In computational pathology, extracting spatial features from gigapixel whole
slide images (WSIs) is a fundamental task, but due to their large size, WSIs
are typically segmented into smaller tiles. A critical aspect of this analysis
is aggregating information from these tiles to make predictions at the WSI
level. We introduce a model that combines a message-passing graph neural
network (GNN) with a state space model (Mamba) to capture both local and global
spatial relationships among the tiles in WSIs. The model's effectiveness was
demonstrated in predicting progression-free survival among patients with
early-stage lung adenocarcinomas (LUAD). We compared the model with other
state-of-the-art methods for tile-level information aggregation in WSIs,
including tile-level information summary statistics-based aggregation, multiple
instance learning (MIL)-based aggregation, GNN-based aggregation, and
GNN-transformer-based aggregation. Additional experiments showed the impact of
different types of node features and different tile sampling strategies on the
model performance. This work can be easily extended to any WSI-based analysis.
Code: this https URL

通过使用消息传递图神经网络（GNN）和状态空间模型（Mamba）结合来捕捉病理切片中切片之间的局部和全局空间关系，本研究旨在预测早期肺腺癌患者（LUAD）的无进展生存率，模型在预测上表现出很好的效果，并与其他最新研究方法进行了比较。