To develop a novel method for training any neural network architecture on any data modality to identify prognostically distinct patient groups by optimizing for survival heterogeneity across patient clusters.
Key Findings:
The method successfully identified distinct patient subgroups with significantly different survival outcomes in both cancer types, validated through rigorous external datasets.
Post-hoc explainability analyses revealed clinically meaningful features that aligned with established risk factors, enhancing the model's interpretability.
Findings in multiple myeloma were validated using the GMMG-MM5 study dataset, and NSCLC findings were validated with institutional data, reinforcing the method's robustness.
Interpretation:
The proposed model-agnostic approach enables the discovery of novel prognostic signatures across diverse data types, significantly enhancing treatment personalization and clinical decision-making.
Limitations:
The method's applicability may be limited to specific data modalities, such as laboratory parameters and imaging data, and requires further validation across broader datasets.
Potential challenges in interpreting complex model outputs in clinical settings may hinder practical implementation.
Conclusion:
This approach offers a promising framework for improving risk stratification in oncology through explainable AI techniques.
by Maximilian Ferle, Jonas Ader, Thomas Wiemers, Nora Grieb, Beatrice Berneck, Adrian Lindenmeyer, Hartmut Goldschmidt, Elias K. Mai, Uta Bertsch, Hans-Jonas Meyer, Thomas Neumuth, Markus Kreuz, Kristin Reiche, Maximilian Merz
