Summary of Xcg: Explainable Cell Graphs For Survival Prediction in Non-small Cell Lung Cancer, by Marvin Sextro et al.
xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
by Marvin Sextro, Gabriel Dernbach, Kai Standvoss, Simon Schallenberg, Frederick Klauschen, Klaus-Robert Müller, Maximilian Alber, Lukas Ruff
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed explainable cell graph (xCG) approach uses graph neural networks to predict oncology patient risk and provide insights into disease progression. The model is validated on a public cohort of imaging mass cytometry (IMC) data for 416 cases of lung adenocarcinoma, with survival predictions explained by computing risk attributions over cell graphs using an efficient grid-based layer-wise relevance propagation (LRP) method. The xCG method, along with the IMC data, is made publicly available to support further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models can predict oncology patient risk, helping doctors make decisions and develop personalized treatments. A new approach uses graph neural networks to understand how cells work together in tumors. This helps predict which patients are more likely to survive or not. The method was tested on a large dataset of lung cancer patients and provides insights into the disease progression. |
Keywords
* Artificial intelligence * Deep learning
