Orlhac Fanny, Nioche Christophe, Soussan Michaël, Buvat Irène
Imagerie Moléculaire In Vivo, IMIV, CEA, INSERM, CNRS, Université Paris-Sud, Université Paris Saclay, CEA-SHFJ, Orsay, France; and
Imagerie Moléculaire In Vivo, IMIV, CEA, INSERM, CNRS, Université Paris-Sud, Université Paris Saclay, CEA-SHFJ, Orsay, France; and.
J Nucl Med. 2017 Mar;58(3):387-392. doi: 10.2967/jnumed.116.181859. Epub 2016 Oct 6.
The use of texture indices to characterize tumor heterogeneity from PET images is being increasingly investigated in retrospective studies, yet the interpretation of PET-derived texture index values has not been thoroughly reported. Furthermore, the calculation of texture indices lacks a standardized methodology, making it difficult to compare published results. To allow for texture index value interpretation, we investigated the changes in value of 6 texture indices computed from simulated and real patient data. Ten sphere models mimicking different activity distribution patterns and the F-FDG PET images from 54 patients with breast cancer were used. For each volume of interest, 6 texture indices were measured. The values of texture indices and how they changed as a function of the activity distribution were assessed and compared with the visual assessment of tumor heterogeneity. Using the sphere models and real tumors, we identified 2 sets of texture indices reflecting different types of uptake heterogeneity. Set 1 included homogeneity, entropy, short-run emphasis, and long-run emphasis, all of which were sensitive to the presence of uptake heterogeneity but did not distinguish between hyper- and hyposignal within an otherwise uniform activity distribution. Set 2 comprised high-gray-level-zone emphasis and low-gray-level-zone emphasis, which were mostly sensitive to the average uptake rather than to the uptake local heterogeneity. Four of 6 texture indices significantly differed between homogeneous and heterogeneous lesions as defined by 2 nuclear medicine physicians ( < 0.05). All texture index values were sensitive to voxel size (variations up to 85.8% for the most homogeneous sphere models) and edge effects (variations up to 29.1%). Unlike a previous report, our study found that variations in texture indices were intuitive in the sphere models and real tumors: the most homogeneous uptake distribution exhibited the highest homogeneity and lowest entropy. Two families of texture index reflecting different types of uptake patterns were identified. Variability in texture index values as a function of voxel size and inclusion of tumor edges was demonstrated, calling for a standardized calculation methodology. This study provides guidance for nuclear medicine physicians in interpreting texture indices in future studies and clinical practice.
在回顾性研究中，越来越多地探讨了使用纹理指数来表征PET图像中的肿瘤异质性，但PET衍生纹理指数值的解释尚未得到充分报道。此外，纹理指数的计算缺乏标准化方法，难以比较已发表的结果。为了实现纹理指数值的解释，我们研究了从模拟和真实患者数据计算出的6种纹理指数的值的变化。使用了10个模拟不同活性分布模式的球体模型以及54例乳腺癌患者的F-FDG PET图像。对于每个感兴趣的体积，测量了6种纹理指数。评估了纹理指数的值以及它们如何随活性分布而变化，并与肿瘤异质性的视觉评估进行比较。使用球体模型和真实肿瘤，我们确定了2组反映不同类型摄取异质性的纹理指数。第1组包括均匀性、熵、短期强调和长期强调，所有这些指数对摄取异质性的存在敏感，但在其他方面均匀的活性分布内，无法区分高信号和低信号。第2组包括高灰度级区域强调和低灰度级区域强调，它们主要对平均摄取敏感，而不是对摄取局部异质性敏感。6种纹理指数中的4种在2名核医学医师定义的均匀和异质病变之间存在显著差异（<0.05）。所有纹理指数值对体素大小（最均匀的球体模型变化高达85.8%）和边缘效应（变化高达29.1%）敏感。与之前的报告不同，我们的研究发现，在球体模型和真实肿瘤中，纹理指数的变化是直观的：最均匀的摄取分布表现出最高的均匀性和最低的熵。确定了反映不同类型摄取模式的两类纹理指数。证明了纹理指数值随体素大小和肿瘤边缘包含情况的变化，这需要一种标准化的计算方法。本研究为核医学医师在未来研究和临床实践中解释纹理指数提供了指导。
