Roy Mousumi, Wang Fusheng, Teodoro George, Vega Jose Velazqeuz, Brat Daniel, Kong Jun
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:4644-4647. doi: 10.1109/EMBC.2018.8513157.
Cellular phenotypic features derived from histopathology images are the basis of pathologic diagnosis and are thought to be related to underlying molecular profiles. Due to overwhelming cell numbers and population heterogeneity, it remains challenging to quantitatively compute and compare features of cells with distinct molecular signatures. In this study, we propose a self-reliant and efficient analysis framework that supports quantitative analysis of cellular phenotypic difference across distinct molecular groups. To demonstrate efficacy, we quantitatively analyze astrocytomas that are molecularly characterized as either Isocitrate Dehydrogenase (IDH) mutant (MUT) or wildtype (WT) using imaging data from The Cancer Genome Atlas database. Representative cell instances that are phenotypically different between these two groups are retrieved after segmentation, feature computation, data pruning, dimensionality reduction, and unsupervised clustering. Our analysis is generic and can be applied to a wide set of cell-based biomedical research.
源自组织病理学图像的细胞表型特征是病理诊断的基础，并且被认为与潜在的分子图谱相关。由于细胞数量众多且群体异质性强，对具有不同分子特征的细胞特征进行定量计算和比较仍然具有挑战性。在本研究中，我们提出了一个自主且高效的分析框架，该框架支持对不同分子组之间的细胞表型差异进行定量分析。为了证明其有效性，我们使用来自癌症基因组图谱数据库的成像数据，对分子特征为异柠檬酸脱氢酶（IDH）突变型（MUT）或野生型（WT）的星形细胞瘤进行定量分析。在分割、特征计算、数据修剪、降维和无监督聚类之后，检索出这两组之间表型不同的代表性细胞实例。我们的分析具有通用性，可应用于广泛的基于细胞的生物医学研究。
