xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
This work addresses survival prediction for lung cancer patients using explainable AI, but it is incremental as it builds on existing graph neural network methods for spatial modeling.
The paper tackled survival prediction in non-small cell lung cancer by developing an explainable cell graph approach, achieving validation on 416 cases of lung adenocarcinoma with insights into risk attributions at the cell level.
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.