论文标题
组织病理学成像特征 - 基于分子测量的癌症预后建模
Histopathological imaging features- versus molecular measurements-based cancer prognosis modeling
论文作者
论文摘要
对于大多数癌症，即使不是全部的癌症，预后非常重要，并且已经进行了广泛的建模研究。在癌症的遗传性质中，在过去的二十年中，已经探索了多种类型的分子数据（例如基因表达和DNA突变）。最近，在活检中通常收集的组织病理学成像数据已显示为对预后进行建模的信息。在这项研究中，使用TCGA LUAD和LUSC数据作为展示，我们使用基因表达与组织病理学成像特征对肺癌的总生存率进行了检查和比较。采用高维正规化方法进行估计和选择。我们的分析表明，基因表达具有更好的预后性能。此外，发现大多数基因表达是弱相关的成像特征。可以预期，这项研究可以为利用癌症预后建模中的两种重要数据以及肺癌的总生存期提供一些见识。
For most if not all cancers, prognosis is of significant importance, and extensive modeling research has been conducted. With the genetic nature of cancer, in the past two decades, multiple types of molecular data (such as gene expressions and DNA mutations) have been explored. More recently, histopathological imaging data, which is routinely collected in biopsy, has been shown as informative for modeling prognosis. In this study, using the TCGA LUAD and LUSC data as a showcase, we examine and compare modeling lung cancer overall survival using gene expressions versus histopathological imaging features. High-dimensional regularization methods are adopted for estimation and selection. Our analysis shows that gene expressions have slightly better prognostic performance. In addition, most of the gene expressions are found to be weakly correlated imaging features. It is expected that this study can provide some insight into utilizing the two types of important data in cancer prognosis modeling and into lung cancer overall survival.
