和最大 SUV，但小于平均 SUV，对于 5 个纹理指数和 14 个纹理指数以及 5 个直方图指数，其变化大于 MV。所有这些结果在 3 种肿瘤类型中都非常一致，并解释了迄今为止文献中报道的许多观察结果。
直方图指数中没有一个，纹理指数中只有 17 个，与肿瘤分割方法具有稳健性。应使用至少 32 个灰度级的适当重采样公式，以避免在纹理指数与 SUV 之间引入误导性关系。无论肿瘤类型如何，一些纹理指数与 MV 高度相关或强烈相关。在解释纹理指数对肿瘤特征描述的有用性时，应考虑到这种相关性，这可能需要系统的多变量分析。
