Department of Medical Biophysics, University of Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.
Department of Radiation Oncology, University of Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.
Phys Med. 2020 Apr;72:142-151. doi: 10.1016/j.ejmp.2020.03.018. Epub 2020 Apr 7.
Studies suggest there is utility in computed tomography (CT) radiomics for pancreatic disease; however, the precise biological interpretation of its features is unclear. In this manuscript, we present a novel approach towards this interpretation by investigating sub-micron tissue structure using digital pathology.
A classification-to attenuation (CAT) function was developed and applied to digital pathology images to create sub-micron linear attenuation maps. From these maps, grey level co-occurrence matrix (GLCM) features were extracted and compared to pathology features. To simulate the spatial frequency loss in a CT scanner, the attenuation maps were convolved with a point spread function (PSF) and subsequently down-sampled. GLCM features were extracted from these down-sampled maps to assess feature stability as a function of spatial frequency loss.
Two GLCM features were shown to be strongly and positively correlated (r = 0.8) with underlying characteristics of the tumor microenvironment, namely percent pimonidazole staining in the tumor. All features underwent marked change as a function of spatial frequency loss; progressively larger spatial frequency losses resulted in progressively larger inter-tumor standard deviations; two GLCM features exhibited stability up to a 100 µm pixel size.
This work represents a necessary step towards understanding the biological significance of radiomics. Our preliminary results suggest that cellular metrics of pimonidazole-detectable hypoxia correlate with sub-micron attenuation coefficient texture; however, the consistency of these textures in face of spatial frequency loss is detrimental for robust radiomics. Further study in larger data sets may elucidate additional, potentially more robust features of biologic and clinical relevance.
研究表明，计算机断层扫描（CT）放射组学在胰腺疾病中有一定的应用价值；然而，其特征的精确生物学解释尚不清楚。在本手稿中，我们通过使用数字病理学研究亚微米组织结构，提出了一种新的解释方法。
开发了一种分类到衰减（CAT）函数，并将其应用于数字病理学图像，以创建亚微米线性衰减图。从这些地图中，提取灰度共生矩阵（GLCM）特征并与病理学特征进行比较。为了模拟 CT 扫描仪中的空间频率损失，将衰减图与点扩散函数（PSF）卷积，并随后进行下采样。从这些下采样图中提取 GLCM 特征，以评估特征随空间频率损失的稳定性。
两个 GLCM 特征与肿瘤微环境的固有特征（即肿瘤内的 pimonidazole 染色百分比）呈强烈正相关（r=0.8）。所有特征都随空间频率损失而发生明显变化；空间频率损失越大，肿瘤间的标准偏差越大；两个 GLCM 特征在 100μm 像素尺寸下表现出稳定性。
这项工作代表了理解放射组学生物学意义的必要步骤。我们的初步结果表明，pimonidazole 检测到的缺氧的细胞度量与亚微米衰减系数纹理相关；然而，这些纹理在面对空间频率损失时的一致性对稳健的放射组学不利。在更大的数据集上进一步研究可能会阐明更多潜在的生物学和临床相关的稳健特征。
