[论文解读] Tissue Classification and Whole-Slide Images Analysis via Modeling of the Tumor Microenvironment and Biological Pathways

Junzhuo Liu, Xuemei Du+6 more|arXiv (Cornell University)|Jan 13, 2026

AI in cancer detection被引用 0

一句话总结

本文提出 BioMorphNet，这是一种多模态网络，融合组织形态学与空间基因表达，用于对全切片图像中的组织进行分类并分析差异基因表达，建模肿瘤微环境和生物通路。

ABSTRACT

Automatic integration of whole slide images (WSIs) and gene expression profiles has demonstrated substantial potential in precision clinical diagnosis and cancer progression studies. However, most existing studies focus on individual gene sequences and slide level classification tasks, with limited attention to spatial transcriptomics and patch level applications. To address this limitation, we propose a multimodal network, BioMorphNet, which automatically integrates tissue morphological features and spatial gene expression to support tissue classification and differential gene analysis. For considering morphological features, BioMorphNet constructs a graph to model the relationships between target patches and their neighbors, and adjusts the response strength based on morphological and molecular level similarity, to better characterize the tumor microenvironment. In terms of multimodal interactions, BioMorphNet derives clinical pathway features from spatial transcriptomic data based on a predefined pathway database, serving as a bridge between tissue morphology and gene expression. In addition, a novel learnable pathway module is designed to automatically simulate the biological pathway formation process, providing a complementary representation to existing clinical pathways. Compared with the latest morphology gene multimodal methods, BioMorphNet's average classification metrics improve by 2.67%, 5.48%, and 6.29% for prostate cancer, colorectal cancer, and breast cancer datasets, respectively. BioMorphNet not only classifies tissue categories within WSIs accurately to support tumor localization, but also analyzes differential gene expression between tissue categories based on prediction confidence, contributing to the discovery of potential tumor biomarkers.

研究动机与目标

• 将全切片图像（WSI）的形态特征与空间基因表达整合，以提升组织分类与生物标志物发现能力。
• 通过建立一个基于形态学与分子相似性的邻域加权的patch级图，建模肿瘤微环境。
• 通过来自空间转录组学的临床通路特征，连接组织形态与基因表达。
• 引入一个可学习的通路模块，模拟生物通路形成，作为互补表示。

提出的方法

• 在目标patch及其邻居之间构建图，以建模空间关系。
• 基于形态学与分子相似性调整响应强度。
• 使用预定义的通路数据库，从空间转录组数据中推导临床通路特征。
• 引入一个新的可学习的通路模块，模拟生物通路形成。
• 在前列腺癌、结直肠癌和乳腺癌WSI数据集上进行多模态融合的评估。
• 与最先进的形态-基因多模态方法进行对比。

实验结果

研究问题

• RQ1 通过结合空间基因表达与微环境上下文，基于图的多模态网络是否能够提升WSI的组织分类？
• RQ2 形态相似性与分子相似性如何影响组织分类中的patch级交互？
• RQ3 来自空间转录组学的通路-derived特征是否能提升组织标记与差异基因分析？
• RQ4 可学习的通路模块是否提供互补表示，提升分类性能？

主要发现

• BioMorphNet 在前列腺、结直肠和乳腺癌数据集的分类指标上平均提升分别为 2.67%、5.48% 与 6.29%。
• 该模型能够在WSI中实现组织类别定位，并支持不同组织类别之间的差异基因表达分析。
• 该方法通过通路特征桥接组织形态与基因表达，辅助潜在的生物标志物发现。