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.

本研究解决了深度学习模型在癌症患者风险预测中的可解释性问题，提出了一种可解释的细胞图（xCG）方法用于生存预测。通过在公共成像质量细胞学数据集上验证，该方法实现了基于细胞图的生存预测解释，最重要的发现是引入癌症分期和模型集成显著提高了风险评估的质量。

可解释的细胞图用于非小细胞肺癌生存预测