Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs that encode cell morphology and organization to denote the tissue information. These allow for utilizing machine learning to map tissue representations to tissue functionality to help quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a graph neural network is proposed to operate on the hierarchical entity-graph representation to map the tissue structure to tissue functionality. Specifically, for input histology images we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a graph neural network, to classify such HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of H&E stained breast tumor images, to evaluate our proposed methodology against pathologists and state-of-the-art approaches. Through comparative assessment and ablation studies, our method is demonstrated to yield superior classification results compared to alternative methods as well as pathologists.

翻译：组织样本的癌症诊断、预感和治疗反应预测高度取决于组织组织组织实体的细胞类型和地形分布。因此,对于计算机辅助癌症病人的护理来说,必须有足够的组织代表来表示肿瘤实体,因此,对于计算机辅助癌症病人的护理来说,必须有足够的组织组织组织实体的表示;为此,若干方法利用了将细胞形态和组织组织进行编码的细胞谱,以表示组织信息。这些方法可以利用机器学习来将组织表示映映映到组织功能,以帮助量化它们之间的关系。虽然细胞信息至关重要,但光全面描述复杂的组织结构结构是不完整的。我们在此将组织视为多种类型骨骼实体从精密到粗皮层层次的等级分布结构分布的等级构成。因此,我们提出了一个新的多层次组织组织组织组织组织组织组织组织组织组织展示结构的多层次等级结构表,用于将细胞细胞组织实体组织实体和组织实体的分布,从精细到粗皮到血統的內部、直系的內和內部的內部、內部的內部、內部的內部、內部的內部、內部、內部的內部的內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、直部、內部、內部、內部、內部、內部、內部、內部、直部、直部、直部、直部、直部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、內部、直部、直部、直部、直部、直部、直部、直部、直部、直部、直部、直部、直部、直部、直部、直部、內