用细胞图模型解释肺癌患者生存预测，提升临床可信度。
xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
- 构建细胞图模型，通过图神经网络捕捉肿瘤微环境空间结构。
- 在416例肺腺癌数据上实现生存预测，结合细胞表型解释风险来源。
- 提出高效层析重要性传播方法，可直观揭示关键细胞群作用。
理解深度学习模型如何预测肿瘤患者风险，有助于揭示疾病进展机制、支持临床决策，并推动可信赖的数据驱动精准医疗。基于图神经网络在肿瘤微环境空间建模的最新进展，本文提出一种可解释的细胞图（xCG）方法用于生存预测。我们在公开的416例肺腺癌影像质谱流式细胞术（IMC）数据集上验证了该模型。通过计算细胞图上的风险归因，以已知细胞表型为依据解释生存预测结果，并提出一种高效的网格化逐层重要性传播（LRP）方法。消融实验表明，引入癌症分期信息和模型集成能显著提升风险估计质量。xCG方法及所用IMC数据均已公开，供后续研究使用。
原文摘要 · Abstract (English)
Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for trustworthy and data-driven precision medicine. Building on recent advances in the spatial modeling of the tumor microenvironment using graph neural networks, we present an explainable cell graph (xCG) approach for survival prediction. We validate our model on a public cohort of imaging mass cytometry (IMC) data for 416 cases of lung adenocarcinoma. We explain survival predictions in terms of known phenotypes on the cell level by computing risk attributions over cell graphs, for which we propose an efficient grid-based layer-wise relevance propagation (LRP) method. Our ablation studies highlight the importance of incorporating the cancer stage and model ensembling to improve the quality of risk estimates. Our xCG method, together with the IMC data, is made publicly available to support further research.
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