Bayesian finite mixture of regression analysis for cancer based on histopathological imaging–environment interactions
Bayesian finite mixture of regression analysis for cancer based on histopathological imaging–environment interactions
复制标题
基于组织病理学成像与环境相互作用的癌症贝叶斯有限混合回归分析
DOI：
10.1093/biostatistics/kxab038
复制
发表时间：
2021
期刊：
影响因子：
2.1
通讯作者：
Ma, Shuangge
中科院分区：
文献类型：
--
作者：
Im, Yunju;Huang, Yuan;Tan, Aixin;Ma, Shuangge
癌症是一种异质性疾病。有限混合回归(FMR)作为一种重要的异质性分析技术，在癌症研究中得到了广泛的应用，揭示了癌症结局/表型和协变量之间的重要差异。癌症FMR分析基于临床、人口统计学和组学变量。一种相对较新的替代数据来源来自组织病理学图像。长期以来，组织病理学图像一直被用于癌症的诊断和分期。最近，有研究表明，使用自动数字图像处理管道提取的高维组织病理图像特征对于模拟癌症结果/表型是有效的。组织病理成像-环境交互分析已经进一步发展，以扩大肿瘤建模和基于组织病理成像的分析的范围。由于癌症FMR分析的重要性和对更有效方法的强烈需求，在本文中，我们自然而然地进入下一步，基于包含低维临床/人口/环境变量、高维成像特征及其相互作用的模型来进行癌症FMR分析。作为对许多现有研究的补充，我们开发了一种贝叶斯方法，用于适应高维、筛选噪声、识别信号，并尊重“主效应、交互作用”变量选择层次。给出了一种有效的计算算法，仿真结果表明该方法具有良好的性能。对关于肺鳞癌的癌症基因组图谱数据的分析导致了不同于替代方法的有趣的发现。
Cancer is a heterogeneous disease. Finite mixture of regression (FMR)—as an important heterogeneity analysis technique when an outcome variable is present—has been extensively employed in cancer research, revealing important differences in the associations between a cancer outcome/phenotype and covariates. Cancer FMR analysis has been based on clinical, demographic, and omics variables. A relatively recent and alternative source of data comes from histopathological images. Histopathological images have been long used for cancer diagnosis and staging. Recently, it has been shown that high-dimensional histopathological image features, which are extracted using automated digital image processing pipelines, are effective for modeling cancer outcomes/phenotypes. Histopathological imaging–environment interaction analysis has been further developed to expand the scope of cancer modeling and histopathological imaging-based analysis. Motivated by the significance of cancer FMR analysis and a still strong demand for more effective methods, in this article, we take the natural next step and conduct cancer FMR analysis based on models that incorporate low-dimensional clinical/demographic/environmental variables, high-dimensional imaging features, as well as their interactions. Complementary to many of the existing studies, we develop a Bayesian approach for accommodating high dimensionality, screening out noises, identifying signals, and respecting the “main effects, interactions” variable selection hierarchy. An effective computational algorithm is developed, and simulation shows advantageous performance of the proposed approach. The analysis of The Cancer Genome Atlas data on lung squamous cell cancer leads to interesting findings different from the alternative approaches.