Cancer diagnosis, prognosis, and therapeutic response prediction are heavily
influenced by the relationship between the histopathological structures and the
function of the tissue. Recent approaches acknowledging the structure-function
relationship, have linked the structural and spatial patterns of cell
organization in tissue via cell-graphs to tumor grades. Though cell
organization is imperative, it is insufficient to entirely represent the
histopathological structure. We propose a novel hierarchical
cell-to-tissue-graph (HACT) representation to improve the structural depiction
of the tissue. It consists of a low-level cell-graph, capturing cell morphology
and interactions, a high-level tissue-graph, capturing morphology and spatial
distribution of tissue parts, and cells-to-tissue hierarchies, encoding the
relative spatial distribution of the cells with respect to the tissue
distribution. Further, a hierarchical graph neural network (HACT-Net) is
proposed to efficiently map the HACT representations to histopathological
breast cancer subtypes. We assess the methodology on a large set of annotated
tissue regions of interest from H\&E stained breast carcinoma whole-slides.
Upon evaluation, the proposed method outperformed recent convolutional neural
network and graph neural network approaches for breast cancer multi-class
subtyping. The proposed entity-based topological analysis is more inline with
the pathological diagnostic procedure of the tissue. It provides more command
over the tissue modelling, therefore encourages the further inclusion of
pathological priors into task-specific tissue representation.

研究提出了一种新颖的分层细胞 - 组织图（HACT）表示，以及一种分层图神经网络（HACT-Net），用于将 HACT 表示映射到组织学乳腺癌亚型中。该方法通过实体为基础的拓扑分析更符合组织病理诊断程序，从而鼓励进一步将病理学先验纳入任务特定的组织表示。

HACT-Net: 一种基于层级细胞到组织图神经网络的组织病理图像分类方法

HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for  Histopathological Image Classification

Background Precise prediction of cancer types is vital for cancer diagnosis
and therapy. Important cancer marker genes can be inferred through predictive
model. Several studies have attempted to build machine learning models for this
task however none has taken into consideration the effects of tissue of origin
that can potentially bias the identification of cancer markers. Results In this
paper, we introduced several Convolutional Neural Network (CNN) models that
take unstructured gene expression inputs to classify tumor and non-tumor
samples into their designated cancer types or as normal. Based on different
designs of gene embeddings and convolution schemes, we implemented three CNN
models: 1D-CNN, 2D-Vanilla-CNN, and 2D-Hybrid-CNN. The models were trained and
tested on combined 10,340 samples of 33 cancer types and 731 matched normal
tissues of The Cancer Genome Atlas (TCGA). Our models achieved excellent
prediction accuracies (93.9-95.0%) among 34 classes (33 cancers and normal).
Furthermore, we interpreted one of the models, known as 1D-CNN model, with a
guided saliency technique and identified a total of 2,090 cancer markers (108
per class). The concordance of differential expression of these markers between
the cancer type they represent and others is confirmed. In breast cancer, for
instance, our model identified well-known markers, such as GATA3 and ESR1.
Finally, we extended the 1D-CNN model for prediction of breast cancer subtypes
and achieved an average accuracy of 88.42% among 5 subtypes. The codes can be
found at this https URL

本研究使用卷积神经网络 (CNN) 模型对 10,340 个来自 33 种不同癌症类型和 731 个正常组织样品进行分类，并在 1D-CNN 模型中确定了 2,090 个癌症标记物，其中包括 GATA3 和 ESR1 等已知标记，并对乳腺癌亚型进行了预测，取得了 88.42% 的平均准确率。