Quantile Function on Scalar Regression Analysis for Distributional Data.
Quantile Function on Scalar Regression Analysis for Distributional Data.
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DOI：
10.1080/01621459.2019.1609969
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发表时间：
2020
影响因子：
3.7
通讯作者：
Morris JS
中科院分区：
文献类型：
--
作者：
Yang H;Baladandayuthapani V;Rao AUK;Morris JS
放射组学涉及肿瘤图像的研究，以确定解释癌症异质性的定量标志物。主要的方法是提取数百到数千个图像特征，包括由像素强度的边缘分布的摘要组成的直方图特征，这会导致多个测试问题，并且可能错过所选特征中不包含的见解。在本文中，我们提出的方法来模拟像素强度的整个边缘分布，通过分位数函数作为功能数据，回归一组人口统计学，临床和遗传预测，以调查其影响成像为基础的癌症异质性。我们称这种方法为分位数函数回归，在一组协变量的重复测量中回归特定于受试者的边缘分布，使我们能够评估哪些协变量与全局意义上的分布相关，以及识别表征这些差异的分布特征，包括均值，方差，偏度，重尾，以及各种上下分位数。为了解释分位数函数的平滑性，解释函数内相关性，并获得统计功效，我们引入了自定义的基函数，我们称之为quantlets，它们是稀疏的，正则化的，近无损的，并且是经验定义的，适应给定数据集的特征，并且包含高斯子空间，因此可以评估非高斯性。我们适合这个模型使用贝叶斯框架，该框架使用非线性收缩的量子系数正则化的功能回归系数，并提供充分的贝叶斯推理后，拟合马尔可夫链蒙特卡罗。我们通过模拟研究证明了基础空间建模的好处，并将该方法应用于多形性胶质母细胞瘤基于磁共振成像（MRI）的放射组学数据集，将基于成像的分位数函数与各种人口统计学，临床和遗传预测因子相关联，发现男性和女性之间以及有和没有DDIT 3突变的肿瘤之间肿瘤像素强度分布的特定差异。
Radiomics involves the study of tumor images to identify quantitative markers explaining cancer heterogeneity. The predominant approach is to extract hundreds to thousands of image features, including histogram features comprised of summaries of the marginal distribution of pixel intensities, which leads to multiple testing problems and can miss out on insights not contained in the selected features. In this paper, we present methods to model the entire marginal distribution of pixel intensities via the quantile function as functional data, regressed on a set of demographic, clinical, and genetic predictors to investigate their effects of imaging-based cancer heterogeneity. We call this approach quantile functional regression, regressing subject-specific marginal distributions across repeated measurements on a set of covariates, allowing us to assess which covariates are associated with the distribution in a global sense, as well as to identify distributional features characterizing these differences, including mean, variance, skewness, heavy-tailedness, and various upper and lower quantiles. To account for smoothness in the quantile functions, account for intrafunctional correlation, and gain statistical power, we introduce custom basis functions we call quantlets that are sparse, regularized, near-lossless, and empirically defined, adapting to the features of a given data set and containing a Gaussian subspace so non-Gaussianness can be assessed. We fit this model using a Bayesian framework that uses nonlinear shrinkage of quantlet coefficients to regularize the functional regression coefficients and provides fully Bayesian inference after fitting a Markov chain Monte Carlo. We demonstrate the benefit of the basis space modeling through simulation studies, and apply the method to Magnetic resonance imaging (MRI) based radiomic dataset from Glioblastoma Multiforme to relate imaging-based quantile functions to various demographic, clinical, and genetic predictors, finding specific differences in tumor pixel intensity distribution between males and females and between tumors with and without DDIT3 mutations.
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影响因子：
4.5
作者：
BICKEL, PJ;FREEDMAN, DA
通讯作者：
FREEDMAN, DA
影响因子：
1.2
作者：
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作者：
Koenker, R
通讯作者：
Koenker, R
影响因子：
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作者：
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通讯作者：
Dunson, David B.
影响因子：
1.9
作者：
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通讯作者：
Guo, WS