Graph attention network enables multipurpose prediction of imaging mass cytometry in a hepatocellular carcinoma clinical trial

作者：Hang Shi, Wei Shao, Jinyuan Song, Gang Che, Yaxing Zhao, Yangyang Shi, Junlei Zhang, Peng Wan, Qi Zhu, Daoqiang Zhang, Jianpeng Sheng · 发表于：Intelligent Oncology · 年份：2025 · DOI：10.1016/j.intonc.2025.10.001 · 被引用次数：3 · 研究领域：Cell Image Analysis Techniques、Bioinformatics and Genomic Networks、Single-cell and spatial transcriptomics

Imaging mass cytometry (IMC) enables the high-resolution spatial profiling of tumor microenvironments, but its clinical utility for prospective prediction remains underdeveloped. In this study, we integrated IMC into a clinical trial of hepatocellular carcinoma (HCC) patients undergoing combination therapy with PD-1 blockade and transarterial chemoembolization. We analyzed 281 regions of interest from 43 patients using a custom 40-marker IMC panel and developed a novel superpixel-based graph attention network, IMCSGAT, to model spatial cell interactions within the tumor microenvironment. IMCSGAT enabled accurate multitask prediction of key clinical features, including Barcelona Clinic Liver Cancer stage, trabecular histologic subtype, and treatment response. Compared to state-of-the-art methods, IMCSGAT achieved superior performance across all classification tasks. Spatial interaction analysis revealed that resident macrophage–centered interactions, particularly those with NK and T cells, were enriched in responders and predictive of therapeutic outcome. These findings were validated in a murine HCC model, reinforcing the role of innate immune architecture in shaping the treatment response. This study establishes IMCSGAT as a powerful spatial learning framework for high-dimensional IMC data, with potential applications in clinical outcome prediction and personalized therapy design for HCC. Our results provide a blueprint for the broader use of spatial analytics in precision o...