通过细胞间关系分析，提升肺癌预后预测准确率
From Cells to Survival: Hierarchical Analysis of Cell Inter-Relations in Multiplex Microscopy for Lung Cancer Prognosis
- 构建分层图模型，捕捉细胞局部与全局互动关系
- 在两个公开数据集上实现更优风险分层效果
- 适合关注肿瘤微环境与生存预测的研究者
肿瘤微环境（TME）已成为有前景的预后生物标志物来源。为充分挖掘其潜力，分析方法需捕捉不同细胞类型间的复杂互作。本文提出HiGINE——一种基于分层图的方法，从多重免疫荧光（mIF）图像中表征TME，预测肺癌患者生存期（短/长），并提升风险分层能力。模型同时编码细胞邻域中的局部与全局互相关系，并融合细胞类型与形态信息。通过多模态融合，整合癌症分期与mIF特征，进一步提升性能。我们在两个公开数据集上验证了HiGINE，结果表明其具有更优的风险分层能力、鲁棒性与泛化性。
原文摘要 · Abstract (English)
The tumor microenvironment (TME) has emerged as a promising source of prognostic biomarkers. To fully leverage its potential, analysis methods must capture complex interactions between different cell types. We propose HiGINE -- a hierarchical graph-based approach to predict patient survival (short vs. long) from TME characterization in multiplex immunofluorescence (mIF) images and enhance risk stratification in lung cancer. Our model encodes both local and global inter-relations in cell neighborhoods, incorporating information about cell types and morphology. Multimodal fusion, aggregating cancer stage with mIF-derived features, further boosts performance. We validate HiGINE on two public datasets, demonstrating improved risk stratification, robustness, and generalizability.
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