Heterogeneity is a hallmark of cancer. For various cancer outcomes/phenotypes, supervised heterogeneity analysis has been conducted, leading to a deeper understanding of disease biology and customized clinical decisions. In the literature, such analysis has been oftentimes based on demographic, clinical, and omics measurements. Recent studies have shown that high-dimensional histopathological imaging features contain valuable information on cancer outcomes. However, comparatively, heterogeneity analysis based on imaging features has been very limited. In this article, we conduct supervised cancer heterogeneity analysis using histopathological imaging features. The penalized fusion technique, which has notable advantages—such as greater flexibility—over the finite mixture modeling and other techniques, is adopted. A sparse penalization is further imposed to accommodate high dimensionality and select relevant imaging features. To improve computational feasibility and generate more reliable estimation, we employ model averaging. Computational and statistical properties of the proposed approach are carefully investigated. Simulation demonstrates its favorable performance. The analysis of The Cancer Genome Atlas (TCGA) data may provide a new way of defining/examining breast cancer heterogeneity.

中文翻译：

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通过模型平均的惩罚融合进行基于组织病理学成像的癌症异质性分析

异质性是癌症的标志。对于各种癌症结果/表型，进行了受监督的异质性分析，从而加深了对疾病生物学的理解和定制的临床决策。在文献中，此类分析通常基于人口统计学、临床和组学测量。最近的研究表明，高维组织病理学成像特征包含有关癌症结果的宝贵信息。然而，相对而言，基于影像特征的异质性分析一直非常有限。在本文中，我们使用组织病理学成像特征进行受监督的癌症异质性分析。采用了惩罚融合技术，该技术与有限混合建模和其他技术相比具有显着优势（例如更大的灵活性）。进一步施加稀疏惩罚以适应高维并选择相关的成像特征。为了提高计算可行性并生成更可靠的估计，我们采用模型平均。仔细研究了所提出方法的计算和统计特性。仿真证明了其良好的性能。癌症基因组图谱 (TCGA) 数据的分析可能会提供一种定义/检查乳腺癌异质性的新方法。