Spatial Bayesian modeling of GLCM with application to malignant lesion characterization
Spatial Bayesian modeling of GLCM with application to malignant lesion characterization
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DOI：
10.1080/02664763.2018.1473348
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发表时间：
2019-01-25
影响因子：
1.5
通讯作者：
Hobbs, Brian P.
中科院分区：
文献类型：
--
作者：
Li, Xiao;Guindani, Michele;Hobbs, Brian P.
癌症放射组学的新兴领域致力于通过将医学图像转换为产生可量化的汇总统计的对象来表征肿瘤表型的固有模式和响应的替代标记，回归和机器学习算法可以应用于统计询问。最近的文献已经确定了基于纹理特征的临床病理学关联，这些纹理特征来自于灰度共生矩阵（GLCM），该矩阵有助于评估划定的感兴趣区域内的灰度空间依赖性。然而，GLCM衍生的特征往往会提供高度冗余的信息。此外，在报告选定的特征集时，调查人员往往无法调整多重性，并且通常无法传达他们发现的预测能力。本文提出了一个贝叶斯概率建模框架的GLCM作为一个多变量的对象，以及描述其应用范围内的癌症检测背景下，基于计算机断层扫描。该方法，它绕过了处理步骤，避免了还原和高度相关的功能集的评价，使用潜在的高斯马尔可夫随机场结构来表征空间的依赖性GLCM细胞之间，并通过预测概率，有利于分类。在我们的病例研究中，正确预测了81%的肾上腺病变的潜在病理学，所提出的方法优于目前的实践，其最高准确率仅为59%。模拟和理论，以进一步阐明这种比较，以及确定应用多元高斯空间过程的GLCM对象的效用。
The emerging field of cancer radiomics endeavors to characterize intrinsic patterns of tumor phenotypes and surrogate markers of response by transforming medical images into objects that yield quantifiable summary statistics to which regression and machine learning algorithms may be applied for statistical interrogation. Recent literature has identified clinicopathological association based on textural features deriving from gray-level co-occurrence matrices (GLCM) which facilitate evaluations of gray-level spatial dependence within a delineated region of interest. GLCM-derived features, however, tend to contribute highly redundant information. Moreover, when reporting selected feature sets, investigators often fail to adjust for multiplicities and commonly fail to convey the predictive power of their findings. This article presents a Bayesian probabilistic modeling framework for the GLCM as a multivariate object as well as describes its application within a cancer detection context based on computed tomography. The methodology, which circumvents processing steps and avoids evaluations of reductive and highly correlated feature sets, uses latent Gaussian Markov random field structure to characterize spatial dependencies among GLCM cells and facilitates classification via predictive probability. Correctly predicting the underlying pathology of 81% of the adrenal lesions in our case study, the proposed method outperformed current practices which achieved a maximum accuracy of only 59%. Simulations and theory are presented to further elucidate this comparison as well as ascertain the utility of applying multivariate Gaussian spatial processes to GLCM objects.